Performance Optimization of Deep Learning Sparse Matrix Kernels on Intel
  Max Series GPU
Mohammad Zubair Christoph Bauinger
In this paper, we focus on three sparse matrix operations that are relevant
for machine learning applications, namely, the sparse-dense matrix
multiplication (SPMM), the sampled dense-dense matrix multiplication (SDDMM),
and the composition of the SDDMM with SPMM, also termed as FusedMM. We develop
optimized implementations for SPMM, SDDMM, and FusedMM operations utilizing
Intel oneAPI's Explicit SIMD (ESIMD) SYCL extension API. In contrast to CUDA or
SYCL, the ESIMD API enables the writing of explicitly vectorized kernel code.
Sparse matrix algorithms implemented with the ESIMD API achieved performance
close to the peak of the targeted Intel Data Center GPU. We compare our
performance results to Intel's oneMKL library on Intel GPUs and to a recent
CUDA implementation for the sparse matrix operations on NVIDIA's V100 GPU and
demonstrate that our implementations for sparse matrix operations outperform
either.