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Matrix Multiply Stress Test

Prerequisites

Building

git clone [email protected]:AMD-HPC/CoralGemm.git
cd CoralGemm
mkdir build
cd build
cmake ..
make -j

Need be, set CMAKE_MODULE_PATH and CMAKE_PREFIX_PATH, e.g.:

export CMAKE_MODULE_PATH=/opt/rocm/hip/cmake:${CMAKE_MODULE_PATH}
export CMAKE_PREFIX_PATH=/opt/rocm/lib/cmake:${CMAKE_PREFIX_PATH}

By default CoralGemm is built for AMD GPUs using ROCm.
However, it can also be built for NVIDIA GPUs using CUDA.
To do so, set USE_HIP=OFF, USE_CUDA=ON, and set CMAKE_CUDA_ARCHITECTURES, e.g.:

cmake -DUSE_HIP=OFF -DUSE_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=90 ..

Common Cases

DGEMM

  • 16 GB devices (Radeon VII): ./gemm R_64F R_64F R_64F R_64F OP_N OP_T 8640 8640 8640 8640 8640 8640 9 300
  • 32 GB devices (MI60, MI100): ./gemm R_64F R_64F R_64F R_64F OP_N OP_T 8640 8640 8640 8640 8640 8640 18 300
  • 64 GB devices (MI200 series): ./gemm R_64F R_64F R_64F R_64F OP_N OP_T 8640 8640 8640 8640 8640 8640 36 300

SGEMM

  • 16 GB devices (Radeon VII): ./gemm R_32F R_32F R_32F R_32F OP_N OP_T 8640 8640 8640 8640 8640 8640 18 300
  • 32 GB devices (MI60, MI100): ./gemm R_32F R_32F R_32F R_32F OP_N OP_T 8640 8640 8640 8640 8640 8640 36 300
  • 64 GB devices (MI200 series): ./gemm R_32F R_32F R_32F R_32F OP_N OP_T 8640 8640 8640 8640 8640 8640 72 300

Mixed-Precision

Mixed-precision GEMMs are provided by the Ex API.
Supply the "ex" command line option to use the Ex API.

To run half-precision (FP16) GEMM with accumulation to FP32 on the MI200 series devices call, e.g.:
./gemm R_16F R_16F R_32F R_32F OP_N OP_T 8640 8640 8640 8640 8640 8640 50 300 ex

To run bfloat16 (BF16) GEMM with accumulation to FP32 on the MI200 series devices call, e.g.:
./gemm R_16B R_16B R_32F R_32F OP_N OP_T 8640 8640 8640 8640 8640 8640 50 300 ex

Command-Line Details

    ./gemm PRECISION_A
           PRECISION_B
           PRECISION_C
           COMPUTE_PRECISION
           OP_A
           OP_B
           M
           N
           K
           LDA
           LDB
           LDC
           BATCH_COUNT
           TIME_SPAN    runtime duration in seconds
           [batched]    run batched GEMM
           [strided]    run strided batched GEMM
           [ex]         use the Ex API
           [hostA]      A in host memory
           [hostB]      B in host memory
           [hostC]      C in host memory
           [coherentA]  if in host memory, A is coherent (not cached)
           [coherentB]  if in host memory, B is coherent (not cached)
           [coherentC]  if in host memory, C is coherent (not cached)
           [sharedA]    one A for all devices
           [sharedB]    one B for all devices
           [zeroBeta]   set beta to zero
           [testing]    perform a basic sanity check
           [times]      print time in microseconds in addition to GFLOPS
           [hostname]   print the hostname
           [threaded]   launch to each device from a different thread

When TIME_SPAN is set to 0, one warmup run is done, followed by one timing run, and printing of column labels is disabled.

Supported Precisions:

  • R_16B: BF16
  • R_16F: FP16
  • R_32F: float
  • R_64F: double
  • C_32F: float complex
  • C_64F: float double
  • R_8I: 8-bit int
  • R_32I: 32-bit int

Supported Ops:

  • OP_N: non-transposed
  • OP_T: transposed
  • OP_C: conjugate-transposed

Details

  • benchmarks hipblas?gemm[Batched|StridedBatched][Ex]
  • allocates BATCH_SIZE number of matrices A, B, and C
  • initializes with hipRAND (random uniform, 0.0 to 1.0)
  • calls hipBLAS and collects execution times using std::chrono
  • sets alpha to 2.71828 and beta to 3.14159
  • for hipblas?gemm[Ex] launches a sequence of calls and takes the median time
  • for hipblas?gemm[Strided]Batched[Ex] launches one call and takes the overall time
  • reports the corresponding GFLOPS
  • repeats until TIME_SPAN exceeded
  • executes simulteneously on all devices

If testing is set, a primitive sanity test is ran. Entries of A, B, and C are set to 1, and so are the factors alpha and beta. Then, after GEMM is ran, all entries of C are checked to contain k+1. Note that performance is usually much higher when using integer initialization then when using random data.

Help

Jakub Kurzak ([email protected])