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GPU-BLOB: GPU BLas Offload Benchmark

DOI

GPU-BLOB is a benchmark tool which can be used to determine at what point (i.e. problem size) it is worthwhile to offload select BLAS computations to the GPU on a heterogeneous system. Not only can this aid to help programmers understand the characteristics of the hardware they are optimising for, but also whether or not it would be useful for them to utilise the GPU at all for their specific application.

For each supported BLAS kernel (listed below) GPU-BLOB will run n iterations of each kernel on CPU and GPU, gradually increasing the problem size up to a user-defined maximum limit. The resulting large amount of performance data collected is output in multiple csv files in the CSV_Results directory; one for each BLAS kernel and probelm-type pair.

Each BLAS kernel is tested with a range of different problem size designs in an attempt to capture the performance differences that can occur between different problem sets when utilising the same underlying kernel. For each BLAS kernel and problem type pair, a table will be displayed which outlines the minimum problem size at which offloading to the GPU became worthwhile for all larger problem sizes. If for the number of iterations and maximum problem dimension this offload threshold cannot be found, the table will show 0 for each problem dimension and N/A for the GPU and CPU GFLOP/s.

All computations performed by each BLAS library are done in column-major and are assumed to be functionally correct. However, a simple checksum is calculated after each CPU and GPU run for each problem size to ensure all utilised libraries are computing the same result.
Only when an error occurs will any checksum be displayed to the user.

GFLOP/s are calculated using the following Total FLOPs formulas. The compute time excludes any initialisation, but does include any data movement / prefetching to/from the GPU device:

  • GEMM : FLOPs = (2 * M * N * K) + (b * M * N) where b is 1 if BETA=0 and 3 if BETA=/=0
  • GEMV : FLOPs = (2 * M * N) + (b * M) where b is 1 if BETA=0 and 3 if BETA=/=0

Build Options

Select the compiler you wish to use. Regardless of choice, gcc is required in order to build the Consume.so external library.

make COMPILER=GNU

The supported compiler names are: ARM, CLANG, GNU, INTEL, NVIDIA, HIP with the default option being GNU.\ These compiler choices correspond to:

  • ARM --> armclang++
  • CLANG --> clang++
  • GNU --> g++
  • INTEL --> icpx (Intel's oneAPI DPC++/C++ Compiler)
  • NVIDIA --> nvc++
  • HIP --> hipcc

CPU BLAS Library

Specify which CPU BLAS Library you are using:

make COMPILER=GNU CPU_LIB=ARMPL

The supported Libraries are as follows:

  • Arm Performance Libraries : ARMPL
  • Intel OneMKL : ONEMKL
    • May require the use of an additional MKLROOT make option specifying the root directory of the oneMKL Library.
  • AMD Optimized Compute Libraries : AOCL
  • NVIDIA Performance Libraries : NVPL
  • OpenBLAS : OPENBLAS

If no library is selected then no CPU BLAS kernels will be executed.

GPU BLAS Library

Specify which GPU BLAS Library you are using:

make COMPILER=GNU CPU_LIB=ARMPL GPU_LIB=CUBLAS

The supported Libraries are as follows:

  • NVIDIA cuBLAS : CUBLAS
  • Intel OneMKL : ONEMKL
    • May require the use of an additional MKLROOT make option specifying the root directory of the oneMKL Library.
  • AMD rocBLAS : ROCBLAS

If no library is selected then no GPU BLAS kernels will be executed.

Additional Flags

Some combinations of BLAS Libraries and compilers will require additional flags. Many of these have been pre-configured in the Makefile, but some require the inclusion of additional shared objects etc. These can be passed to the Makefile using CXXFLAGS=:

make COMPILER=GNU CPU_LIB=ARMPL GPU_LIB=CUBLAS CXXFLAGS="-I/path/to/include -L/path/to/lib -Wl,-rpath,/path/to/lib"

Running

The benchmark takes the following runtime arguments:

./gpu-blob --iterations I --start_dimension S --dimension_limit D
        OR
./gpu-blob -i I -s S -d D

Where I (default of 10) specifies how many iterations each kernel will run, S (default of 1) is the first problem dimension tested, and D (default of 128) specifies the the upper limit for the largest dimention in a problem size.
Example: For a square GEMM, the problem size will iterate from M=N=K=S, up to M=N=K=D.
Example: For a rectangular GEMM where M=N and K=4*M, the probelm size will iterate from M=N=S and K=S*4 up toM=N=D and K=D*4.

Additional arguments are as follows:

  • --no_cpu : disables the CPU kernels from executing at runtime
  • --no_gpu : disables the GPU kernels from executing at runtime

Environment Variables

It is recommended to set the relevant environment variables to ensure the best performance on host and device. For more information about extrating the best library performance at runtime (especially for CPU libraries), please refer to the appropriate specifications. Seen below are some suggestions on commonly used environment variables for their associated libraries.

Arm Performance Libraries

When using ArmPL, setting the following environment variables is beneficial:

  • OMP_NUM_THREADS -- Setting to the core count of the host CPU should ensure the best performance
  • OMP_PROC_BIND
  • OMP_PLACES

Intel OnMKL

When using oneMKL as the CPU BLAS Library, setting the following environment variables is beneficial:

  • OMP_NUM_THREADS -- Setting to the core count of the host CPU should ensure the best performance
  • OMP_PROC_BIND
  • OMP_PLACES

AMD Optimizing CPU libraries

When using AOCL, setting the following environment variables is beneficial:

  • BLIS_NUM_THREADS -- Setting to the core count of the host CPU should ensure the best performance
  • OMP_PROC_BIND
  • OMP_PLACES

NVIDIA Performance Libraries

When using NVPL as the CPU BLAS Library, setting the following environment variables is beneficial:

  • OMP_NUM_THREADS -- Setting to the core count of the host CPU should ensure the best performance
  • OMP_PROC_BIND
  • OMP_PLACES

OpenBLAS

When using NVPL as the CPU BLAS Library, setting the following environment variables is beneficial:

  • OMP_NUM_THREADS -- Setting to the core count of the host CPU should ensure the best performance
  • OMP_PROC_BIND
  • OMP_PLACES

BLAS Kernels Supported

The kernels listed below are computed by the benchmark for a wide range of problem sizes and shapes.

Level 3 BLAS

  • GEMM
    • FP32, FP64
    • Square, short-&-wide, tall-&-thin input sizes

Level 2 BLAS

  • GEMV
    • FP32, FP64
    • Square, short-&-wide, tall-&-thin input sizes

Auxiliary Files

Additional to the main benchmark, there are two auxiliary python scripts which perform the following:

  • createGflopsGraphs.py : Will read all default filenames from CSV_Results and create a CPU, GPU, or GPU-GPU graph as appropriate. A custom CSV directory can be passed as a runtime argument.
  • calculateOffloadThresholds.py : Takes two mandatory runtime arguments; the first being a CPU-only CSV file, the second being a GPU-only CSV file. This will calculate and print the offload threshold table akin to GPU-BLOB.

Future Work

  • Add support for Sparce Kernels
  • Add FP16/BF16 support for kernels
  • Add batched GEMM functions
  • Add support for Apple Accelerate
  • Add support for Apple Metal Performance Shaders