Several optimization methods of half-precision general matrix vector multiplication (HGEMV) using CUDA core. The calculation expression is as follows, where the precision of matrix A (1 * K), B (K * N) and C (1 * N) is FP16. Through exploring various parallel task design, the current performance between 1 to 4096 dimensions is not less than 150% of the performance of cublas.

C (1 * N) = A (1 * K) * B (K * N)

• Thread Naive: each thread computes 1 result of C
• Thread Smem: each thread computes 1 result of C using shared memory
• Warp1 Naive: each warp computes 1 result of C
• Warp1 Smem: each warp computes 1 result of C using shared memory
• Warp2 Naive: each warp computes 2 results of C
• Warp2 Smem: each warp computes 2 results of C using shared memory
• Warp4 Naive: each warp computes 4 results of C
• Warp4 Smem: each warp computes 4 results of C using shared memory
• Warp8 Naive: each warp computes 8 results of C
• Warp8 Smem: each warp computes 8 results of C using shared memory
• Warp16 Naive: each warp computes 16 results of C
• Warp16 Smem: each warp computes 16 results of C using shared memory

• OS: Linux
• Cmake Version: >= 3.12
• GCC Version: >= 4.8
• CUDA Version: >= 11.0
• Others: gflags, ccache

sudo apt-get install libgflags-dev ccache

git clone https://github.com/Bruce-Lee-LY/cuda_hgemv.git

cd cuda_hgemv
./build.sh -a 80 -t Release -b OFF
./build.sh -a 80 -t Debug -b OFF

cd cuda_hgemv
./build.sh -a 86 -t Release -b OFF
./build.sh -a 86 -t Debug -b OFF

./run_sample.sh

Process the data in the log and plot it as a line chart.

cd tools/performance
./performance.sh

• CUDA Version: 11.8
• K: 128

Performance achieved by current optimization methods.