Sparse-Matrix Compression Primitives with OpenCL Framework to Support Halide

Halide and OpenCL now play important roles for heterogeneous multi-core computing. OpenCL provides vendor-level support and Halide provides domain-specific support such as vision processing and AI model (TVM Halide IR). Halide also provides flexible scheduling for applications on target machines. OpenCL plays a supporting role for Halide environments. In this work, we investigate the research issues in supporting sparse computation with Halide and their corresponding OpenCL support. We present sparse matrix compression primitives on Halide for sparse matrix matrix (SpMM) multiplication with OpenCL framework. Halide is a programming language designed to process image and array from numerous algorithms and scheduling primitives to achieve state-of-art performance including SIMD and heterogeneous computation. This paper proposed the implementation of sparse matrix compression for Halide scheduling primitives including COO, CSR, and hybrid CSR. The design of experiments includes Halide primitives for sparse matrix compression and matrix computations. The experimental result of computation with compressing matrix shows the performance are improved by up to 85% compared to the baseline without compression.