hma02 / cublashgemm-p100

Code for testing the native float16 matrix multiplication performance on Tesla P100 and V100 GPU based on cublasHgemm

fp16-cublasHgemm-test

A simple benchmarking code of the half-precision (float16) performance on Tesla P100 (sm_60) or V100 (sm_70) GPU based on cublasHgemm.

Build and Run

The code does C=alpha*A*B+beta*C on GPU with different sizes of square matrices A, B and C. Shape A is (m,k). Shape B is (k,n). Shape C is (m,n).

To test float16 matrix multiplication,

$ make
$ ./hgemm

Comment line 11 in hgemm.cu to test float32 matrix multiplication.

Tesla P100 Example Testing Result

nvcc hgemm.cu -lcublas --std=c++11 -arch=sm_60  -o hgemm

running cublasHgemm test

running with min_m_k_n: 2 max_m_k_n: 32768 repeats: 10
allocating device variables
float16; size 2 average: 7.69632e-05 s 
float16; size 4 average: 1.34304e-05 s 
float16; size 8 average: 3.49152e-05 s 
float16; size 16 average: 1.6272e-05 s 
float16; size 32 average: 1.91808e-05 s 
float16; size 64 average: 2.52672e-05 s 
float16; size 128 average: 2.48512e-05 s 
float16; size 256 average: 6.52992e-05 s 
float16; size 512 average: 0.000111104 s 
float16; size 1024 average: 0.000275123 s 
float16; size 2048 average: 0.00155046 s 
float16; size 4096 average: 0.00934949 s 
float16; size 8192 average: 0.0659167 s 
float16; size 16384 average: 0.508014 s 
float16; size 32768 average: 4.01786 s 

nvcc hgemm.cu -lcublas --std=c++11 -arch=sm_60  -o hgemm

running cublasSgemm test

running with min_m_k_n: 2 max_m_k_n: 32768 repeats: 10
allocating device variables
float32; size 2 average: 5.21152e-05 s 
float32; size 4 average: 2.06112e-05 s 
float32; size 8 average: 7.1616e-06 s 
float32; size 16 average: 5.3248e-06 s 
float32; size 32 average: 4.624e-06 s 
float32; size 64 average: 1.128e-05 s 
float32; size 128 average: 2.37504e-05 s 
float32; size 256 average: 4.83776e-05 s 
float32; size 512 average: 0.000117616 s 
float32; size 1024 average: 0.000599805 s 
float32; size 2048 average: 0.0026987 s 
float32; size 4096 average: 0.0180615 s 
float32; size 8192 average: 0.128823 s 
float32; size 16384 average: 1.00408 s 
float32; size 32768 average: 8.07247 s 

Tesla V100 Example Testing Result

nvcc hgemm.cu -lcublas --std=c++11 -arch=sm_70  -o hgemm

running cublasHgemm test

running with min_m_k_n: 2 max_m_k_n: 32768 repeats: 10
allocating device variables
float16; size 2 average: 0.000115712 s
float16; size 4 average: 6.76864e-05 s
float16; size 8 average: 7.03488e-05 s
float16; size 16 average: 7.08608e-05 s
float16; size 32 average: 7.8336e-05 s
float16; size 64 average: 8.16128e-05 s
float16; size 128 average: 8.7552e-05 s
float16; size 256 average: 0.000126157 s
float16; size 512 average: 0.000196301 s
float16; size 1024 average: 0.000361267 s
float16; size 2048 average: 0.00156385 s
float16; size 4096 average: 0.00853637 s
float16; size 8192 average: 0.0443268 s
float16; size 16384 average: 0.307294 s
float16; size 32768 average: 2.30823 s

nvcc hgemm.cu -lcublas --std=c++11 -arch=sm_70  -o hgemm

running cublasSgemm test

running with min_m_k_n: 2 max_m_k_n: 32768 repeats: 10
allocating device variables
float32; size 2 average: 6.7584e-05 s 
float32; size 4 average: 6.53312e-05 s 
float32; size 8 average: 6.47168e-05 s 
float32; size 16 average: 6.44096e-05 s 
float32; size 32 average: 7.29088e-05 s 
float32; size 64 average: 7.4752e-05 s 
float32; size 128 average: 8.06912e-05 s 
float32; size 256 average: 0.000160768 s 
float32; size 512 average: 0.000111923 s 
float32; size 1024 average: 0.000254464 s 
float32; size 2048 average: 0.00134257 s 
float32; size 4096 average: 0.00944916 s 
float32; size 8192 average: 0.0721418 s 
float32; size 16384 average: 0.573173 s 
float32; size 32768 average: 4.6143 s

Reference

• Mixed-Precision Programming with CUDA 8
• parallel-forall haxpy
• cublasSgemmBatched Example
• Why cublasHgemm is slower
• BLAS Interface for Different Precision