ECG Benchmark Suite

Graph Processing Framework with SniperSIM/GEM5/OpenMP

Overview

ECG builds on OpenGraph by integrating it with simulators like SNIPER/GEM5. It is an open source graph processing framework, designed as a modular benchmarking suite for graph processing algorithms. It provides an end to end evaluation infrastructure which includes the preprocessing stage (forming the graph structure) and the graph algorithm. The OpenMP part of ECG has been developed on Ubuntu 20.04, with PowerPC/Intel architecture taken into account. ECG is coded using C giving the researcher full flexibility with modifying data structures and other algorithmic optimizations.

Presentations that explains end-to-end graph processing (implementation is inspired from these sources)
- Preprocessing two steps (third one is optional) :
  1. [Sorting the edge-list], using count-sort or radix-sort.
  2. [Building the graph structure]. CSR, Gird, Adjacency-Linked-List, and Adjacency-Array-List.
    - Ref: Xiaowei Zhu, Wentao Han and Wenguang Chen. GridGraph: Large-Scale Graph Processing on a Single Machine Using 2-Level Hierarchical Partitioning. Proceedings of the 2015 USENIX Annual Technical Conference, pages 375-386.
    - Ref: Malicevic, Jasmina, Baptiste Lepers, and Willy Zwaenepoel. "Everything you always wanted to know about multicore graph processing but were afraid to ask." 2017 USENIX Annual Technical Conference. Proceedings of the 2015 USENIX Annual Technical Conference, pages 375-386.
  3. [Relabeling the graph], this step achieves better cache locality (better performance) with preprocessing overhead.
    - Ref: J. Arai, H. Shiokawa, T. Yamamuro, M. Onizuka, and S. Iwamura. Rabbit Order: Just-in-time Parallel Reordering for Fast Graph Analysis. IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2016.
    - Ref:P. Faldu and J. Diamond and B. Grot, "A Closer Look at Lightweight Graph Reordering," in Proceedings of the International Symposium on Workload Characterization (IISWC), November 2019.
- Graph Algorithm step depends on the direction of the data (Push/Pull):
  1. [BFS example], although it doesn't show direction optimized. But we discusses the Push and Pull approach separately.
    - [Ref]: Scott Beamer, Krste Asanović, David Patterson. The GAP Benchmark Suite. arXiv:1508.03619 [cs.DC], 2015.
  2. [Page-Rank (PR) example]: Discussing PR cache behavior.
    - Ref: J. Arai, H. Shiokawa, T. Yamamuro, M. Onizuka, and S. Iwamura. Rabbit Order: Just-in-time Parallel Reordering for Fast Graph Analysis. IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2016.

Installation and Dependencies

CPU Mode

OpenMP

Judy Arrays

open@graph:~$ sudo apt-get install libjudy-dev

OpenMP is already a feature of the compiler, so this step is not necessary.

open@graph:~$ sudo apt-get install libomp-dev

Setting up the source code

Clone ECG.

open@graph:~$ git clone https://github.com/atmughrabi/ECG.git

From the home directory go to the ECG directory:

open@graph:~$ cd ECG/

Make the source code

open@graph:~ECG$ make

Simulation Mode

Simple Trace Cache simulator

No setup needed, cache simulator is included within the code. And highlighted in the code with: (Algorithms Supported)

#ifdef CACHE_HARNESS_META
  //Simple Cache structures
#endif

OR

#ifdef CACHE_HARNESS
  //Simple Cache function calls
#endif

The Sniper Multi-Core Simulator

Sniper simulator is needed. And highlighted in the code with: (Algorithms Supported)

#ifdef SNIPER_HARNESS
  //Sniper ROI function call
#endif

Obtain The Sniper Multi-Core Simulator for their website, (SniperSim).
Follow the steps for setting up and patching sniper to function with the correct the compiler, and ROI support.
Copy the sniper simulator to ECG/00_graph_bench

open@graph:~ECG$ mkdir -p 00_graph_bench/sniper
open@graph:~ECG$ cp ORIGINAL_SNIPERSIM_SOURCE 00_graph_bench/sniper

go to 00_graph_bench/sniper and build

open@graph:~ECG$ cd 00_graph_bench/sniper
open@graph:~ECG/00_graph_bench/sniper$ make

go to the root directory to ECG now you can run sniper with OpenGraph benchmarks

open@graph:~ECG/00_graph_bench/sniper$ cd ../..
open@graph:~ECG$ make run-sniper

gem5 simulator

Coming soon

Running ECG

CPU Mode

Initial compilation for the Graph framework with OpenMP

The default compilation is openmp mode:

open@graph:~ECG$ make

From the root directory you can modify the Makefile with the (parameters) you need for OpenMP:

open@graph:~ECG$ make run

OR

open@graph:~ECG$ make run-openmp

You can pass parameters modifying Makefile parameters (easiest way) - cross reference with (parameters) to pass the correct values.

PARAMETER	FUNCTION
ARGS	arguments passed to open-graph

PARAMETER	FUNCTION
Graph Files Directory
FILE_BIN	graph edge-list location
FILE_LABEL	graph edge-list reorder list

PARAMETER	FUNCTION
Graph Structures PreProcessing
SORT_TYPE	graph edge-list sort (count/radix)
DATA_STRUCTURES	CSR,GRID,LinkedList,ArrayList
REORDER_LAYER1	Reorder graph for cache optimization

PARAMETER	FUNCTION
Algorithms General
ALGORITHMS	BFS, PR, DFS, etc
PULL_PUSH	Direction push,pull,hybrid

PARAMETER	FUNCTION
Algorithms Specific
ROOT	source node for BFS, etc
TOLERANCE	PR tolerance for convergence
NUM_ITERATIONS	PR iterations or convergence
DELTA	SSSP delta step

PARAMETER	FUNCTION
General Performance
NUM_THREADS_PRE	number of threads for the preprocess step (graph sorting, generation)
NUM_THREADS_ALGO	number of threads for the algorithm step (BFS,PR, etc)
NUM_THREADS_KER	(Optional) number of threads for the algorithm kernel (BFS,PR, etc)
NUM_TRIALS	number of trials for the same algorithms

Simulation Mode

Simple trace-driven Cache

From the root directory you can modify the Makefile with the (parameters) you need for trace cache:

open@graph:~ECG$ make clean; make run-cache

These arguments are not passed through the Args-list you need to modify from ECG/00_graph_bench/include/cache/cache.h :

//ECG/00_graph_bench/src/main/open-graph.c
#ifdef CACHE_HARNESS_META
    arguments->l1_size   = L1_SIZE;
    arguments->l1_assoc  = L1_ASSOC;
    arguments->blocksize = BLOCKSIZE;
    arguments->policey   = POLICY;
#endif

The Sniper Multi-Core Simulator

From the root directory you can modify the Makefile with the (parameters) you need for sniper:

open@graph:~ECG$ make clean; make run-sniper

Simulation results are output to ECG/00_graph_bench/sniper-results
To clean simulation stats

open@graph:~ECG$ make clean-stats

You can pass parameters modifying Makefile parameters (easiest way) - cross reference with (SniperSim) to pass the correct values.

PARAMETER	FUNCTION
SNIPER_ARGS	arguments passed to sniper simulator

gem5 Simulator

Coming soon

Graph structure Input (Edge list)

If you open the Makefile you will see the convention for graph directories : BENCHMARKS_DIR/GRAPH_NAME/graph.wbin.
.bin stands to unweighted edge list, .wbin stands for wighted, In binary format. (This is only a convention you don't have to use it)
The reason behind converting the edge-list from text to binary, it is simply takes less space on the drive for large graphs, and easier to use with the mmap function.

Source	Dest	Weight (Optional)
30	3	1
3	4	1

Example:
INPUT: (unweighted textual edge-list)
../BENCHMARKS_DIR/GRAPH_NAME/graph

convert to binary format and add random weights, for this example all the weights are 1.
--graph-file-format is the type of graph you are reading, --convert-format is the type of format you are converting to.
NOTE: you can read the file from text format without the convert step. By adding --graph-file-format 0 to the argument list. The default is 1 assuming it is binary. please check --help for better explanation.
--stats is a flag that enables conversion. It used also for collecting stats about the graph (but this feature is on hold for now).
(unweighted graph)

open@graph:~ECG/00_graph_bench$ make convert

OR (weighted graph)

open@graph:~ECG/00_graph_bench$ make convert-w

OR (weighted graph)

open@graph:~ECG/00_graph_bench$ ./bin/open-graph-openmp  --generate-weights --stats --graph-file-format=0 --convert-format=1 --graph-file=../BENCHMARKS_DIR/GRAPH_NAME/graph

Makefile parameters

PARAMETER	FUNCTION
File Formats
FILE_FORMAT	the type of graph read
CONVERT_FORMAT	the type of graph converted

OUTPUT: (weighted binary edge-list)
../BENCHMARKS_DIR/GRAPH_NAME/graph.wbin

1e00 0000 0300 0000 0100 0000 0300 0000
0400 0000 0100 0000 1900 0000 0500 0000
0100 0000 1900 0000 0700 0000 0100 0000
0600 0000 0300 0000 0100 0000 0400 0000
0200 0000 0100 0000 0600 0000 0c00 0000
0100 0000 0600 0000 0800 0000 0100 0000
0600 0000 0b00 0000 0100 0000 0800 0000
1600 0000 0100 0000 0900 0000 1b00 0000
0100 0000

Graph Structure Preprocessing:

ECG can handle multiple representations of the graph structure in memory, each has their own theoretical benefits and shortcomings.

Regular unsorted Edge-list as input.

CSR (Compressed Sparse Row)

Grid

Array-List

Linked-List

ECG Options

Usage: open-graph-openmp [OPTION...]
            -f <graph file> -d [data structure] -a [algorithm] -r [root] -n
            [num threads] [-h -c -s -w]

ECG is an open source graph processing framework, it is designed to be a
benchmarking suite for various graph processing algorithms using pure C.

   -a, --algorithm=[DEFAULT:[0]-BFS]
                             [0]-BFS, [1]-Page-rank, [2]-SSSP-DeltaStepping,
                             [3]-SSSP-BellmanFord, [4]-DFS,[5]-SPMV,
                             [6]-Connected-Components,
                             [7]-Betweenness-Centrality, [8]-Triangle Counting,
                             [9-BUGGY]-IncrementalAggregation.

  -b, --delta=[DEFAULT:1]    SSSP Delta value [Default:1].

  -c, --convert-format=[DEFAULT:[1]-binary-edgeList]
                             [serialize flag must be on --serialize to write]
                             Serialize graph text format (edge list format) to
                             binary graph file on load example:-f <graph file>
                             -c this is specifically useful if you have Graph
                             CSR/Grid structure and want to save in a binary
                             file format to skip the preprocessing step for
                             future runs. [0]-text-edgeList [1]-binary-edgeList
                             [2]-graphCSR-binary.

  -d, --data-structure=[DEFAULT:[0]-CSR]
                             [0]-CSR, [1]-Grid, [2]-Adj LinkedList, [3]-Adj
                             ArrayList [4-5] same order bitmap frontiers.

  -e, --tolerance=[EPSILON:0.0001]
                             Tolerance value of for page rank
                             [default:0.0001].

  -f, --graph-file=<FILE>    Edge list represents the graph binary format to
                             run the algorithm textual format change
                             graph-file-format.

  -F, --labels-file=<FILE>   Read and reorder vertex labels from a text file,
                             Specify the file name for the new graph reorder,
                             generated from Gorder, Rabbit-order, etc.
  -g, --bin-size=[SIZE:512]  You bin vertices's histogram according to this
                             parameter, if you have a large graph you want to
                             illustrate.

  -i, --num-iterations=[DEFAULT:20]
                             Number of iterations for page rank to converge
                             [default:20] SSSP-BellmanFord [default:V-1].

  -j, --verbosity=[DEFAULT:[0:no stats output]
                             For now it controls the output of .perf file and
                             PageRank .stats (needs --stats enabled)
                             filesPageRank .stat [1:top-k results] [2:top-k
                             results and top-k ranked vertices listed.

  -k, --remove-duplicate     Removers duplicate edges and self loops from the
                             graph.

  -K, --Kernel-num-threads=[DEFAULT:algo-num-threads]
                             Number of threads for graph processing kernel
                             (critical-path) (graph algorithm)

  -l, --light-reorder-l1=[DEFAULT:[0]-no-reordering]
                             Relabels the graph for better cache performance
                             (first layer). [0]-no-reordering [1]-out-degree
                             [2]-in-degree [3]-(in+out)-degree [4]-DBG-out
                             [5]-DBG-in [6]-HUBSort-out [7]-HUBSort-in
                             [8]-HUBCluster-out [9]-HUBCluster-in
                             [10]-(random)-degree  [11]-LoadFromFile

  -L, --light-reorder-l2=[DEFAULT:[0]-no-reordering]
                             Relabels the graph for better cache performance
                             (second layer). [0]-no-reordering [1]-out-degree
                             [2]-in-degree [3]-(in+out)-degree [4]-DBG-out
                             [5]-DBG-in [6]-HUBSort-out [7]-HUBSort-in
                             [8]-HUBCluster-out [9]-HUBCluster-in
                             [10]-(random)-degree  [11]-LoadFromFile

  -M, --mask-mode=[DEFAULT:[0:disabled]]
                             Encodes [0:disabled] the last two bits of
                             [1:out-degree]-Edgelist-labels
                             [2:in-degree]-Edgelist-labels or
                             [3:out-degree]-vertex-property-data
                             [4:in-degree]-vertex-property-data with hot/cold
                             hints [11:HOT]|[10:WARM]|[01:LUKEWARM]|[00:COLD]
                             to specialize caching. The algorithm needs to
                             support value unmask to work.

  -n, --pre-num-threads=[DEFAULT:MAX]
                             Number of threads for preprocessing (graph
                             structure) step 

  -N, --algo-num-threads=[DEFAULT:MAX]
                             Number of threads for graph processing (graph
                             algorithm)

  -o, --sort=[DEFAULT:[0]-radix-src]
                             [0]-radix-src [1]-radix-src-dest [2]-count-src
                             [3]-count-src-dst.

  -O, --light-reorder-l3=[DEFAULT:[0]-no-reordering]
                             Relabels the graph for better cache performance
                             (third layer). [0]-no-reordering [1]-out-degree
                             [2]-in-degree [3]-(in+out)-degree [4]-DBG-out
                             [5]-DBG-in [6]-HUBSort-out [7]-HUBSort-in
                             [8]-HUBCluster-out [9]-HUBCluster-in
                             [10]-(random)-degree  [11]-LoadFromFile

  -p, --direction=[DEFAULT:[0]-PULL]
                             [0]-PULL, [1]-PUSH,[2]-HYBRID. NOTE: Please
                             consult the function switch table for each
                             algorithm.

  -r, --root=[DEFAULT:0]     BFS, DFS, SSSP root

  -s, --symmetrize           Symmetric graph, create a set of incoming edges.

  -S, --stats                Write algorithm stats to file. same directory as
                             the graph.PageRank: Dumps top-k ranks matching
                             using QPR similarity metrics.

  -t, --num-trials=[DEFAULT:[1 Trial]]
                             Number of trials for whole run (graph algorithm
                             run) [default:1].

  -w, --generate-weights     Load or Generate weights. Check ->graphConfig.h
                             #define WEIGHTED 1 beforehand then recompile using
                             this option.

  -x, --serialize            Enable file conversion/serialization use with
                             --convert-format.

  -z, --graph-file-format=[DEFAULT:[1]-binary-edgeList]
                             Specify file format to be read, is it textual edge
                             list, or a binary file edge list. This is
                             specifically useful if you have Graph CSR/Grid
                             structure already saved in a binary file format to
                             skip the preprocessing step. [0]-text edgeList
                             [1]-binary edgeList [2]-graphCSR binary.

  -?, --help                 Give this help list
      --usage                Give a short usage message
  -V, --version              Print program version

Organization

00_graph_bench
- include - Major function headers
  - graphalgorithms - supported Graph algorithms
    - openmp - OpenMP integration
      - BFS.h - Breadth First Search
      - DFS.h - Depth First Search
      - SSSP.h - Single Source Shortest Path
      - bellmanFord.h - Single Source Shortest Path using Bellman Ford
      - incrementalAgreggation.h - Incremental Aggregation for clustering
      - pageRank.h - Page Rank Algorithm
      - SPMV.h - Sparse Matrix Vector Multiplication
  - preprocessing - preprocessing graph structure
    - countsort.h - sort edge list using count sort
    - radixsort.h - sort edge list using radix sort
    - reorder.h - cluster reorder the graph for better cache locality
    - sortRun.h - chose which sorting algorithm to use
  - structures - structures that hold the graph in memory
    - graphAdjArrayList.h - graph using adjacency list array with arrays
    - graphAdjLinkeList.h - graph using adjacency list array with linked lists
    - graphCSR.h - graph using compressed sparse matrix
    - graphGrid.h - graph using Grid
- src - Major function Source files
  - graphalgorithms - supported Graph algorithms
    - openmp - OpenMP integration
      - BFS.c - Breadth First Search
      - DFS.c - Depth First Search
      - SSSP.c - Single Source Shortest Path
      - bellmanFord.c - Single Source Shortest Path using Bellman Ford
      - incrementalAgreggation.c - Incremental Aggregation for clustering
      - pageRank.c - Page Rank Algorithm
      - SPMV.c - Sparse Matrix Vector Multiplication
  - preprocessing - preprocessing graph structure
    - countsort.c - sort edge list using count sort
    - radixsort.c - sort edge list using radix sort
    - reorder.c - cluster reorder the graph for better cache locality
    - sortRun.c - chose which sorting algorithm to use
  - structures - structures that hold the graph in memory
    - graphAdjArrayList.c - graph using adjacency list array with arrays
    - graphAdjLinkeList.c - graph using adjacency list array with linked lists
    - graphCSR.c - graph using compressed sparse matrix
    - graphGrid.c - graph using Grid
Makefile - Global makefile

Tasks TODO CSR Graphs Only:

Report bugs to:

Name		Name	Last commit message	Last commit date
Latest commit History 53 Commits
00_graph_bench		00_graph_bench
01_test_graphs		01_test_graphs
02_slides		02_slides
.gitignore		.gitignore
.travis.yml		.travis.yml
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md

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ECG Benchmark Suite

Graph Processing Framework with SniperSIM/GEM5/OpenMP

Overview

Installation and Dependencies

CPU Mode

OpenMP

Setting up the source code

Simulation Mode

Simple Trace Cache simulator

The Sniper Multi-Core Simulator

gem5 simulator

Running ECG

CPU Mode

Initial compilation for the Graph framework with OpenMP

Simulation Mode

Simple trace-driven Cache

The Sniper Multi-Core Simulator

gem5 Simulator

Graph structure Input (Edge list)

Graph Structure Preprocessing:

Regular unsorted Edge-list as input.

CSR (Compressed Sparse Row)

Grid

Array-List

Linked-List

ECG Options

Organization

Tasks TODO CSR Graphs Only:

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ECG Benchmark Suite

Graph Processing Framework with SniperSIM/GEM5/OpenMP

Overview

Installation and Dependencies

CPU Mode

OpenMP

Setting up the source code

Simulation Mode

Simple Trace Cache simulator

The Sniper Multi-Core Simulator

gem5 simulator

Running ECG

CPU Mode

Initial compilation for the Graph framework with OpenMP

Simulation Mode

Simple trace-driven Cache

The Sniper Multi-Core Simulator

gem5 Simulator

Graph structure Input (Edge list)

Graph Structure Preprocessing:

Regular unsorted Edge-list as input.

CSR (Compressed Sparse Row)

Grid

Array-List

Linked-List

ECG Options

Organization

Tasks TODO CSR Graphs Only:

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