ivb_server_ratios.py

#
# auto generated TopDown/TMA 4.2-full-perf description for Intel Xeon E5 v2 (code named IvyBridge EP)
# Please see http://ark.intel.com for more details on these CPUs.
#
# References:
# http://bit.ly/tma-ispass14
# http://halobates.de/blog/p/262
# https://sites.google.com/site/analysismethods/yasin-pubs
# https://download.01.org/perfmon/
# https://github.com/andikleen/pmu-tools/wiki/toplev-manual
#

# Helpers

print_error = lambda msg: False
smt_enabled = False
ebs_mode = False
version = "4.2-full-perf"
base_frequency = -1.0
Memory = 0
Average_Frequency = 0.0


def handle_error(obj, msg):
    print_error(msg)
    obj.errcount += 1
    obj.val = 0
    obj.thresh = False

def handle_error_metric(obj, msg):
    print_error(msg)
    obj.errcount += 1
    obj.val = 0



# Constants

Mem_L2_Store_Cost = 9
Pipeline_Width = 4
Mem_L3_Weight = 7
Mem_STLB_Hit_Cost = 7
BAClear_Cost = 12
MS_Switches_Cost = 3
Avg_Assist_Cost = 100 * Pipeline_Width
OneMillion = 1000000
OneBillion = 1000000000
Energy_Unit = 15.6

# Aux. formulas


def Backend_Bound_Cycles(self, EV, level):
    return (STALLS_TOTAL(self, EV, level) + EV("UOPS_EXECUTED.CYCLES_GE_1_UOP_EXEC", level) - Few_Uops_Executed_Threshold(self, EV, level) - Frontend_RS_Empty_Cycles(self, EV, level) + EV("RESOURCE_STALLS.SB", level))

def Cycles_0_Ports_Utilized(self, EV, level):
    return (EV("UOPS_EXECUTED.CORE:i1:c1", level)) / 2 if smt_enabled else(STALLS_TOTAL(self, EV, level) - Frontend_RS_Empty_Cycles(self, EV, level))

def Cycles_1_Port_Utilized(self, EV, level):
    return (EV("UOPS_EXECUTED.CORE:c1", level) - EV("UOPS_EXECUTED.CORE:c2", level)) / 2 if smt_enabled else(EV("UOPS_EXECUTED.CYCLES_GE_1_UOP_EXEC", level) - EV("UOPS_EXECUTED.CYCLES_GE_2_UOPS_EXEC", level))

def Cycles_2_Ports_Utilized(self, EV, level):
    return (EV("UOPS_EXECUTED.CORE:c2", level) - EV("UOPS_EXECUTED.CORE:c3", level)) / 2 if smt_enabled else(EV("UOPS_EXECUTED.CYCLES_GE_2_UOPS_EXEC", level) - EV("UOPS_EXECUTED.CYCLES_GE_3_UOPS_EXEC", level))

def Cycles_3m_Ports_Utilized(self, EV, level):
    return (EV("UOPS_EXECUTED.CORE:c3", level) / 2) if smt_enabled else EV("UOPS_EXECUTED.CYCLES_GE_3_UOPS_EXEC", level)

def DurationTimeInSeconds(self, EV, level):
    return EV("interval-ms", 0) / 1000

def Execute_Cycles(self, EV, level):
    return (EV("UOPS_EXECUTED.CORE:c1", level) / 2) if smt_enabled else EV("UOPS_EXECUTED.CYCLES_GE_1_UOP_EXEC", level)

def Fetched_Uops(self, EV, level):
    return (EV("IDQ.DSB_UOPS", level) + EV("LSD.UOPS", level) + EV("IDQ.MITE_UOPS", level) + EV("IDQ.MS_UOPS", level))

def Few_Uops_Executed_Threshold(self, EV, level):
    EV("UOPS_EXECUTED.CYCLES_GE_3_UOPS_EXEC", level)
    EV("UOPS_EXECUTED.CYCLES_GE_2_UOPS_EXEC", level)
    return EV("UOPS_EXECUTED.CYCLES_GE_3_UOPS_EXEC", level) if (IPC(self, EV, level)> 1.8) else EV("UOPS_EXECUTED.CYCLES_GE_2_UOPS_EXEC", level)

# Floating Point computational (arithmetic) Operations Count
def FLOP_Count(self, EV, level):
    return (1 *(EV("FP_COMP_OPS_EXE.SSE_SCALAR_SINGLE", level) + EV("FP_COMP_OPS_EXE.SSE_SCALAR_DOUBLE", level)) + 2 * EV("FP_COMP_OPS_EXE.SSE_PACKED_DOUBLE", level) + 4 *(EV("FP_COMP_OPS_EXE.SSE_PACKED_SINGLE", level) + EV("SIMD_FP_256.PACKED_DOUBLE", level)) + 8 * EV("SIMD_FP_256.PACKED_SINGLE", level))

# Floating Point computational (arithmetic) Operations Count
def FP_Arith_Scalar(self, EV, level):
    return EV("FP_COMP_OPS_EXE.SSE_SCALAR_SINGLE", level) + EV("FP_COMP_OPS_EXE.SSE_SCALAR_DOUBLE", level)

# Floating Point computational (arithmetic) Operations Count
def FP_Arith_Vector(self, EV, level):
    return EV("FP_COMP_OPS_EXE.SSE_PACKED_DOUBLE", level) + EV("FP_COMP_OPS_EXE.SSE_PACKED_SINGLE", level) + EV("SIMD_FP_256.PACKED_SINGLE", level) + EV("SIMD_FP_256.PACKED_DOUBLE", level)

def Frontend_RS_Empty_Cycles(self, EV, level):
    EV("RS_EVENTS.EMPTY_CYCLES", level)
    return EV("RS_EVENTS.EMPTY_CYCLES", level) if (self.Fetch_Latency.compute(EV)> 0.1) else 0

def Frontend_Latency_Cycles(self, EV, level):
    return EV(lambda EV , level : min(EV("CPU_CLK_UNHALTED.THREAD", level) , EV("IDQ_UOPS_NOT_DELIVERED.CYCLES_0_UOPS_DELIV.CORE", level)) , level )

def HighIPC(self, EV, level):
    return IPC(self, EV, level) / Pipeline_Width

def ITLB_Miss_Cycles(self, EV, level):
    return (12 * EV("ITLB_MISSES.STLB_HIT", level) + EV("ITLB_MISSES.WALK_DURATION", level))

def LOAD_L1_MISS(self, EV, level):
    return EV("MEM_LOAD_UOPS_RETIRED.L2_HIT", level) + EV("MEM_LOAD_UOPS_RETIRED.LLC_HIT", level) + EV("MEM_LOAD_UOPS_LLC_HIT_RETIRED.XSNP_HIT", level) + EV("MEM_LOAD_UOPS_LLC_HIT_RETIRED.XSNP_HITM", level) + EV("MEM_LOAD_UOPS_LLC_HIT_RETIRED.XSNP_MISS", level)

def LOAD_L1_MISS_NET(self, EV, level):
    return LOAD_L1_MISS(self, EV, level) + EV("MEM_LOAD_UOPS_LLC_MISS_RETIRED.LOCAL_DRAM", level) + EV("MEM_LOAD_UOPS_LLC_MISS_RETIRED.REMOTE_DRAM", level) + EV("MEM_LOAD_UOPS_LLC_MISS_RETIRED.REMOTE_HITM", level) + EV("MEM_LOAD_UOPS_LLC_MISS_RETIRED.REMOTE_FWD", level)

def LOAD_L3_HIT(self, EV, level):
    return EV("MEM_LOAD_UOPS_RETIRED.LLC_HIT", level) * (1 + EV("MEM_LOAD_UOPS_RETIRED.HIT_LFB", level) / LOAD_L1_MISS_NET(self, EV, level))

def LOAD_LCL_MEM(self, EV, level):
    return EV("MEM_LOAD_UOPS_LLC_MISS_RETIRED.LOCAL_DRAM", level) * (1 + EV("MEM_LOAD_UOPS_RETIRED.HIT_LFB", level) / LOAD_L1_MISS_NET(self, EV, level))

def LOAD_RMT_FWD(self, EV, level):
    return EV("MEM_LOAD_UOPS_LLC_MISS_RETIRED.REMOTE_FWD", level) * (1 + EV("MEM_LOAD_UOPS_RETIRED.HIT_LFB", level) / LOAD_L1_MISS_NET(self, EV, level))

def LOAD_RMT_HITM(self, EV, level):
    return EV("MEM_LOAD_UOPS_LLC_MISS_RETIRED.REMOTE_HITM", level) * (1 + EV("MEM_LOAD_UOPS_RETIRED.HIT_LFB", level) / LOAD_L1_MISS_NET(self, EV, level))

def LOAD_RMT_MEM(self, EV, level):
    return EV("MEM_LOAD_UOPS_LLC_MISS_RETIRED.REMOTE_DRAM", level) * (1 + EV("MEM_LOAD_UOPS_RETIRED.HIT_LFB", level) / LOAD_L1_MISS_NET(self, EV, level))

def LOAD_XSNP_HIT(self, EV, level):
    return EV("MEM_LOAD_UOPS_LLC_HIT_RETIRED.XSNP_HIT", level) * (1 + EV("MEM_LOAD_UOPS_RETIRED.HIT_LFB", level) / LOAD_L1_MISS_NET(self, EV, level))

def LOAD_XSNP_HITM(self, EV, level):
    return EV("MEM_LOAD_UOPS_LLC_HIT_RETIRED.XSNP_HITM", level) * (1 + EV("MEM_LOAD_UOPS_RETIRED.HIT_LFB", level) / LOAD_L1_MISS_NET(self, EV, level))

def LOAD_XSNP_MISS(self, EV, level):
    return EV("MEM_LOAD_UOPS_LLC_HIT_RETIRED.XSNP_MISS", level) * (1 + EV("MEM_LOAD_UOPS_RETIRED.HIT_LFB", level) / LOAD_L1_MISS_NET(self, EV, level))

def Mem_L3_Hit_Fraction(self, EV, level):
    return EV("MEM_LOAD_UOPS_RETIRED.LLC_HIT", level) / (EV("MEM_LOAD_UOPS_RETIRED.LLC_HIT", level) + Mem_L3_Weight * EV("MEM_LOAD_UOPS_RETIRED.LLC_MISS", level))

def Mem_Lock_St_Fraction(self, EV, level):
    return EV("MEM_UOPS_RETIRED.LOCK_LOADS", level) / EV("MEM_UOPS_RETIRED.ALL_STORES", level)

def Memory_Bound_Fraction(self, EV, level):
    return (STALLS_MEM_ANY(self, EV, level) + EV("RESOURCE_STALLS.SB", level)) / Backend_Bound_Cycles(self, EV, level)

def Mispred_Clears_Fraction(self, EV, level):
    return EV("BR_MISP_RETIRED.ALL_BRANCHES", level) / (EV("BR_MISP_RETIRED.ALL_BRANCHES", level) + EV("MACHINE_CLEARS.COUNT", level))

def ORO_Demand_RFO_C1(self, EV, level):
    return EV(lambda EV , level : min(EV("CPU_CLK_UNHALTED.THREAD", level) , EV("OFFCORE_REQUESTS_OUTSTANDING.CYCLES_WITH_DEMAND_RFO", level)) , level )

def ORO_DRD_Any_Cycles(self, EV, level):
    return EV(lambda EV , level : min(EV("CPU_CLK_UNHALTED.THREAD", level) , EV("OFFCORE_REQUESTS_OUTSTANDING.CYCLES_WITH_DATA_RD", level)) , level )

def ORO_DRD_BW_Cycles(self, EV, level):
    return EV(lambda EV , level : min(EV("CPU_CLK_UNHALTED.THREAD", level) , EV("OFFCORE_REQUESTS_OUTSTANDING.ALL_DATA_RD:c6", level)) , level )

def Recovery_Cycles(self, EV, level):
    return (EV("INT_MISC.RECOVERY_CYCLES_ANY", level) / 2) if smt_enabled else EV("INT_MISC.RECOVERY_CYCLES", level)

def Retire_Fraction(self, EV, level):
    return Retired_Slots(self, EV, level) / EV("UOPS_ISSUED.ANY", level)

# Retired slots per Logical Processor
def Retired_Slots(self, EV, level):
    return EV("UOPS_RETIRED.RETIRE_SLOTS", level)

def SQ_Full_Cycles(self, EV, level):
    return (EV("OFFCORE_REQUESTS_BUFFER.SQ_FULL", level) / 2) if smt_enabled else EV("OFFCORE_REQUESTS_BUFFER.SQ_FULL", level)

def STALLS_MEM_ANY(self, EV, level):
    return EV(lambda EV , level : min(EV("CPU_CLK_UNHALTED.THREAD", level) , EV("CYCLE_ACTIVITY.STALLS_LDM_PENDING", level)) , level )

def STALLS_TOTAL(self, EV, level):
    return EV(lambda EV , level : min(EV("CPU_CLK_UNHALTED.THREAD", level) , EV("CYCLE_ACTIVITY.CYCLES_NO_EXECUTE", level)) , level )

def Store_L2_Hit_Cycles(self, EV, level):
    return EV("L2_RQSTS.RFO_HIT", level) * Mem_L2_Store_Cost *(1 - Mem_Lock_St_Fraction(self, EV, level))

def Mem_XSNP_HitM_Cost(self, EV, level):
    return 60

def Mem_XSNP_Hit_Cost(self, EV, level):
    return 43

def Mem_XSNP_None_Cost(self, EV, level):
    return 41

def Mem_Local_DRAM_Cost(self, EV, level):
    return 200

def Mem_Remote_DRAM_Cost(self, EV, level):
    return 310

def Mem_Remote_HitM_Cost(self, EV, level):
    return 200

def Mem_Remote_Fwd_Cost(self, EV, level):
    return 180

# Instructions Per Cycle (per Logical Processor)
def IPC(self, EV, level):
    return EV("INST_RETIRED.ANY", level) / CLKS(self, EV, level)

# Uops Per Instruction
def UPI(self, EV, level):
    return Retired_Slots(self, EV, level) / EV("INST_RETIRED.ANY", level)

# Instruction per taken branch
def IpTB(self, EV, level):
    return EV("INST_RETIRED.ANY", level) / EV("BR_INST_RETIRED.NEAR_TAKEN", level)

# Cycles Per Instruction (per Logical Processor)
def CPI(self, EV, level):
    return 1 / IPC(self, EV, level)

# Per-Logical Processor actual clocks when the Logical Processor is active.
def CLKS(self, EV, level):
    return EV("CPU_CLK_UNHALTED.THREAD", level)

# Total issue-pipeline slots (per-Physical Core till ICL; per-Logical Processor ICL onward)
def SLOTS(self, EV, level):
    return Pipeline_Width * CORE_CLKS(self, EV, level)

# Instructions Per Cycle (per physical core)
def CoreIPC(self, EV, level):
    return EV("INST_RETIRED.ANY", level) / CORE_CLKS(self, EV, level)

# Floating Point Operations Per Cycle
def FLOPc(self, EV, level):
    return FLOP_Count(self, EV, level) / CORE_CLKS(self, EV, level)

# Instruction-Level-Parallelism (average number of uops executed when there is at least 1 uop executed)
def ILP(self, EV, level):
    return EV("UOPS_EXECUTED.THREAD", level) / Execute_Cycles(self, EV, level)

# Number of Instructions per non-speculative Branch Misprediction (JEClear)
def IpMispredict(self, EV, level):
    return EV("INST_RETIRED.ANY", level) / EV("BR_MISP_RETIRED.ALL_BRANCHES", level)

# Core actual clocks when any Logical Processor is active on the Physical Core
def CORE_CLKS(self, EV, level):
    return ((EV("CPU_CLK_UNHALTED.THREAD", level) / 2) * (1 + EV("CPU_CLK_UNHALTED.ONE_THREAD_ACTIVE", level) / EV("CPU_CLK_UNHALTED.REF_XCLK", level))) if ebs_mode else(EV("CPU_CLK_UNHALTED.THREAD_ANY", level) / 2) if smt_enabled else CLKS(self, EV, level)

# Instructions per Load (lower number means higher occurrence rate)
def IpLoad(self, EV, level):
    return EV("INST_RETIRED.ANY", level) / EV("MEM_UOPS_RETIRED.ALL_LOADS", level)

# Instructions per Store (lower number means higher occurrence rate)
def IpStore(self, EV, level):
    return EV("INST_RETIRED.ANY", level) / EV("MEM_UOPS_RETIRED.ALL_STORES", level)

# Instructions per Branch (lower number means higher occurrence rate)
def IpBranch(self, EV, level):
    return EV("INST_RETIRED.ANY", level) / EV("BR_INST_RETIRED.ALL_BRANCHES", level)

# Instructions per (near) call (lower number means higher occurrence rate)
def IpCall(self, EV, level):
    return EV("INST_RETIRED.ANY", level) / EV("BR_INST_RETIRED.NEAR_CALL", level)

# Branch instructions per taken branch. 
def BpTkBranch(self, EV, level):
    return EV("BR_INST_RETIRED.ALL_BRANCHES", level) / EV("BR_INST_RETIRED.NEAR_TAKEN", level)

# Instructions per FP Arithmetic instruction (lower number means higher occurrence rate). Approximated prior to BDW.
def IpArith(self, EV, level):
    return 1 /(self.FP_Scalar.compute(EV) + self.FP_Vector.compute(EV))

# Total number of retired Instructions
def Instructions(self, EV, level):
    return EV("INST_RETIRED.ANY", level)

# Fraction of Uops delivered by the DSB (aka Decoded ICache; or Uop Cache)
def DSB_Coverage(self, EV, level):
    return EV("IDQ.DSB_UOPS", level) / Fetched_Uops(self, EV, level)

# Actual Average Latency for L1 data-cache miss demand loads (in core cycles)
def Load_Miss_Real_Latency(self, EV, level):
    return EV("L1D_PEND_MISS.PENDING", level) / (EV("MEM_LOAD_UOPS_RETIRED.L1_MISS", level) + EV("MEM_LOAD_UOPS_RETIRED.HIT_LFB", level))

# Memory-Level-Parallelism (average number of L1 miss demand load when there is at least one such miss. Per-Logical Processor)
def MLP(self, EV, level):
    return EV("L1D_PEND_MISS.PENDING", level) / EV("L1D_PEND_MISS.PENDING_CYCLES", level)

# Utilization of the core's Page Walker(s) serving STLB misses triggered by instruction/Load/Store accesses
def Page_Walks_Utilization(self, EV, level):
    return (EV("ITLB_MISSES.WALK_DURATION", level) + EV("DTLB_LOAD_MISSES.WALK_DURATION", level) + EV("DTLB_STORE_MISSES.WALK_DURATION", level)) / CORE_CLKS(self, EV, level)

# Average data fill bandwidth to the L1 data cache [GB / sec]
def L1D_Cache_Fill_BW(self, EV, level):
    return 64 * EV("L1D.REPLACEMENT", level) / OneBillion / Time(self, EV, level)

# Average data fill bandwidth to the L2 cache [GB / sec]
def L2_Cache_Fill_BW(self, EV, level):
    return 64 * EV("L2_LINES_IN.ALL", level) / OneBillion / Time(self, EV, level)

# Average per-core data fill bandwidth to the L3 cache [GB / sec]
def L3_Cache_Fill_BW(self, EV, level):
    return 64 * EV("LONGEST_LAT_CACHE.MISS", level) / OneBillion / Time(self, EV, level)

# L1 cache true misses per kilo instruction for retired demand loads
def L1MPKI(self, EV, level):
    return 1000 * EV("MEM_LOAD_UOPS_RETIRED.L1_MISS", level) / EV("INST_RETIRED.ANY", level)

# L2 cache true misses per kilo instruction for retired demand loads
def L2MPKI(self, EV, level):
    return 1000 * EV("MEM_LOAD_UOPS_RETIRED.L2_MISS", level) / EV("INST_RETIRED.ANY", level)

# L3 cache true misses per kilo instruction for retired demand loads
def L3MPKI(self, EV, level):
    return 1000 * EV("MEM_LOAD_UOPS_RETIRED.LLC_MISS", level) / EV("INST_RETIRED.ANY", level)

# Average CPU Utilization
def CPU_Utilization(self, EV, level):
    return EV("CPU_CLK_UNHALTED.REF_TSC", level) / EV("msr/tsc/", 0)

# Measured Average Frequency for unhalted processors [GHz]
def Average_Frequency(self, EV, level):
    return Turbo_Utilization(self, EV, level) * EV("msr/tsc/", 0) / OneBillion / Time(self, EV, level)

# Giga Floating Point Operations Per Second
def GFLOPs(self, EV, level):
    return (FLOP_Count(self, EV, level) / OneBillion) / Time(self, EV, level)

# Average Frequency Utilization relative nominal frequency
def Turbo_Utilization(self, EV, level):
    return CLKS(self, EV, level) / EV("CPU_CLK_UNHALTED.REF_TSC", level)

# Fraction of cycles where both hardware Logical Processors were active
def SMT_2T_Utilization(self, EV, level):
    return 1 - EV("CPU_CLK_UNHALTED.ONE_THREAD_ACTIVE", level) / (EV("CPU_CLK_UNHALTED.REF_XCLK_ANY", level) / 2) if smt_enabled else 0

# Fraction of cycles spent in the Operating System (OS) Kernel mode
def Kernel_Utilization(self, EV, level):
    return EV("CPU_CLK_UNHALTED.THREAD_P:SUP", level) / EV("CPU_CLK_UNHALTED.THREAD", level)

# Average external Memory Bandwidth Use for reads and writes [GB / sec]
def DRAM_BW_Use(self, EV, level):
    return (64 *(EV("UNC_M_CAS_COUNT.RD", level) + EV("UNC_M_CAS_COUNT.WR", level)) / OneBillion) / Time(self, EV, level)

# Average latency of data read request to external memory (in nanoseconds). Accounts for demand loads and L1/L2 prefetches
def MEM_Read_Latency(self, EV, level):
    return OneBillion *(EV("UNC_C_TOR_OCCUPANCY.MISS_OPCODE:Match=0x182", level) / EV("UNC_C_TOR_INSERTS.MISS_OPCODE:Match=0x182", level)) / (Socket_CLKS(self, EV, level) / Time(self, EV, level))

# Average number of parallel data read requests to external memory. Accounts for demand loads and L1/L2 prefetches
def MEM_Parallel_Reads(self, EV, level):
    return EV("UNC_C_TOR_OCCUPANCY.MISS_OPCODE:Match=0x182", level) / EV("UNC_C_TOR_OCCUPANCY.MISS_OPCODE:Match=0x182:c1", level)

# Run duration time in seconds
def Time(self, EV, level):
    return EV("interval-s", 0)

# Socket actual clocks when any core is active on that socket
def Socket_CLKS(self, EV, level):
    return EV("UNC_C_CLOCKTICKS:one_unit", level)

# Instructions per Far Branch ( Far Branches apply upon transition from application to operating system, handling interrupts, exceptions) [lower number means higher occurrence rate]
def IpFarBranch(self, EV, level):
    return EV("INST_RETIRED.ANY", level) / EV("BR_INST_RETIRED.FAR_BRANCH:USER", level)

# Event groups


class Frontend_Bound:
    name = "Frontend_Bound"
    domain = "Slots"
    area = "FE"
    level = 1
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['TmaL1']
    def compute(self, EV):
        try:
            self.val = EV("IDQ_UOPS_NOT_DELIVERED.CORE", 1) / SLOTS(self, EV, 1)
            self.thresh = (self.val > 0.2)
        except ZeroDivisionError:
            handle_error(self, "Frontend_Bound zero division")
        return self.val
    desc = """
This category represents fraction of slots where the
processor's Frontend undersupplies its Backend. Frontend
denotes the first part of the processor core responsible to
fetch operations that are executed later on by the Backend
part. Within the Frontend; a branch predictor predicts the
next address to fetch; cache-lines are fetched from the
memory subsystem; parsed into instructions; and lastly
decoded into micro-operations (uops). Ideally the Frontend
can issue Machine_Width uops every cycle to the Backend.
Frontend Bound denotes unutilized issue-slots when there is
no Backend stall; i.e. bubbles where Frontend delivered no
uops while Backend could have accepted them. For example;
stalls due to instruction-cache misses would be categorized
under Frontend Bound."""


class Fetch_Latency:
    name = "Fetch_Latency"
    domain = "Slots"
    area = "FE"
    level = 2
    htoff = False
    sample = ['RS_EVENTS.EMPTY_END']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['Frontend', 'TmaL2']
    def compute(self, EV):
        try:
            self.val = Pipeline_Width * Frontend_Latency_Cycles(self, EV, 2) / SLOTS(self, EV, 2)
            self.thresh = (self.val > 0.15) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Fetch_Latency zero division")
        return self.val
    desc = """
This metric represents fraction of slots the CPU was stalled
due to Frontend latency issues.  For example; instruction-
cache misses; iTLB misses or fetch stalls after a branch
misprediction are categorized under Frontend Latency. In
such cases; the Frontend eventually delivers no uops for
some period."""


class ICache_Misses:
    name = "ICache_Misses"
    domain = "Clocks"
    area = "FE"
    level = 3
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['FetchLat', 'IcMiss']
    def compute(self, EV):
        try:
            self.val = EV("ICACHE.IFETCH_STALL", 3) / CLKS(self, EV, 3) - self.ITLB_Misses.compute(EV)
            self.thresh = (self.val > 0.05) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "ICache_Misses zero division")
        return self.val
    desc = """
This metric represents fraction of cycles the CPU was
stalled due to instruction cache misses."""


class ITLB_Misses:
    name = "ITLB_Misses"
    domain = "Clocks"
    area = "FE"
    level = 3
    htoff = False
    sample = ['ITLB_MISSES.WALK_COMPLETED']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['FetchLat', 'MemoryTLB']
    def compute(self, EV):
        try:
            self.val = ITLB_Miss_Cycles(self, EV, 3) / CLKS(self, EV, 3)
            self.thresh = (self.val > 0.05) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "ITLB_Misses zero division")
        return self.val
    desc = """
This metric represents fraction of cycles the CPU was
stalled due to Instruction TLB (ITLB) misses."""


class Branch_Resteers:
    name = "Branch_Resteers"
    domain = "Clocks_Estimated"
    area = "FE"
    level = 3
    htoff = False
    sample = ['BR_MISP_RETIRED.ALL_BRANCHES:pp']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['BadSpec', 'FetchLat']
    def compute(self, EV):
        try:
            self.val = BAClear_Cost *(EV("BR_MISP_RETIRED.ALL_BRANCHES", 3) + EV("MACHINE_CLEARS.COUNT", 3) + EV("BACLEARS.ANY", 3)) / CLKS(self, EV, 3)
            self.thresh = (self.val > 0.05) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Branch_Resteers zero division")
        return self.val
    desc = """
This metric represents fraction of cycles the CPU was
stalled due to Branch Resteers. Branch Resteers estimates
the Frontend delay in fetching operations from corrected
path; following all sorts of miss-predicted branches. For
example; branchy code with lots of miss-predictions might
get categorized under Branch Resteers. Note the value of
this node may overlap with its siblings."""


class DSB_Switches:
    name = "DSB_Switches"
    domain = "Clocks"
    area = "FE"
    level = 3
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['DSB', 'FetchLat']
    def compute(self, EV):
        try:
            self.val = EV("DSB2MITE_SWITCHES.PENALTY_CYCLES", 3) / CLKS(self, EV, 3)
            self.thresh = (self.val > 0.05) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "DSB_Switches zero division")
        return self.val
    desc = """
This metric represents fraction of cycles the CPU was
stalled due to switches from DSB to MITE pipelines. The DSB
(decoded i-cache; introduced with the Sandy Bridge
microarchitecture) pipeline has shorter latency and
delivered higher bandwidth than the MITE (legacy instruction
decode pipeline). Switching between the two pipelines can
cause penalties. This metric estimates when such penalty can
be exposed. Optimizing for better DSB hit rate may be
considered."""


class LCP:
    name = "LCP"
    domain = "Clocks"
    area = "FE"
    level = 3
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['FetchLat']
    def compute(self, EV):
        try:
            self.val = EV("ILD_STALL.LCP", 3) / CLKS(self, EV, 3)
            self.thresh = (self.val > 0.05) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "LCP zero division")
        return self.val
    desc = """
This metric represents fraction of cycles CPU was stalled
due to Length Changing Prefixes (LCPs). Using proper
compiler flags or Intel Compiler by default will certainly
avoid this."""


class MS_Switches:
    name = "MS_Switches"
    domain = "Clocks"
    area = "FE"
    level = 3
    htoff = False
    sample = ['IDQ.MS_SWITCHES']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['FetchLat', 'MicroSeq']
    def compute(self, EV):
        try:
            self.val = MS_Switches_Cost * EV("IDQ.MS_SWITCHES", 3) / CLKS(self, EV, 3)
            self.thresh = (self.val > 0.05) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "MS_Switches zero division")
        return self.val
    desc = """
This metric estimates the fraction of cycles when the CPU
was stalled due to switches of uop delivery to the Microcode
Sequencer (MS). Commonly used instructions are optimized for
delivery by the DSB (decoded i-cache) or MITE (legacy
instruction decode) pipelines. Certain operations cannot be
handled natively by the execution pipeline; and must be
performed by microcode (small programs injected into the
execution stream). Switching to the MS too often can
negatively impact performance. The MS is designated to
deliver long uop flows required by CISC instructions like
CPUID; or uncommon conditions like Floating Point Assists
when dealing with Denormals."""


class Fetch_Bandwidth:
    name = "Fetch_Bandwidth"
    domain = "Slots"
    area = "FE"
    level = 2
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['FetchBW', 'Frontend', 'TmaL2']
    def compute(self, EV):
        try:
            self.val = self.Frontend_Bound.compute(EV) - self.Fetch_Latency.compute(EV)
            self.thresh = (self.val > 0.1) & self.parent.thresh & (HighIPC(self, EV, 2) > 0)
        except ZeroDivisionError:
            handle_error(self, "Fetch_Bandwidth zero division")
        return self.val
    desc = """
This metric represents fraction of slots the CPU was stalled
due to Frontend bandwidth issues.  For example;
inefficiencies at the instruction decoders; or code
restrictions for caching in the DSB (decoded uops cache) are
categorized under Frontend Bandwidth. In such cases; the
Frontend typically delivers non-optimal amount of uops to
the Backend (less than four)."""


class MITE:
    name = "MITE"
    domain = "Slots_Estimated"
    area = "FE"
    level = 3
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['FetchBW']
    def compute(self, EV):
        try:
            self.val = (EV("IDQ.ALL_MITE_CYCLES_ANY_UOPS", 3) - EV("IDQ.ALL_MITE_CYCLES_4_UOPS", 3)) / CORE_CLKS(self, EV, 3) / 2
            self.thresh = (self.val > 0.15) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "MITE zero division")
        return self.val
    desc = """
This metric represents Core fraction of cycles in which CPU
was likely limited due to the MITE pipeline (Legacy Decode
Pipeline). This pipeline is used for code that was not pre-
cached in the DSB or LSD. For example; inefficiencies in the
instruction decoders are categorized here."""


class DSB:
    name = "DSB"
    domain = "Slots_Estimated"
    area = "FE"
    level = 3
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['DSB', 'FetchBW']
    def compute(self, EV):
        try:
            self.val = (EV("IDQ.ALL_DSB_CYCLES_ANY_UOPS", 3) - EV("IDQ.ALL_DSB_CYCLES_4_UOPS", 3)) / CORE_CLKS(self, EV, 3) / 2
            self.thresh = (self.val > 0.15) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "DSB zero division")
        return self.val
    desc = """
This metric represents Core fraction of cycles in which CPU
was likely limited due to DSB (decoded uop cache) fetch
pipeline.  For example; inefficient utilization of the DSB
cache structure or bank conflict when reading from it; are
categorized here."""


class Bad_Speculation:
    name = "Bad_Speculation"
    domain = "Slots"
    area = "BAD"
    level = 1
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['BadSpec', 'TmaL1']
    def compute(self, EV):
        try:
            self.val = (EV("UOPS_ISSUED.ANY", 1) - Retired_Slots(self, EV, 1) + Pipeline_Width * Recovery_Cycles(self, EV, 1)) / SLOTS(self, EV, 1)
            self.thresh = (self.val > 0.15)
        except ZeroDivisionError:
            handle_error(self, "Bad_Speculation zero division")
        return self.val
    desc = """
This category represents fraction of slots wasted due to
incorrect speculations. This include slots used to issue
uops that do not eventually get retired and slots for which
the issue-pipeline was blocked due to recovery from earlier
incorrect speculation. For example; wasted work due to miss-
predicted branches are categorized under Bad Speculation
category. Incorrect data speculation followed by Memory
Ordering Nukes is another example."""


class Branch_Mispredicts:
    name = "Branch_Mispredicts"
    domain = "Slots"
    area = "BAD"
    level = 2
    htoff = False
    sample = ['BR_MISP_RETIRED.ALL_BRANCHES:pp']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['BadSpec', 'BrMispredicts', 'TmaL2']
    def compute(self, EV):
        try:
            self.val = Mispred_Clears_Fraction(self, EV, 2) * self.Bad_Speculation.compute(EV)
            self.thresh = (self.val > 0.05) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Branch_Mispredicts zero division")
        return self.val
    desc = """
This metric represents fraction of slots the CPU has wasted
due to Branch Misprediction.  These slots are either wasted
by uops fetched from an incorrectly speculated program path;
or stalls when the out-of-order part of the machine needs to
recover its state from a speculative path."""


class Machine_Clears:
    name = "Machine_Clears"
    domain = "Slots"
    area = "BAD"
    level = 2
    htoff = False
    sample = ['MACHINE_CLEARS.COUNT']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['BadSpec', 'MachineClears', 'TmaL2']
    def compute(self, EV):
        try:
            self.val = self.Bad_Speculation.compute(EV) - self.Branch_Mispredicts.compute(EV)
            self.thresh = (self.val > 0.05) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Machine_Clears zero division")
        return self.val
    desc = """
This metric represents fraction of slots the CPU has wasted
due to Machine Clears.  These slots are either wasted by
uops fetched prior to the clear; or stalls the out-of-order
portion of the machine needs to recover its state after the
clear. For example; this can happen due to memory ordering
Nukes (e.g. Memory Disambiguation) or Self-Modifying-Code
(SMC) nukes."""


class Backend_Bound:
    name = "Backend_Bound"
    domain = "Slots"
    area = "BE"
    level = 1
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['TmaL1']
    def compute(self, EV):
        try:
            self.val = 1 -(self.Frontend_Bound.compute(EV) + self.Bad_Speculation.compute(EV) + self.Retiring.compute(EV))
            self.thresh = (self.val > 0.2)
        except ZeroDivisionError:
            handle_error(self, "Backend_Bound zero division")
        return self.val
    desc = """
This category represents fraction of slots where no uops are
being delivered due to a lack of required resources for
accepting new uops in the Backend. Backend is the portion of
the processor core where the out-of-order scheduler
dispatches ready uops into their respective execution units;
and once completed these uops get retired according to
program order. For example; stalls due to data-cache misses
or stalls due to the divider unit being overloaded are both
categorized under Backend Bound. Backend Bound is further
divided into two main categories: Memory Bound and Core
Bound."""


class Memory_Bound:
    name = "Memory_Bound"
    domain = "Slots"
    area = "BE/Mem"
    level = 2
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['Backend', 'TmaL2']
    def compute(self, EV):
        try:
            self.val = Memory_Bound_Fraction(self, EV, 2) * self.Backend_Bound.compute(EV)
            self.thresh = (self.val > 0.2) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Memory_Bound zero division")
        return self.val
    desc = """
This metric represents fraction of slots the Memory
subsystem within the Backend was a bottleneck.  Memory Bound
estimates fraction of slots where pipeline is likely stalled
due to demand load or store instructions. This accounts
mainly for (1) non-completed in-flight memory demand loads
which coincides with execution units starvation; in addition
to (2) cases where stores could impose backpressure on the
pipeline when many of them get buffered at the same time
(less common out of the two)."""


class L1_Bound:
    name = "L1_Bound"
    domain = "Stalls"
    area = "BE/Mem"
    level = 3
    htoff = False
    sample = ['MEM_LOAD_UOPS_RETIRED.L1_HIT:pp', 'MEM_LOAD_UOPS_RETIRED.HIT_LFB:pp']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['CacheMisses', 'MemoryBound', 'TmaL3mem']
    def compute(self, EV):
        try:
            self.val = max((STALLS_MEM_ANY(self, EV, 3) - EV("CYCLE_ACTIVITY.STALLS_L1D_PENDING", 3)) / CLKS(self, EV, 3) , 0 )
            self.thresh = (self.val > 0.1) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "L1_Bound zero division")
        return self.val
    desc = """
This metric estimates how often the CPU was stalled without
loads missing the L1 data cache.  The L1 data cache
typically has the shortest latency.  However; in certain
cases like loads blocked on older stores; a load might
suffer due to high latency even though it is being satisfied
by the L1. Another example is loads who miss in the TLB.
These cases are characterized by execution unit stalls;
while some non-completed demand load lives in the machine
without having that demand load missing the L1 cache."""


class DTLB_Load:
    name = "DTLB_Load"
    domain = "Clocks_Estimated"
    area = "BE/Mem"
    level = 4
    htoff = False
    sample = ['MEM_UOPS_RETIRED.STLB_MISS_LOADS:pp']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['MemoryTLB']
    def compute(self, EV):
        try:
            self.val = (Mem_STLB_Hit_Cost * EV("DTLB_LOAD_MISSES.STLB_HIT", 4) + EV("DTLB_LOAD_MISSES.WALK_DURATION", 4)) / CLKS(self, EV, 4)
            self.thresh = (self.val > 0.1) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "DTLB_Load zero division")
        return self.val
    desc = """
This metric roughly estimates the fraction of cycles where
the Data TLB (DTLB) was missed by load accesses. TLBs
(Translation Look-aside Buffers) are processor caches for
recently used entries out of the Page Tables that are used
to map virtual- to physical-addresses by the operating
system. This metric approximates the potential delay of
demand loads missing the first-level data TLB (assuming
worst case scenario with back to back misses to different
pages). This includes hitting in the second-level TLB (STLB)
as well as performing a hardware page walk on an STLB miss."""


class Store_Fwd_Blk:
    name = "Store_Fwd_Blk"
    domain = "Clocks_Estimated"
    area = "BE/Mem"
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = []
    def compute(self, EV):
        try:
            self.val = 13 * EV("LD_BLOCKS.STORE_FORWARD", 4) / CLKS(self, EV, 4)
            self.thresh = (self.val > 0.1) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Store_Fwd_Blk zero division")
        return self.val
    desc = """
This metric roughly estimates fraction of cycles when the
memory subsystem had loads blocked since they could not
forward data from earlier (in program order) overlapping
stores. To streamline memory operations in the pipeline; a
load can avoid waiting for memory if a prior in-flight store
is writing the data that the load wants to read (store
forwarding process). However; in some cases the load may be
blocked for a significant time pending the store forward.
For example; when the prior store is writing a smaller
region than the load is reading."""


class Lock_Latency:
    name = "Lock_Latency"
    domain = "Clocks"
    area = "BE/Mem"
    level = 4
    htoff = False
    sample = ['MEM_UOPS_RETIRED.LOCK_LOADS:pp']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['Offcore']
    def compute(self, EV):
        try:
            self.val = Mem_Lock_St_Fraction(self, EV, 4) * ORO_Demand_RFO_C1(self, EV, 4) / CLKS(self, EV, 4)
            self.thresh = (self.val > 0.2) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Lock_Latency zero division")
        return self.val
    desc = """
This metric represents fraction of cycles the CPU spent
handling cache misses due to lock operations. Due to the
microarchitecture handling of locks; they are classified as
L1_Bound regardless of what memory source satisfied them."""


class Split_Loads:
    name = "Split_Loads"
    domain = "Clocks_Calculated"
    area = "BE/Mem"
    level = 4
    htoff = False
    sample = ['MEM_UOPS_RETIRED.SPLIT_LOADS:pp']
    errcount = 0
    sibling = None
    server = False
    metricgroup = []
    def compute(self, EV):
        try:
            self.val = 13 * EV("LD_BLOCKS.NO_SR", 4) / CLKS(self, EV, 4)
            self.thresh = (self.val > 0.2) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Split_Loads zero division")
        return self.val
    desc = """
This metric estimates fraction of cycles handling memory
load split accesses - load that cross 64-byte cache line
boundary."""


class G4K_Aliasing:
    name = "4K_Aliasing"
    domain = "Clocks_Estimated"
    area = "BE/Mem"
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = []
    def compute(self, EV):
        try:
            self.val = EV("LD_BLOCKS_PARTIAL.ADDRESS_ALIAS", 4) / CLKS(self, EV, 4)
            self.thresh = (self.val > 0.2) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "G4K_Aliasing zero division")
        return self.val
    desc = """
This metric estimates how often memory load accesses were
aliased by preceding stores (in program order) with a 4K
address offset. False match is possible; which incur a few
cycles load re-issue. However; the short re-issue duration
is often hidden by the out-of-order core and HW
optimizations; hence a user may safely ignore a high value
of this metric unless it manages to propagate up into parent
nodes of the hierarchy (e.g. to L1_Bound)."""


class FB_Full:
    name = "FB_Full"
    domain = "Clocks_Calculated"
    area = "BE/Mem"
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['MemoryBW']
    def compute(self, EV):
        try:
            self.val = Load_Miss_Real_Latency(self, EV, 4) * EV("L1D_PEND_MISS.FB_FULL:c1", 4) / CLKS(self, EV, 4)
            self.thresh = (self.val > 0.3)
        except ZeroDivisionError:
            handle_error(self, "FB_Full zero division")
        return self.val
    desc = """
This metric does a *rough estimation* of how often L1D Fill
Buffer unavailability limited additional L1D miss memory
access requests to proceed. The higher the metric value; the
deeper the memory hierarchy level the misses are satisfied
from (metric values >1 are valid). Often it hints on
approaching bandwidth limits (to L2 cache; L3 cache or
external memory)."""


class L2_Bound:
    name = "L2_Bound"
    domain = "Stalls"
    area = "BE/Mem"
    level = 3
    htoff = False
    sample = ['MEM_LOAD_UOPS_RETIRED.L2_HIT:pp']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['CacheMisses', 'MemoryBound', 'TmaL3mem']
    def compute(self, EV):
        try:
            self.val = (EV("CYCLE_ACTIVITY.STALLS_L1D_PENDING", 3) - EV("CYCLE_ACTIVITY.STALLS_L2_PENDING", 3)) / CLKS(self, EV, 3)
            self.thresh = (self.val > 0.05) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "L2_Bound zero division")
        return self.val
    desc = """
This metric estimates how often the CPU was stalled due to
L2 cache accesses by loads.  Avoiding cache misses (i.e. L1
misses/L2 hits) can improve the latency and increase
performance."""


class L3_Bound:
    name = "L3_Bound"
    domain = "Stalls"
    area = "BE/Mem"
    level = 3
    htoff = False
    sample = ['MEM_LOAD_UOPS_RETIRED.LLC_HIT:pp']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['CacheMisses', 'MemoryBound', 'TmaL3mem']
    def compute(self, EV):
        try:
            self.val = Mem_L3_Hit_Fraction(self, EV, 3) * EV("CYCLE_ACTIVITY.STALLS_L2_PENDING", 3) / CLKS(self, EV, 3)
            self.thresh = (self.val > 0.05) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "L3_Bound zero division")
        return self.val
    desc = """
This metric estimates how often the CPU was stalled due to
loads accesses to L3 cache or contended with a sibling Core.
Avoiding cache misses (i.e. L2 misses/L3 hits) can improve
the latency and increase performance."""


class Contested_Accesses:
    name = "Contested_Accesses"
    domain = "Clocks_Estimated"
    area = "BE/Mem"
    level = 4
    htoff = False
    sample = ['MEM_LOAD_UOPS_LLC_HIT_RETIRED.XSNP_HITM:pp', 'MEM_LOAD_UOPS_LLC_HIT_RETIRED.XSNP_MISS:pp']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['DataSharing', 'Offcore']
    def compute(self, EV):
        try:
            self.val = (Mem_XSNP_HitM_Cost(self, EV, 4) * LOAD_XSNP_HITM(self, EV, 4) + Mem_XSNP_Hit_Cost(self, EV, 4) * LOAD_XSNP_MISS(self, EV, 4)) / CLKS(self, EV, 4)
            self.thresh = (self.val > 0.05) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Contested_Accesses zero division")
        return self.val
    desc = """
This metric estimates fraction of cycles while the memory
subsystem was handling synchronizations due to contested
accesses. Contested accesses occur when data written by one
Logical Processor are read by another Logical Processor on a
different Physical Core. Examples of contested accesses
include synchronizations such as locks; true data sharing
such as modified locked variables; and false sharing."""


class Data_Sharing:
    name = "Data_Sharing"
    domain = "Clocks_Estimated"
    area = "BE/Mem"
    level = 4
    htoff = False
    sample = ['MEM_LOAD_UOPS_LLC_HIT_RETIRED.XSNP_HIT:pp']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['Offcore']
    def compute(self, EV):
        try:
            self.val = Mem_XSNP_Hit_Cost(self, EV, 4) * LOAD_XSNP_HIT(self, EV, 4) / CLKS(self, EV, 4)
            self.thresh = (self.val > 0.05) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Data_Sharing zero division")
        return self.val
    desc = """
This metric estimates fraction of cycles while the memory
subsystem was handling synchronizations due to data-sharing
accesses. Data shared by multiple Logical Processors (even
just read shared) may cause increased access latency due to
cache coherency. Excessive data sharing can drastically harm
multithreaded performance."""


class L3_Hit_Latency:
    name = "L3_Hit_Latency"
    domain = "Clocks_Estimated"
    area = "BE/Mem"
    level = 4
    htoff = False
    sample = ['MEM_LOAD_UOPS_RETIRED.LLC_HIT:pp']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['MemoryLat']
    def compute(self, EV):
        try:
            self.val = Mem_XSNP_None_Cost(self, EV, 4) * LOAD_L3_HIT(self, EV, 4) / CLKS(self, EV, 4)
            self.thresh = (self.val > 0.1) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "L3_Hit_Latency zero division")
        return self.val
    desc = """
This metric represents fraction of cycles with demand load
accesses that hit the L3 cache under unloaded scenarios
(possibly L3 latency limited).  Avoiding private cache
misses (i.e. L2 misses/L3 hits) will improve the latency;
reduce contention with sibling physical cores and increase
performance.  Note the value of this node may overlap with
its siblings."""


class SQ_Full:
    name = "SQ_Full"
    domain = "Clocks"
    area = "BE/Mem"
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['MemoryBW', 'Offcore']
    def compute(self, EV):
        try:
            self.val = SQ_Full_Cycles(self, EV, 4) / CORE_CLKS(self, EV, 4)
            self.thresh = (self.val > 0.3) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "SQ_Full zero division")
        return self.val
    desc = """
This metric measures fraction of cycles where the Super
Queue (SQ) was full taking into account all request-types
and both hardware SMT threads (Logical Processors). The
Super Queue is used for requests to access the L2 cache or
to go out to the Uncore."""


class DRAM_Bound:
    name = "DRAM_Bound"
    domain = "Stalls"
    area = "BE/Mem"
    level = 3
    htoff = False
    sample = ['MEM_LOAD_UOPS_RETIRED.LLC_MISS:pp']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['MemoryBound', 'TmaL3mem']
    def compute(self, EV):
        try:
            self.val = (1 - Mem_L3_Hit_Fraction(self, EV, 3)) * EV("CYCLE_ACTIVITY.STALLS_L2_PENDING", 3) / CLKS(self, EV, 3)
            self.thresh = (self.val > 0.1) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "DRAM_Bound zero division")
        return self.val
    desc = """
This metric estimates how often the CPU was stalled on
accesses to external memory (DRAM) by loads. Better caching
can improve the latency and increase performance."""


class MEM_Bandwidth:
    name = "MEM_Bandwidth"
    domain = "Clocks"
    area = "BE/Mem"
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['MemoryBW', 'Offcore']
    def compute(self, EV):
        try:
            self.val = ORO_DRD_BW_Cycles(self, EV, 4) / CLKS(self, EV, 4)
            self.thresh = (self.val > 0.2) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "MEM_Bandwidth zero division")
        return self.val
    desc = """
This metric estimates fraction of cycles where the core's
performance was likely hurt due to approaching bandwidth
limits of external memory (DRAM).  The underlying heuristic
assumes that a similar off-core traffic is generated by all
IA cores. This metric does not aggregate non-data-read
requests by this logical processor; requests from other IA
Logical Processors/Physical Cores/sockets; or other non-IA
devices like GPU; hence the maximum external memory
bandwidth limits may or may not be approached when this
metric is flagged (see Uncore counters for that)."""


class MEM_Latency:
    name = "MEM_Latency"
    domain = "Clocks"
    area = "BE/Mem"
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['MemoryLat', 'Offcore']
    def compute(self, EV):
        try:
            self.val = ORO_DRD_Any_Cycles(self, EV, 4) / CLKS(self, EV, 4) - self.MEM_Bandwidth.compute(EV)
            self.thresh = (self.val > 0.1) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "MEM_Latency zero division")
        return self.val
    desc = """
This metric estimates fraction of cycles where the
performance was likely hurt due to latency from external
memory (DRAM).  This metric does not aggregate requests from
other Logical Processors/Physical Cores/sockets (see Uncore
counters for that)."""


class Local_DRAM:
    name = "Local_DRAM"
    domain = "Clocks_Estimated"
    area = "BE/Mem"
    level = 5
    htoff = False
    sample = ['MEM_LOAD_UOPS_RETIRED.LLC_MISS:pp']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['Server']
    def compute(self, EV):
        try:
            self.val = Mem_Local_DRAM_Cost(self, EV, 5) * LOAD_LCL_MEM(self, EV, 5) / CLKS(self, EV, 5)
            self.thresh = (self.val > 0.1) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Local_DRAM zero division")
        return self.val
    desc = """
This metric estimates fraction of cycles while the memory
subsystem was handling loads from local memory. Caching will
improve the latency and increase performance."""


class Remote_DRAM:
    name = "Remote_DRAM"
    domain = "Clocks_Estimated"
    area = "BE/Mem"
    level = 5
    htoff = False
    sample = ['MEM_LOAD_UOPS_LLC_MISS_RETIRED.REMOTE_DRAM:pp']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['Server']
    def compute(self, EV):
        try:
            self.val = Mem_Remote_DRAM_Cost(self, EV, 5) * LOAD_RMT_MEM(self, EV, 5) / CLKS(self, EV, 5)
            self.thresh = (self.val > 0.1) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Remote_DRAM zero division")
        return self.val
    desc = """
This metric estimates fraction of cycles while the memory
subsystem was handling loads from remote memory. This is
caused often due to non-optimal NUMA allocations."""


class Remote_Cache:
    name = "Remote_Cache"
    domain = "Clocks_Estimated"
    area = "BE/Mem"
    level = 5
    htoff = False
    sample = ['MEM_LOAD_UOPS_LLC_MISS_RETIRED.REMOTE_HITM:pp', 'MEM_LOAD_UOPS_LLC_MISS_RETIRED.REMOTE_FWD:pp']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['Offcore', 'Server']
    def compute(self, EV):
        try:
            self.val = (Mem_Remote_HitM_Cost(self, EV, 5) * LOAD_RMT_HITM(self, EV, 5) + Mem_Remote_Fwd_Cost(self, EV, 5) * LOAD_RMT_FWD(self, EV, 5)) / CLKS(self, EV, 5)
            self.thresh = (self.val > 0.05) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Remote_Cache zero division")
        return self.val
    desc = """
This metric estimates fraction of cycles while the memory
subsystem was handling loads from remote cache in other
sockets including synchronizations issues. This is caused
often due to non-optimal NUMA allocations."""


class Store_Bound:
    name = "Store_Bound"
    domain = "Stalls"
    area = "BE/Mem"
    level = 3
    htoff = False
    sample = ['MEM_UOPS_RETIRED.ALL_STORES:pp']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['MemoryBound', 'TmaL3mem']
    def compute(self, EV):
        try:
            self.val = EV("RESOURCE_STALLS.SB", 3) / CLKS(self, EV, 3)
            self.thresh = (self.val > 0.2) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Store_Bound zero division")
        return self.val
    desc = """
This metric estimates how often CPU was stalled  due to RFO
store memory accesses; RFO store issue a read-for-ownership
request before the write. Even though store accesses do not
typically stall out-of-order CPUs; there are few cases where
stores can lead to actual stalls. This metric will be
flagged should RFO stores be a bottleneck."""


class Store_Latency:
    name = "Store_Latency"
    domain = "Clocks_Estimated"
    area = "BE/Mem"
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['MemoryLat', 'Offcore']
    def compute(self, EV):
        try:
            self.val = (Store_L2_Hit_Cycles(self, EV, 4) + (1 - Mem_Lock_St_Fraction(self, EV, 4)) * ORO_Demand_RFO_C1(self, EV, 4)) / CLKS(self, EV, 4)
            self.thresh = (self.val > 0.1) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Store_Latency zero division")
        return self.val
    desc = """
This metric estimates fraction of cycles the CPU spent
handling L1D store misses. Store accesses usually less
impact out-of-order core performance; however; holding
resources for longer time can lead into undesired
implications (e.g. contention on L1D fill-buffer entries -
see FB_Full)"""


class False_Sharing:
    name = "False_Sharing"
    domain = "Clocks_Estimated"
    area = "BE/Mem"
    level = 4
    htoff = False
    sample = ['MEM_LOAD_UOPS_LLC_HIT_RETIRED.XSNP_HITM:pp', 'MEM_LOAD_UOPS_LLC_MISS_RETIRED.REMOTE_HITM:pp', 'OFFCORE_RESPONSE.DEMAND_RFO.LLC_HIT.HITM_OTHER_CORE', 'OFFCORE_RESPONSE.DEMAND_RFO.LLC_MISS.REMOTE_HITM']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['DataSharing', 'Offcore']
    def compute(self, EV):
        try:
            self.val = (Mem_Remote_HitM_Cost(self, EV, 4) * EV("OFFCORE_RESPONSE.DEMAND_RFO.LLC_MISS.REMOTE_HITM", 4) + Mem_XSNP_HitM_Cost(self, EV, 4) * EV("OFFCORE_RESPONSE.DEMAND_RFO.LLC_HIT.HITM_OTHER_CORE", 4)) / CLKS(self, EV, 4)
            self.thresh = (self.val > 0.05) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "False_Sharing zero division")
        return self.val
    desc = """
This metric roughly estimates how often CPU was handling
synchronizations due to False Sharing. False Sharing is a
multithreading hiccup; where multiple Logical Processors
contend on different data-elements mapped into the same
cache line."""


class Split_Stores:
    name = "Split_Stores"
    domain = "Core_Clocks"
    area = "BE/Mem"
    level = 4
    htoff = False
    sample = ['MEM_UOPS_RETIRED.SPLIT_STORES:pp']
    errcount = 0
    sibling = None
    server = False
    metricgroup = []
    def compute(self, EV):
        try:
            self.val = 2 * EV("MEM_UOPS_RETIRED.SPLIT_STORES", 4) / CORE_CLKS(self, EV, 4)
            self.thresh = (self.val > 0.2) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Split_Stores zero division")
        return self.val
    desc = """
This metric represents rate of split store accesses.
Consider aligning your data to the 64-byte cache line
granularity."""


class DTLB_Store:
    name = "DTLB_Store"
    domain = "Clocks_Estimated"
    area = "BE/Mem"
    level = 4
    htoff = False
    sample = ['MEM_UOPS_RETIRED.STLB_MISS_STORES:pp']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['MemoryTLB']
    def compute(self, EV):
        try:
            self.val = (Mem_STLB_Hit_Cost * EV("DTLB_STORE_MISSES.STLB_HIT", 4) + EV("DTLB_STORE_MISSES.WALK_DURATION", 4)) / CLKS(self, EV, 4)
            self.thresh = (self.val > 0.05) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "DTLB_Store zero division")
        return self.val
    desc = """
This metric roughly estimates the fraction of cycles spent
handling first-level data TLB store misses.  As with
ordinary data caching; focus on improving data locality and
reducing working-set size to reduce DTLB overhead.
Additionally; consider using profile-guided optimization
(PGO) to collocate frequently-used data on the same page.
Try using larger page sizes for large amounts of frequently-
used data."""


class Core_Bound:
    name = "Core_Bound"
    domain = "Slots"
    area = "BE/Core"
    level = 2
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['Backend', 'TmaL2', 'Compute']
    def compute(self, EV):
        try:
            self.val = self.Backend_Bound.compute(EV) - self.Memory_Bound.compute(EV)
            self.thresh = (self.val > 0.1) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Core_Bound zero division")
        return self.val
    desc = """
This metric represents fraction of slots where Core non-
memory issues were of a bottleneck.  Shortage in hardware
compute resources; or dependencies in software's
instructions are both categorized under Core Bound. Hence it
may indicate the machine ran out of an out-of-order
resource; certain execution units are overloaded or
dependencies in program's data- or instruction-flow are
limiting the performance (e.g. FP-chained long-latency
arithmetic operations)."""


class Divider:
    name = "Divider"
    domain = "Clocks"
    area = "BE/Core"
    level = 3
    htoff = False
    sample = ['ARITH.FPU_DIV_ACTIVE']
    errcount = 0
    sibling = None
    server = False
    metricgroup = []
    def compute(self, EV):
        try:
            self.val = EV("ARITH.FPU_DIV_ACTIVE", 3) / CORE_CLKS(self, EV, 3)
            self.thresh = (self.val > 0.2) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Divider zero division")
        return self.val
    desc = """
This metric represents fraction of cycles where the Divider
unit was active. Divide and square root instructions are
performed by the Divider unit and can take considerably
longer latency than integer or Floating Point addition;
subtraction; or multiplication."""


class Ports_Utilization:
    name = "Ports_Utilization"
    domain = "Clocks"
    area = "BE/Core"
    level = 3
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['PortsUtil']
    def compute(self, EV):
        try:
            self.val = (Backend_Bound_Cycles(self, EV, 3) - EV("RESOURCE_STALLS.SB", 3) - STALLS_MEM_ANY(self, EV, 3)) / CLKS(self, EV, 3)
            self.thresh = (self.val > 0.2) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Ports_Utilization zero division")
        return self.val
    desc = """
This metric estimates fraction of cycles the CPU performance
was potentially limited due to Core computation issues (non
divider-related).  Two distinct categories can be attributed
into this metric: (1) heavy data-dependency among contiguous
instructions would manifest in this metric - such cases are
often referred to as low Instruction Level Parallelism
(ILP). (2) Contention on some hardware execution unit other
than Divider. For example; when there are too many multiply
operations."""


class Ports_Utilized_0:
    name = "Ports_Utilized_0"
    domain = "Clocks"
    area = "BE/Core"
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['PortsUtil']
    def compute(self, EV):
        try:
            self.val = Cycles_0_Ports_Utilized(self, EV, 4) / CORE_CLKS(self, EV, 4)
            self.thresh = (self.val > 0.2) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Ports_Utilized_0 zero division")
        return self.val
    desc = """
This metric represents fraction of cycles CPU executed no
uops on any execution port (Logical Processor cycles since
ICL, Physical Core cycles otherwise). Long-latency
instructions like divides may contribute to this metric."""


class Ports_Utilized_1:
    name = "Ports_Utilized_1"
    domain = "Clocks"
    area = "BE/Core"
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['PortsUtil']
    def compute(self, EV):
        try:
            self.val = Cycles_1_Port_Utilized(self, EV, 4) / CORE_CLKS(self, EV, 4)
            self.thresh = (self.val > 0.2) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Ports_Utilized_1 zero division")
        return self.val
    desc = """
This metric represents fraction of cycles where the CPU
executed total of 1 uop per cycle on all execution ports
(Logical Processor cycles since ICL, Physical Core cycles
otherwise). This can be due to heavy data-dependency among
software instructions; or over oversubscribing a particular
hardware resource. In some other cases with high
1_Port_Utilized and L1_Bound; this metric can point to L1
data-cache latency bottleneck that may not necessarily
manifest with complete execution starvation (due to the
short L1 latency e.g. walking a linked list) - looking at
the assembly can be helpful."""


class Ports_Utilized_2:
    name = "Ports_Utilized_2"
    domain = "Clocks"
    area = "BE/Core"
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['PortsUtil']
    def compute(self, EV):
        try:
            self.val = Cycles_2_Ports_Utilized(self, EV, 4) / CORE_CLKS(self, EV, 4)
            self.thresh = (self.val > 0.2) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Ports_Utilized_2 zero division")
        return self.val
    desc = """
This metric represents fraction of cycles CPU executed total
of 2 uops per cycle on all execution ports (Logical
Processor cycles since ICL, Physical Core cycles otherwise).
Loop Vectorization -most compilers feature auto-
Vectorization options today- reduces pressure on the
execution ports as multiple elements are calculated with
same uop."""


class Ports_Utilized_3m:
    name = "Ports_Utilized_3m"
    domain = "Clocks"
    area = "BE/Core"
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['PortsUtil']
    def compute(self, EV):
        try:
            self.val = Cycles_3m_Ports_Utilized(self, EV, 4) / CORE_CLKS(self, EV, 4)
            self.thresh = (self.val > 0.7) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Ports_Utilized_3m zero division")
        return self.val
    desc = """
This metric represents fraction of cycles CPU executed total
of 3 or more uops per cycle on all execution ports (Logical
Processor cycles since ICL, Physical Core cycles otherwise)."""


class ALU_Op_Utilization:
    name = "ALU_Op_Utilization"
    domain = "Core_Execution"
    area = "BE/Core"
    level = 5
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = []
    def compute(self, EV):
        try:
            self.val = (EV("UOPS_DISPATCHED_PORT.PORT_0", 5) + EV("UOPS_DISPATCHED_PORT.PORT_1", 5) + EV("UOPS_DISPATCHED_PORT.PORT_5", 5)) / (3 * CORE_CLKS(self, EV, 5))
            self.thresh = (self.val > 0.6)
        except ZeroDivisionError:
            handle_error(self, "ALU_Op_Utilization zero division")
        return self.val
    desc = """
This metric represents Core fraction of cycles CPU
dispatched uops on execution ports for ALU operations."""


class Port_0:
    name = "Port_0"
    domain = "Core_Clocks"
    area = "BE/Core"
    level = 6
    htoff = False
    sample = ['UOPS_DISPATCHED_PORT.PORT_0']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['Compute']
    def compute(self, EV):
        try:
            self.val = EV("UOPS_DISPATCHED_PORT.PORT_0", 6) / CORE_CLKS(self, EV, 6)
            self.thresh = (self.val > 0.6)
        except ZeroDivisionError:
            handle_error(self, "Port_0 zero division")
        return self.val
    desc = """
This metric represents Core fraction of cycles CPU
dispatched uops on execution port 0 ALU"""


class Port_1:
    name = "Port_1"
    domain = "Core_Clocks"
    area = "BE/Core"
    level = 6
    htoff = False
    sample = ['UOPS_DISPATCHED_PORT.PORT_1']
    errcount = 0
    sibling = None
    server = False
    metricgroup = []
    def compute(self, EV):
        try:
            self.val = EV("UOPS_DISPATCHED_PORT.PORT_1", 6) / CORE_CLKS(self, EV, 6)
            self.thresh = (self.val > 0.6)
        except ZeroDivisionError:
            handle_error(self, "Port_1 zero division")
        return self.val
    desc = """
This metric represents Core fraction of cycles CPU
dispatched uops on execution port 1 (ALU)"""


class Port_5:
    name = "Port_5"
    domain = "Core_Clocks"
    area = "BE/Core"
    level = 6
    htoff = False
    sample = ['UOPS_DISPATCHED_PORT.PORT_5']
    errcount = 0
    sibling = None
    server = False
    metricgroup = []
    def compute(self, EV):
        try:
            self.val = EV("UOPS_DISPATCHED_PORT.PORT_5", 6) / CORE_CLKS(self, EV, 6)
            self.thresh = (self.val > 0.6)
        except ZeroDivisionError:
            handle_error(self, "Port_5 zero division")
        return self.val
    desc = """
This metric represents Core fraction of cycles CPU
dispatched uops on execution port 5 Branches and ALU"""


class Load_Op_Utilization:
    name = "Load_Op_Utilization"
    domain = "Core_Execution"
    area = "BE/Core"
    level = 5
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = []
    def compute(self, EV):
        try:
            self.val = (EV("UOPS_DISPATCHED_PORT.PORT_2", 5) + EV("UOPS_DISPATCHED_PORT.PORT_3", 5) - EV("UOPS_DISPATCHED_PORT.PORT_4", 5)) / (2 * CORE_CLKS(self, EV, 5))
            self.thresh = (self.val > 0.6)
        except ZeroDivisionError:
            handle_error(self, "Load_Op_Utilization zero division")
        return self.val
    desc = """
This metric represents Core fraction of cycles CPU
dispatched uops on execution port for Load operations"""


class Port_2:
    name = "Port_2"
    domain = "Core_Clocks"
    area = "BE/Core"
    level = 6
    htoff = False
    sample = ['UOPS_DISPATCHED_PORT.PORT_2']
    errcount = 0
    sibling = None
    server = False
    metricgroup = []
    def compute(self, EV):
        try:
            self.val = EV("UOPS_DISPATCHED_PORT.PORT_2", 6) / CORE_CLKS(self, EV, 6)
            self.thresh = (self.val > 0.6)
        except ZeroDivisionError:
            handle_error(self, "Port_2 zero division")
        return self.val
    desc = """
This metric represents Core fraction of cycles CPU
dispatched uops on execution port 2 ([SNB+]Loads and Store-
address; [ICL+] Loads)"""


class Port_3:
    name = "Port_3"
    domain = "Core_Clocks"
    area = "BE/Core"
    level = 6
    htoff = False
    sample = ['UOPS_DISPATCHED_PORT.PORT_3']
    errcount = 0
    sibling = None
    server = False
    metricgroup = []
    def compute(self, EV):
        try:
            self.val = EV("UOPS_DISPATCHED_PORT.PORT_3", 6) / CORE_CLKS(self, EV, 6)
            self.thresh = (self.val > 0.6)
        except ZeroDivisionError:
            handle_error(self, "Port_3 zero division")
        return self.val
    desc = """
This metric represents Core fraction of cycles CPU
dispatched uops on execution port 3 ([SNB+]Loads and Store-
address; [ICL+] Loads)"""


class Store_Op_Utilization:
    name = "Store_Op_Utilization"
    domain = "Core_Execution"
    area = "BE/Core"
    level = 5
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = []
    def compute(self, EV):
        try:
            self.val = EV("UOPS_DISPATCHED_PORT.PORT_4", 5) / CORE_CLKS(self, EV, 5)
            self.thresh = (self.val > 0.6)
        except ZeroDivisionError:
            handle_error(self, "Store_Op_Utilization zero division")
        return self.val
    desc = """
This metric represents Core fraction of cycles CPU
dispatched uops on execution port for Store operations"""


class Port_4:
    name = "Port_4"
    domain = "Core_Clocks"
    area = "BE/Core"
    level = 6
    htoff = False
    sample = ['UOPS_DISPATCHED_PORT.PORT_4']
    errcount = 0
    sibling = None
    server = False
    metricgroup = []
    def compute(self, EV):
        try:
            self.val = EV("UOPS_DISPATCHED_PORT.PORT_4", 6) / CORE_CLKS(self, EV, 6)
            self.thresh = (self.val > 0.6)
        except ZeroDivisionError:
            handle_error(self, "Port_4 zero division")
        return self.val
    desc = """
This metric represents Core fraction of cycles CPU
dispatched uops on execution port 4 (Store-data)"""


class Retiring:
    name = "Retiring"
    domain = "Slots"
    area = "RET"
    level = 1
    htoff = False
    sample = ['UOPS_RETIRED.RETIRE_SLOTS']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['TmaL1']
    def compute(self, EV):
        try:
            self.val = Retired_Slots(self, EV, 1) / SLOTS(self, EV, 1)
            self.thresh = (self.val > 0.75) | self.Heavy_Operations.thresh
        except ZeroDivisionError:
            handle_error(self, "Retiring zero division")
        return self.val
    desc = """
This category represents fraction of slots utilized by
useful work i.e. issued uops that eventually get retired.
Ideally; all pipeline slots would be attributed to the
Retiring category.  Retiring of 100% would indicate the
maximum Pipeline_Width throughput was achieved.  Maximizing
Retiring typically increases the Instructions-per-cycle (see
IPC metric). Note that a high Retiring value does not
necessary mean there is no room for more performance.  For
example; Heavy-operations or Microcode Assists are
categorized under Retiring. They often indicate suboptimal
performance and can often be optimized or avoided."""


class Light_Operations:
    name = "Light_Operations"
    domain = "Slots"
    area = "RET"
    level = 2
    htoff = False
    sample = ['INST_RETIRED.PREC_DIST']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['Retire', 'TmaL2']
    def compute(self, EV):
        try:
            self.val = self.Retiring.compute(EV) - self.Heavy_Operations.compute(EV)
            self.thresh = (self.val > 0.6) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Light_Operations zero division")
        return self.val
    desc = """
This metric represents fraction of slots where the CPU was
retiring light-weight operations -- instructions that
require no more than one uop (micro-operation). This
correlates with total number of instructions used by the
program. A uops-per-instruction (see UPI metric) ratio of 1
or less should be expected on modern Intel Core generations.
While this often indicates efficient X86 instructions were
executed; high values does not necessarily mean further
performance cannot be gained."""


class FP_Arith:
    name = "FP_Arith"
    domain = "Uops"
    area = "RET"
    level = 3
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['HPC', 'Retire']
    def compute(self, EV):
        try:
            self.val = self.X87_Use.compute(EV) + self.FP_Scalar.compute(EV) + self.FP_Vector.compute(EV)
            self.thresh = (self.val > 0.2) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "FP_Arith zero division")
        return self.val
    desc = """
This metric represents overall arithmetic floating-point
(FP) uops fraction the CPU has executed (retired)"""


class X87_Use:
    name = "X87_Use"
    domain = "Uops"
    area = "RET"
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['Compute']
    def compute(self, EV):
        try:
            self.val = EV("FP_COMP_OPS_EXE.X87", 4) / EV("UOPS_EXECUTED.THREAD", 4)
            self.thresh = (self.val > 0.1) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "X87_Use zero division")
        return self.val
    desc = """
This metric serves as an approximation of legacy x87 usage.
It accounts for instructions beyond X87 FP arithmetic
operations; hence may be used as a thermometer to avoid X87
high usage and preferably upgrade to modern ISA. Tip:
consider compiler flags to generate newer AVX (or SSE)
instruction sets; which typically perform better and feature
vectors."""


class FP_Scalar:
    name = "FP_Scalar"
    domain = "Uops"
    area = "RET"
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['Compute', 'Flops']
    def compute(self, EV):
        try:
            self.val = FP_Arith_Scalar(self, EV, 4) / EV("UOPS_EXECUTED.THREAD", 4)
            self.thresh = (self.val > 0.1) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "FP_Scalar zero division")
        return self.val
    desc = """
This metric represents arithmetic floating-point (FP) scalar
uops fraction the CPU has executed (retired)."""


class FP_Vector:
    name = "FP_Vector"
    domain = "Uops"
    area = "RET"
    level = 4
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['Compute', 'Flops']
    def compute(self, EV):
        try:
            self.val = FP_Arith_Vector(self, EV, 4) / EV("UOPS_EXECUTED.THREAD", 4)
            self.thresh = (self.val > 0.2) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "FP_Vector zero division")
        return self.val
    desc = """
This metric represents arithmetic floating-point (FP) vector
uops fraction the CPU has executed (retired) aggregated
across all vector widths."""


class Other_Light_Ops:
    name = "Other_Light_Ops"
    domain = "Uops"
    area = "RET"
    level = 3
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['Pipeline', 'Retire']
    def compute(self, EV):
        try:
            self.val = 1 - self.FP_Arith.compute(EV)
            self.thresh = (self.val > 0.3) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Other_Light_Ops zero division")
        return self.val
    desc = """
This metric represents non-floating-point (FP) uop fraction
the CPU has executed. If you application has no FP
operations and performs with decent IPC (Instructions Per
Cycle); this node will likely be biggest fraction."""


class Heavy_Operations:
    name = "Heavy_Operations"
    domain = "Slots"
    area = "RET"
    level = 2
    htoff = False
    sample = []
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['Retire', 'TmaL2']
    def compute(self, EV):
        try:
            self.val = self.Microcode_Sequencer.compute(EV)
            self.thresh = (self.val > 0.1)
        except ZeroDivisionError:
            handle_error(self, "Heavy_Operations zero division")
        return self.val
    desc = """
This metric represents fraction of slots where the CPU was
retiring heavy-weight operations -- instructions that
require two or more uops ([ICL/TGL] this metric accounts
only for the subset of heavy operations that are delivered
by the microcode sequencer unit). This highly-correlates
with the uop length of these instructions."""


class Microcode_Sequencer:
    name = "Microcode_Sequencer"
    domain = "Slots"
    area = "RET"
    level = 3
    htoff = False
    sample = ['IDQ.MS_UOPS']
    errcount = 0
    sibling = None
    server = False
    metricgroup = ['MicroSeq', 'Retire']
    def compute(self, EV):
        try:
            self.val = Retire_Fraction(self, EV, 3) * EV("IDQ.MS_UOPS", 3) / SLOTS(self, EV, 3)
            self.thresh = (self.val > 0.05) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Microcode_Sequencer zero division")
        return self.val
    desc = """
This metric represents fraction of slots the CPU was
retiring uops fetched by the Microcode Sequencer (MS) unit.
The MS is used for CISC instructions not supported by the
default decoders (like repeat move strings; or CPUID); or by
microcode assists used to address some operation modes (like
in Floating Point assists). These cases can often be
avoided."""


class Assists:
    name = "Assists"
    domain = "Slots_Estimated"
    area = "RET"
    level = 4
    htoff = False
    sample = ['OTHER_ASSISTS.ANY_WB_ASSIST']
    errcount = 0
    sibling = None
    server = False
    metricgroup = []
    def compute(self, EV):
        try:
            self.val = Avg_Assist_Cost * EV("OTHER_ASSISTS.ANY_WB_ASSIST", 4) / SLOTS(self, EV, 4)
            self.thresh = (self.val > 0.1) & self.parent.thresh
        except ZeroDivisionError:
            handle_error(self, "Assists zero division")
        return self.val
    desc = """
This metric estimates fraction of cycles the CPU retired
uops delivered by the Microcode_Sequencer as a result of
Assists. Assists are long sequences of uops that are
required in certain corner-cases for operations that cannot
be handled natively by the execution pipeline. For example;
when working with very small floating point values (so-
called Denormals); the FP units are not set up to perform
these operations natively. Instead; a sequence of
instructions to perform the computation on the Denormals is
injected into the pipeline. Since these microcode sequences
might be hundreds of uops long; Assists can be extremely
deleterious to performance and they can be avoided in many
cases."""


class Metric_IPC:
    name = "IPC"
    domain = "Metric"
    maxval = Pipeline_Width + 1
    server = False
    errcount = 0
    area = "Info.Thread"
    metricgroup = ['Summary']
    sibling = None

    def compute(self, EV):
        try:
            self.val = IPC(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "IPC zero division")
    desc = """
Instructions Per Cycle (per Logical Processor)"""


class Metric_UPI:
    name = "UPI"
    domain = "Metric"
    maxval = 2
    server = False
    errcount = 0
    area = "Info.Thread"
    metricgroup = ['Pipeline', 'Retire']
    sibling = None

    def compute(self, EV):
        try:
            self.val = UPI(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "UPI zero division")
    desc = """
Uops Per Instruction"""


class Metric_IpTB:
    name = "IpTB"
    domain = "Metric"
    maxval = 0
    server = False
    errcount = 0
    area = "Info.Thread"
    metricgroup = ['Branches', 'FetchBW', 'PGO']
    sibling = None

    def compute(self, EV):
        try:
            self.val = IpTB(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "IpTB zero division")
    desc = """
Instruction per taken branch"""


class Metric_CPI:
    name = "CPI"
    domain = "Metric"
    maxval = 0
    server = False
    errcount = 0
    area = "Info.Thread"
    metricgroup = ['Pipeline']
    sibling = None

    def compute(self, EV):
        try:
            self.val = CPI(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "CPI zero division")
    desc = """
Cycles Per Instruction (per Logical Processor)"""


class Metric_CLKS:
    name = "CLKS"
    domain = "Count"
    maxval = 0
    server = False
    errcount = 0
    area = "Info.Thread"
    metricgroup = ['Pipeline']
    sibling = None

    def compute(self, EV):
        try:
            self.val = CLKS(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "CLKS zero division")
    desc = """
Per-Logical Processor actual clocks when the Logical
Processor is active."""


class Metric_SLOTS:
    name = "SLOTS"
    domain = "Count"
    maxval = 0
    server = False
    errcount = 0
    area = "Info.Thread"
    metricgroup = ['TmaL1']
    sibling = None

    def compute(self, EV):
        try:
            self.val = SLOTS(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "SLOTS zero division")
    desc = """
Total issue-pipeline slots (per-Physical Core till ICL; per-
Logical Processor ICL onward)"""


class Metric_CoreIPC:
    name = "CoreIPC"
    domain = "CoreMetric"
    maxval = 5
    server = False
    errcount = 0
    area = "Info.Core"
    metricgroup = ['SMT', 'TmaL1']
    sibling = None

    def compute(self, EV):
        try:
            self.val = CoreIPC(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "CoreIPC zero division")
    desc = """
Instructions Per Cycle (per physical core)"""


class Metric_FLOPc:
    name = "FLOPc"
    domain = "CoreMetric"
    maxval = 10
    server = False
    errcount = 0
    area = "Info.Core"
    metricgroup = ['Flops']
    sibling = None

    def compute(self, EV):
        try:
            self.val = FLOPc(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "FLOPc zero division")
    desc = """
Floating Point Operations Per Cycle"""


class Metric_ILP:
    name = "ILP"
    domain = "CoreMetric"
    maxval = 10
    server = False
    errcount = 0
    area = "Info.Core"
    metricgroup = ['Pipeline', 'PortsUtil']
    sibling = None

    def compute(self, EV):
        try:
            self.val = ILP(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "ILP zero division")
    desc = """
Instruction-Level-Parallelism (average number of uops
executed when there is at least 1 uop executed)"""


class Metric_IpMispredict:
    name = "IpMispredict"
    domain = "Metric"
    maxval = 0
    server = False
    errcount = 0
    area = "Info.Core"
    metricgroup = ['BrMispredicts']
    sibling = None

    def compute(self, EV):
        try:
            self.val = IpMispredict(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "IpMispredict zero division")
    desc = """
Number of Instructions per non-speculative Branch
Misprediction (JEClear)"""


class Metric_CORE_CLKS:
    name = "CORE_CLKS"
    domain = "Count"
    maxval = 0
    server = False
    errcount = 0
    area = "Info.Core"
    metricgroup = ['SMT']
    sibling = None

    def compute(self, EV):
        try:
            self.val = CORE_CLKS(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "CORE_CLKS zero division")
    desc = """
Core actual clocks when any Logical Processor is active on
the Physical Core"""


class Metric_IpLoad:
    name = "IpLoad"
    domain = "Metric"
    maxval = 0
    server = False
    errcount = 0
    area = "Info.Inst_Mix"
    metricgroup = ['InsType']
    sibling = None

    def compute(self, EV):
        try:
            self.val = IpLoad(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "IpLoad zero division")
    desc = """
Instructions per Load (lower number means higher occurrence
rate)"""


class Metric_IpStore:
    name = "IpStore"
    domain = "Metric"
    maxval = 0
    server = False
    errcount = 0
    area = "Info.Inst_Mix"
    metricgroup = ['InsType']
    sibling = None

    def compute(self, EV):
        try:
            self.val = IpStore(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "IpStore zero division")
    desc = """
Instructions per Store (lower number means higher occurrence
rate)"""


class Metric_IpBranch:
    name = "IpBranch"
    domain = "Metric"
    maxval = 0
    server = False
    errcount = 0
    area = "Info.Inst_Mix"
    metricgroup = ['Branches', 'InsType']
    sibling = None

    def compute(self, EV):
        try:
            self.val = IpBranch(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "IpBranch zero division")
    desc = """
Instructions per Branch (lower number means higher
occurrence rate)"""


class Metric_IpCall:
    name = "IpCall"
    domain = "Metric"
    maxval = 0
    server = False
    errcount = 0
    area = "Info.Inst_Mix"
    metricgroup = ['Branches']
    sibling = None

    def compute(self, EV):
        try:
            self.val = IpCall(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "IpCall zero division")
    desc = """
Instructions per (near) call (lower number means higher
occurrence rate)"""


class Metric_BpTkBranch:
    name = "BpTkBranch"
    domain = "Metric"
    maxval = 0
    server = False
    errcount = 0
    area = "Info.Inst_Mix"
    metricgroup = ['Branches', 'PGO']
    sibling = None

    def compute(self, EV):
        try:
            self.val = BpTkBranch(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "BpTkBranch zero division")
    desc = """
Branch instructions per taken branch."""


class Metric_IpArith:
    name = "IpArith"
    domain = "Metric"
    maxval = 0
    server = False
    errcount = 0
    area = "Info.Inst_Mix"
    metricgroup = ['Flops', 'FpArith', 'InsType']
    sibling = None

    def compute(self, EV):
        try:
            self.val = IpArith(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "IpArith zero division")
    desc = """
Instructions per FP Arithmetic instruction (lower number
means higher occurrence rate). Approximated prior to BDW."""


class Metric_Instructions:
    name = "Instructions"
    domain = "Count"
    maxval = 0
    server = False
    errcount = 0
    area = "Info.Inst_Mix"
    metricgroup = ['Summary', 'TmaL1']
    sibling = None

    def compute(self, EV):
        try:
            self.val = Instructions(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "Instructions zero division")
    desc = """
Total number of retired Instructions"""


class Metric_DSB_Coverage:
    name = "DSB_Coverage"
    domain = "Metric"
    maxval = 1
    server = False
    errcount = 0
    area = "Info.Frontend"
    metricgroup = ['DSB', 'FetchBW']
    sibling = None

    def compute(self, EV):
        try:
            self.val = DSB_Coverage(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "DSB_Coverage zero division")
    desc = """
Fraction of Uops delivered by the DSB (aka Decoded ICache;
or Uop Cache)"""


class Metric_Load_Miss_Real_Latency:
    name = "Load_Miss_Real_Latency"
    domain = "Metric"
    maxval = 1000
    server = False
    errcount = 0
    area = "Info.Memory"
    metricgroup = ['MemoryBound', 'MemoryLat']
    sibling = None

    def compute(self, EV):
        try:
            self.val = Load_Miss_Real_Latency(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "Load_Miss_Real_Latency zero division")
    desc = """
Actual Average Latency for L1 data-cache miss demand loads
(in core cycles)"""


class Metric_MLP:
    name = "MLP"
    domain = "Metric"
    maxval = 10
    server = False
    errcount = 0
    area = "Info.Memory"
    metricgroup = ['MemoryBound', 'MemoryBW']
    sibling = None

    def compute(self, EV):
        try:
            self.val = MLP(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "MLP zero division")
    desc = """
Memory-Level-Parallelism (average number of L1 miss demand
load when there is at least one such miss. Per-Logical
Processor)"""


class Metric_Page_Walks_Utilization:
    name = "Page_Walks_Utilization"
    domain = "CoreMetric"
    maxval = 1
    server = False
    errcount = 0
    area = "Info.Memory"
    metricgroup = ['MemoryTLB']
    sibling = None

    def compute(self, EV):
        try:
            self.val = Page_Walks_Utilization(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "Page_Walks_Utilization zero division")
    desc = """
Utilization of the core's Page Walker(s) serving STLB misses
triggered by instruction/Load/Store accesses"""


class Metric_L1D_Cache_Fill_BW:
    name = "L1D_Cache_Fill_BW"
    domain = "GB/sec"
    maxval = 0
    server = False
    errcount = 0
    area = "Info.Memory"
    metricgroup = ['MemoryBW']
    sibling = None

    def compute(self, EV):
        try:
            self.val = L1D_Cache_Fill_BW(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "L1D_Cache_Fill_BW zero division")
    desc = """
Average data fill bandwidth to the L1 data cache [GB / sec]"""


class Metric_L2_Cache_Fill_BW:
    name = "L2_Cache_Fill_BW"
    domain = "GB/sec"
    maxval = 0
    server = False
    errcount = 0
    area = "Info.Memory"
    metricgroup = ['MemoryBW']
    sibling = None

    def compute(self, EV):
        try:
            self.val = L2_Cache_Fill_BW(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "L2_Cache_Fill_BW zero division")
    desc = """
Average data fill bandwidth to the L2 cache [GB / sec]"""


class Metric_L3_Cache_Fill_BW:
    name = "L3_Cache_Fill_BW"
    domain = "GB/sec"
    maxval = 0
    server = False
    errcount = 0
    area = "Info.Memory"
    metricgroup = ['MemoryBW']
    sibling = None

    def compute(self, EV):
        try:
            self.val = L3_Cache_Fill_BW(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "L3_Cache_Fill_BW zero division")
    desc = """
Average per-core data fill bandwidth to the L3 cache [GB /
sec]"""


class Metric_L1MPKI:
    name = "L1MPKI"
    domain = "Metric"
    maxval = 0
    server = False
    errcount = 0
    area = "Info.Memory"
    metricgroup = ['CacheMisses']
    sibling = None

    def compute(self, EV):
        try:
            self.val = L1MPKI(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "L1MPKI zero division")
    desc = """
L1 cache true misses per kilo instruction for retired demand
loads"""


class Metric_L2MPKI:
    name = "L2MPKI"
    domain = "Metric"
    maxval = 0
    server = False
    errcount = 0
    area = "Info.Memory"
    metricgroup = ['CacheMisses']
    sibling = None

    def compute(self, EV):
        try:
            self.val = L2MPKI(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "L2MPKI zero division")
    desc = """
L2 cache true misses per kilo instruction for retired demand
loads"""


class Metric_L3MPKI:
    name = "L3MPKI"
    domain = "Metric"
    maxval = 0
    server = False
    errcount = 0
    area = "Info.Memory"
    metricgroup = ['CacheMisses']
    sibling = None

    def compute(self, EV):
        try:
            self.val = L3MPKI(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "L3MPKI zero division")
    desc = """
L3 cache true misses per kilo instruction for retired demand
loads"""


class Metric_CPU_Utilization:
    name = "CPU_Utilization"
    domain = "Metric"
    maxval = 200
    server = False
    errcount = 0
    area = "Info.System"
    metricgroup = ['HPC', 'Summary']
    sibling = None

    def compute(self, EV):
        try:
            self.val = CPU_Utilization(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "CPU_Utilization zero division")
    desc = """
Average CPU Utilization"""


class Metric_Average_Frequency:
    name = "Average_Frequency"
    domain = "SystemMetric"
    maxval = 0
    server = False
    errcount = 0
    area = "Info.System"
    metricgroup = ['Summary', 'Power']
    sibling = None

    def compute(self, EV):
        try:
            self.val = Average_Frequency(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "Average_Frequency zero division")
    desc = """
Measured Average Frequency for unhalted processors [GHz]"""


class Metric_GFLOPs:
    name = "GFLOPs"
    domain = "Metric"
    maxval = 200
    server = False
    errcount = 0
    area = "Info.System"
    metricgroup = ['Flops', 'HPC']
    sibling = None

    def compute(self, EV):
        try:
            self.val = GFLOPs(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "GFLOPs zero division")
    desc = """
Giga Floating Point Operations Per Second"""


class Metric_Turbo_Utilization:
    name = "Turbo_Utilization"
    domain = "CoreMetric"
    maxval = 10
    server = False
    errcount = 0
    area = "Info.System"
    metricgroup = ['Power']
    sibling = None

    def compute(self, EV):
        try:
            self.val = Turbo_Utilization(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "Turbo_Utilization zero division")
    desc = """
Average Frequency Utilization relative nominal frequency"""


class Metric_SMT_2T_Utilization:
    name = "SMT_2T_Utilization"
    domain = "CoreMetric"
    maxval = 1
    server = False
    errcount = 0
    area = "Info.System"
    metricgroup = ['SMT']
    sibling = None

    def compute(self, EV):
        try:
            self.val = SMT_2T_Utilization(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "SMT_2T_Utilization zero division")
    desc = """
Fraction of cycles where both hardware Logical Processors
were active"""


class Metric_Kernel_Utilization:
    name = "Kernel_Utilization"
    domain = "Metric"
    maxval = 1
    server = False
    errcount = 0
    area = "Info.System"
    metricgroup = ['OS']
    sibling = None

    def compute(self, EV):
        try:
            self.val = Kernel_Utilization(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "Kernel_Utilization zero division")
    desc = """
Fraction of cycles spent in the Operating System (OS) Kernel
mode"""


class Metric_DRAM_BW_Use:
    name = "DRAM_BW_Use"
    domain = "GB/sec"
    maxval = 200
    server = False
    errcount = 0
    area = "Info.System"
    metricgroup = ['HPC', 'MemoryBW', 'SoC']
    sibling = None

    def compute(self, EV):
        try:
            self.val = DRAM_BW_Use(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "DRAM_BW_Use zero division")
    desc = """
Average external Memory Bandwidth Use for reads and writes
[GB / sec]"""


class Metric_MEM_Read_Latency:
    name = "MEM_Read_Latency"
    domain = "NanoSeconds"
    maxval = 1000
    server = False
    errcount = 0
    area = "Info.System"
    metricgroup = ['MemoryLat', 'SoC']
    sibling = None

    def compute(self, EV):
        try:
            self.val = MEM_Read_Latency(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "MEM_Read_Latency zero division")
    desc = """
Average latency of data read request to external memory (in
nanoseconds). Accounts for demand loads and L1/L2 prefetches"""


class Metric_MEM_Parallel_Reads:
    name = "MEM_Parallel_Reads"
    domain = "SystemMetric"
    maxval = 100
    server = False
    errcount = 0
    area = "Info.System"
    metricgroup = ['MemoryBW', 'SoC']
    sibling = None

    def compute(self, EV):
        try:
            self.val = MEM_Parallel_Reads(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "MEM_Parallel_Reads zero division")
    desc = """
Average number of parallel data read requests to external
memory. Accounts for demand loads and L1/L2 prefetches"""


class Metric_Time:
    name = "Time"
    domain = "Seconds"
    maxval = 0
    server = False
    errcount = 0
    area = "Info.System"
    metricgroup = ['Summary']
    sibling = None

    def compute(self, EV):
        try:
            self.val = Time(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "Time zero division")
    desc = """
Run duration time in seconds"""


class Metric_Socket_CLKS:
    name = "Socket_CLKS"
    domain = "Count"
    maxval = 0
    server = False
    errcount = 0
    area = "Info.System"
    metricgroup = ['SoC']
    sibling = None

    def compute(self, EV):
        try:
            self.val = Socket_CLKS(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "Socket_CLKS zero division")
    desc = """
Socket actual clocks when any core is active on that socket"""


class Metric_IpFarBranch:
    name = "IpFarBranch"
    domain = "Metric"
    maxval = 0
    server = False
    errcount = 0
    area = "Info.System"
    metricgroup = ['Branches', 'OS']
    sibling = None

    def compute(self, EV):
        try:
            self.val = IpFarBranch(self, EV, 0)
        except ZeroDivisionError:
            handle_error_metric(self, "IpFarBranch zero division")
    desc = """
Instructions per Far Branch ( Far Branches apply upon
transition from application to operating system, handling
interrupts, exceptions) [lower number means higher
occurrence rate]"""


# Schedule



class Setup:
    def __init__(self, r):
        o = dict()
        n = Frontend_Bound() ; r.run(n) ; o["Frontend_Bound"] = n
        n = Fetch_Latency() ; r.run(n) ; o["Fetch_Latency"] = n
        n = ICache_Misses() ; r.run(n) ; o["ICache_Misses"] = n
        n = ITLB_Misses() ; r.run(n) ; o["ITLB_Misses"] = n
        n = Branch_Resteers() ; r.run(n) ; o["Branch_Resteers"] = n
        n = DSB_Switches() ; r.run(n) ; o["DSB_Switches"] = n
        n = LCP() ; r.run(n) ; o["LCP"] = n
        n = MS_Switches() ; r.run(n) ; o["MS_Switches"] = n
        n = Fetch_Bandwidth() ; r.run(n) ; o["Fetch_Bandwidth"] = n
        n = MITE() ; r.run(n) ; o["MITE"] = n
        n = DSB() ; r.run(n) ; o["DSB"] = n
        n = Bad_Speculation() ; r.run(n) ; o["Bad_Speculation"] = n
        n = Branch_Mispredicts() ; r.run(n) ; o["Branch_Mispredicts"] = n
        n = Machine_Clears() ; r.run(n) ; o["Machine_Clears"] = n
        n = Backend_Bound() ; r.run(n) ; o["Backend_Bound"] = n
        n = Memory_Bound() ; r.run(n) ; o["Memory_Bound"] = n
        n = L1_Bound() ; r.run(n) ; o["L1_Bound"] = n
        n = DTLB_Load() ; r.run(n) ; o["DTLB_Load"] = n
        n = Store_Fwd_Blk() ; r.run(n) ; o["Store_Fwd_Blk"] = n
        n = Lock_Latency() ; r.run(n) ; o["Lock_Latency"] = n
        n = Split_Loads() ; r.run(n) ; o["Split_Loads"] = n
        n = G4K_Aliasing() ; r.run(n) ; o["G4K_Aliasing"] = n
        n = FB_Full() ; r.run(n) ; o["FB_Full"] = n
        n = L2_Bound() ; r.run(n) ; o["L2_Bound"] = n
        n = L3_Bound() ; r.run(n) ; o["L3_Bound"] = n
        n = Contested_Accesses() ; r.run(n) ; o["Contested_Accesses"] = n
        n = Data_Sharing() ; r.run(n) ; o["Data_Sharing"] = n
        n = L3_Hit_Latency() ; r.run(n) ; o["L3_Hit_Latency"] = n
        n = SQ_Full() ; r.run(n) ; o["SQ_Full"] = n
        n = DRAM_Bound() ; r.run(n) ; o["DRAM_Bound"] = n
        n = MEM_Bandwidth() ; r.run(n) ; o["MEM_Bandwidth"] = n
        n = MEM_Latency() ; r.run(n) ; o["MEM_Latency"] = n
        n = Local_DRAM() ; r.run(n) ; o["Local_DRAM"] = n
        n = Remote_DRAM() ; r.run(n) ; o["Remote_DRAM"] = n
        n = Remote_Cache() ; r.run(n) ; o["Remote_Cache"] = n
        n = Store_Bound() ; r.run(n) ; o["Store_Bound"] = n
        n = Store_Latency() ; r.run(n) ; o["Store_Latency"] = n
        n = False_Sharing() ; r.run(n) ; o["False_Sharing"] = n
        n = Split_Stores() ; r.run(n) ; o["Split_Stores"] = n
        n = DTLB_Store() ; r.run(n) ; o["DTLB_Store"] = n
        n = Core_Bound() ; r.run(n) ; o["Core_Bound"] = n
        n = Divider() ; r.run(n) ; o["Divider"] = n
        n = Ports_Utilization() ; r.run(n) ; o["Ports_Utilization"] = n
        n = Ports_Utilized_0() ; r.run(n) ; o["Ports_Utilized_0"] = n
        n = Ports_Utilized_1() ; r.run(n) ; o["Ports_Utilized_1"] = n
        n = Ports_Utilized_2() ; r.run(n) ; o["Ports_Utilized_2"] = n
        n = Ports_Utilized_3m() ; r.run(n) ; o["Ports_Utilized_3m"] = n
        n = ALU_Op_Utilization() ; r.run(n) ; o["ALU_Op_Utilization"] = n
        n = Port_0() ; r.run(n) ; o["Port_0"] = n
        n = Port_1() ; r.run(n) ; o["Port_1"] = n
        n = Port_5() ; r.run(n) ; o["Port_5"] = n
        n = Load_Op_Utilization() ; r.run(n) ; o["Load_Op_Utilization"] = n
        n = Port_2() ; r.run(n) ; o["Port_2"] = n
        n = Port_3() ; r.run(n) ; o["Port_3"] = n
        n = Store_Op_Utilization() ; r.run(n) ; o["Store_Op_Utilization"] = n
        n = Port_4() ; r.run(n) ; o["Port_4"] = n
        n = Retiring() ; r.run(n) ; o["Retiring"] = n
        n = Light_Operations() ; r.run(n) ; o["Light_Operations"] = n
        n = FP_Arith() ; r.run(n) ; o["FP_Arith"] = n
        n = X87_Use() ; r.run(n) ; o["X87_Use"] = n
        n = FP_Scalar() ; r.run(n) ; o["FP_Scalar"] = n
        n = FP_Vector() ; r.run(n) ; o["FP_Vector"] = n
        n = Other_Light_Ops() ; r.run(n) ; o["Other_Light_Ops"] = n
        n = Heavy_Operations() ; r.run(n) ; o["Heavy_Operations"] = n
        n = Microcode_Sequencer() ; r.run(n) ; o["Microcode_Sequencer"] = n
        n = Assists() ; r.run(n) ; o["Assists"] = n

        # parents

        o["Fetch_Latency"].parent = o["Frontend_Bound"]
        o["ICache_Misses"].parent = o["Fetch_Latency"]
        o["ITLB_Misses"].parent = o["Fetch_Latency"]
        o["Branch_Resteers"].parent = o["Fetch_Latency"]
        o["DSB_Switches"].parent = o["Fetch_Latency"]
        o["LCP"].parent = o["Fetch_Latency"]
        o["MS_Switches"].parent = o["Fetch_Latency"]
        o["Fetch_Bandwidth"].parent = o["Frontend_Bound"]
        o["MITE"].parent = o["Fetch_Bandwidth"]
        o["DSB"].parent = o["Fetch_Bandwidth"]
        o["Branch_Mispredicts"].parent = o["Bad_Speculation"]
        o["Machine_Clears"].parent = o["Bad_Speculation"]
        o["Memory_Bound"].parent = o["Backend_Bound"]
        o["L1_Bound"].parent = o["Memory_Bound"]
        o["DTLB_Load"].parent = o["L1_Bound"]
        o["Store_Fwd_Blk"].parent = o["L1_Bound"]
        o["Lock_Latency"].parent = o["L1_Bound"]
        o["Split_Loads"].parent = o["L1_Bound"]
        o["G4K_Aliasing"].parent = o["L1_Bound"]
        o["FB_Full"].parent = o["L1_Bound"]
        o["L2_Bound"].parent = o["Memory_Bound"]
        o["L3_Bound"].parent = o["Memory_Bound"]
        o["Contested_Accesses"].parent = o["L3_Bound"]
        o["Data_Sharing"].parent = o["L3_Bound"]
        o["L3_Hit_Latency"].parent = o["L3_Bound"]
        o["SQ_Full"].parent = o["L3_Bound"]
        o["DRAM_Bound"].parent = o["Memory_Bound"]
        o["MEM_Bandwidth"].parent = o["DRAM_Bound"]
        o["MEM_Latency"].parent = o["DRAM_Bound"]
        o["Local_DRAM"].parent = o["MEM_Latency"]
        o["Remote_DRAM"].parent = o["MEM_Latency"]
        o["Remote_Cache"].parent = o["MEM_Latency"]
        o["Store_Bound"].parent = o["Memory_Bound"]
        o["Store_Latency"].parent = o["Store_Bound"]
        o["False_Sharing"].parent = o["Store_Bound"]
        o["Split_Stores"].parent = o["Store_Bound"]
        o["DTLB_Store"].parent = o["Store_Bound"]
        o["Core_Bound"].parent = o["Backend_Bound"]
        o["Divider"].parent = o["Core_Bound"]
        o["Ports_Utilization"].parent = o["Core_Bound"]
        o["Ports_Utilized_0"].parent = o["Ports_Utilization"]
        o["Ports_Utilized_1"].parent = o["Ports_Utilization"]
        o["Ports_Utilized_2"].parent = o["Ports_Utilization"]
        o["Ports_Utilized_3m"].parent = o["Ports_Utilization"]
        o["ALU_Op_Utilization"].parent = o["Ports_Utilized_3m"]
        o["Port_0"].parent = o["ALU_Op_Utilization"]
        o["Port_1"].parent = o["ALU_Op_Utilization"]
        o["Port_5"].parent = o["ALU_Op_Utilization"]
        o["Load_Op_Utilization"].parent = o["Ports_Utilized_3m"]
        o["Port_2"].parent = o["Load_Op_Utilization"]
        o["Port_3"].parent = o["Load_Op_Utilization"]
        o["Store_Op_Utilization"].parent = o["Ports_Utilized_3m"]
        o["Port_4"].parent = o["Store_Op_Utilization"]
        o["Light_Operations"].parent = o["Retiring"]
        o["FP_Arith"].parent = o["Light_Operations"]
        o["X87_Use"].parent = o["FP_Arith"]
        o["FP_Scalar"].parent = o["FP_Arith"]
        o["FP_Vector"].parent = o["FP_Arith"]
        o["Other_Light_Ops"].parent = o["Light_Operations"]
        o["Heavy_Operations"].parent = o["Retiring"]
        o["Microcode_Sequencer"].parent = o["Heavy_Operations"]
        o["Assists"].parent = o["Microcode_Sequencer"]

        # user visible metrics

        n = Metric_IPC() ; r.metric(n) ; o["IPC"] = n
        n = Metric_UPI() ; r.metric(n) ; o["UPI"] = n
        n = Metric_IpTB() ; r.metric(n) ; o["IpTB"] = n
        n = Metric_CPI() ; r.metric(n) ; o["CPI"] = n
        n = Metric_CLKS() ; r.metric(n) ; o["CLKS"] = n
        n = Metric_SLOTS() ; r.metric(n) ; o["SLOTS"] = n
        n = Metric_CoreIPC() ; r.metric(n) ; o["CoreIPC"] = n
        n = Metric_FLOPc() ; r.metric(n) ; o["FLOPc"] = n
        n = Metric_ILP() ; r.metric(n) ; o["ILP"] = n
        n = Metric_IpMispredict() ; r.metric(n) ; o["IpMispredict"] = n
        n = Metric_CORE_CLKS() ; r.metric(n) ; o["CORE_CLKS"] = n
        n = Metric_IpLoad() ; r.metric(n) ; o["IpLoad"] = n
        n = Metric_IpStore() ; r.metric(n) ; o["IpStore"] = n
        n = Metric_IpBranch() ; r.metric(n) ; o["IpBranch"] = n
        n = Metric_IpCall() ; r.metric(n) ; o["IpCall"] = n
        n = Metric_BpTkBranch() ; r.metric(n) ; o["BpTkBranch"] = n
        n = Metric_IpArith() ; r.metric(n) ; o["IpArith"] = n
        n = Metric_Instructions() ; r.metric(n) ; o["Instructions"] = n
        n = Metric_DSB_Coverage() ; r.metric(n) ; o["DSB_Coverage"] = n
        n = Metric_Load_Miss_Real_Latency() ; r.metric(n) ; o["Load_Miss_Real_Latency"] = n
        n = Metric_MLP() ; r.metric(n) ; o["MLP"] = n
        n = Metric_Page_Walks_Utilization() ; r.metric(n) ; o["Page_Walks_Utilization"] = n
        n = Metric_L1D_Cache_Fill_BW() ; r.metric(n) ; o["L1D_Cache_Fill_BW"] = n
        n = Metric_L2_Cache_Fill_BW() ; r.metric(n) ; o["L2_Cache_Fill_BW"] = n
        n = Metric_L3_Cache_Fill_BW() ; r.metric(n) ; o["L3_Cache_Fill_BW"] = n
        n = Metric_L1MPKI() ; r.metric(n) ; o["L1MPKI"] = n
        n = Metric_L2MPKI() ; r.metric(n) ; o["L2MPKI"] = n
        n = Metric_L3MPKI() ; r.metric(n) ; o["L3MPKI"] = n
        n = Metric_CPU_Utilization() ; r.metric(n) ; o["CPU_Utilization"] = n
        n = Metric_Average_Frequency() ; r.metric(n) ; o["Average_Frequency"] = n
        n = Metric_GFLOPs() ; r.metric(n) ; o["GFLOPs"] = n
        n = Metric_Turbo_Utilization() ; r.metric(n) ; o["Turbo_Utilization"] = n
        n = Metric_SMT_2T_Utilization() ; r.metric(n) ; o["SMT_2T_Utilization"] = n
        n = Metric_Kernel_Utilization() ; r.metric(n) ; o["Kernel_Utilization"] = n
        n = Metric_DRAM_BW_Use() ; r.metric(n) ; o["DRAM_BW_Use"] = n
        n = Metric_MEM_Read_Latency() ; r.metric(n) ; o["MEM_Read_Latency"] = n
        n = Metric_MEM_Parallel_Reads() ; r.metric(n) ; o["MEM_Parallel_Reads"] = n
        n = Metric_Time() ; r.metric(n) ; o["Time"] = n
        n = Metric_Socket_CLKS() ; r.metric(n) ; o["Socket_CLKS"] = n
        n = Metric_IpFarBranch() ; r.metric(n) ; o["IpFarBranch"] = n

        # references between groups

        o["ICache_Misses"].ITLB_Misses = o["ITLB_Misses"]
        o["Fetch_Bandwidth"].Frontend_Bound = o["Frontend_Bound"]
        o["Fetch_Bandwidth"].Fetch_Latency = o["Fetch_Latency"]
        o["Branch_Mispredicts"].Bad_Speculation = o["Bad_Speculation"]
        o["Machine_Clears"].Bad_Speculation = o["Bad_Speculation"]
        o["Machine_Clears"].Branch_Mispredicts = o["Branch_Mispredicts"]
        o["Backend_Bound"].Retiring = o["Retiring"]
        o["Backend_Bound"].Bad_Speculation = o["Bad_Speculation"]
        o["Backend_Bound"].Frontend_Bound = o["Frontend_Bound"]
        o["Memory_Bound"].Retiring = o["Retiring"]
        o["Memory_Bound"].Bad_Speculation = o["Bad_Speculation"]
        o["Memory_Bound"].Frontend_Bound = o["Frontend_Bound"]
        o["Memory_Bound"].Backend_Bound = o["Backend_Bound"]
        o["Memory_Bound"].Fetch_Latency = o["Fetch_Latency"]
        o["MEM_Latency"].MEM_Bandwidth = o["MEM_Bandwidth"]
        o["Core_Bound"].Retiring = o["Retiring"]
        o["Core_Bound"].Frontend_Bound = o["Frontend_Bound"]
        o["Core_Bound"].Memory_Bound = o["Memory_Bound"]
        o["Core_Bound"].Backend_Bound = o["Backend_Bound"]
        o["Core_Bound"].Bad_Speculation = o["Bad_Speculation"]
        o["Core_Bound"].Fetch_Latency = o["Fetch_Latency"]
        o["Ports_Utilization"].Fetch_Latency = o["Fetch_Latency"]
        o["Ports_Utilized_0"].Fetch_Latency = o["Fetch_Latency"]
        o["Retiring"].Heavy_Operations = o["Heavy_Operations"]
        o["Light_Operations"].Retiring = o["Retiring"]
        o["Light_Operations"].Heavy_Operations = o["Heavy_Operations"]
        o["Light_Operations"].Microcode_Sequencer = o["Microcode_Sequencer"]
        o["FP_Arith"].FP_Scalar = o["FP_Scalar"]
        o["FP_Arith"].X87_Use = o["X87_Use"]
        o["FP_Arith"].FP_Vector = o["FP_Vector"]
        o["Other_Light_Ops"].FP_Arith = o["FP_Arith"]
        o["Other_Light_Ops"].X87_Use = o["X87_Use"]
        o["Other_Light_Ops"].FP_Scalar = o["FP_Scalar"]
        o["Other_Light_Ops"].FP_Vector = o["FP_Vector"]
        o["Heavy_Operations"].Microcode_Sequencer = o["Microcode_Sequencer"]
        o["IpArith"].FP_Vector = o["FP_Vector"]
        o["IpArith"].FP_Scalar = o["FP_Scalar"]

        # siblings cross-tree

        o["MS_Switches"].sibling = (o["Microcode_Sequencer"],)
        o["L1_Bound"].sibling = (o["Ports_Utilized_1"],)
        o["Lock_Latency"].sibling = (o["Store_Latency"],)
        o["FB_Full"].sibling = (o["SQ_Full"], o["MEM_Bandwidth"], o["Store_Latency"],)
        o["Contested_Accesses"].sibling = (o["Data_Sharing"], o["Remote_Cache"], o["False_Sharing"],)
        o["Data_Sharing"].sibling = (o["Contested_Accesses"], o["Remote_Cache"], o["False_Sharing"],)
        o["L3_Hit_Latency"].sibling = (o["MEM_Latency"],)
        o["L3_Hit_Latency"].overlap = True
        o["SQ_Full"].sibling = (o["FB_Full"], o["MEM_Bandwidth"],)
        o["MEM_Bandwidth"].sibling = (o["FB_Full"], o["SQ_Full"],)
        o["MEM_Latency"].sibling = (o["L3_Hit_Latency"],)
        o["Remote_Cache"].sibling = (o["Contested_Accesses"], o["Data_Sharing"], o["False_Sharing"],)
        o["Store_Latency"].sibling = (o["Lock_Latency"], o["FB_Full"],)
        o["Store_Latency"].overlap = True
        o["False_Sharing"].sibling = (o["Contested_Accesses"], o["Data_Sharing"], o["Remote_Cache"],)
        o["Split_Stores"].sibling = (o["Port_4"],)
        o["Ports_Utilized_1"].sibling = (o["L1_Bound"],)
        o["Ports_Utilized_2"].sibling = (o["Port_0"], o["Port_1"], o["Port_5"],)
        o["Port_0"].sibling = (o["Ports_Utilized_2"], o["Port_1"], o["Port_5"],)
        o["Port_1"].sibling = (o["Ports_Utilized_2"], o["Port_0"], o["Port_5"],)
        o["Port_5"].sibling = (o["Ports_Utilized_2"], o["Port_0"], o["Port_1"],)
        o["Port_4"].sibling = (o["Split_Stores"],)
        o["Microcode_Sequencer"].sibling = (o["MS_Switches"],)
        o["IpTB"].sibling = (o["Fetch_Bandwidth"],)
        o["IpMispredict"].sibling = (o["Branch_Mispredicts"],)
        o["DRAM_BW_Use"].sibling = (o["FB_Full"], o["SQ_Full"], o["MEM_Bandwidth"],)