CSR&RV: An Efficient Value Compression Format for Sparse Matrix-Vector Multiplication

Junjun Yan, Xinhai Chen, Jie Liu

Abstract: Sparse Matrix-Vector Multiplication (SpMV) plays a critical role in many areas of science and engineering applications. The storage space of value array in general real sparse matrices accounts for costly. However, the existing compressed formats cannot balance the compressed rate and computational speed. To address this issue, we propose an efficient value compression format implemented by AVX512 instructions called Compressed Sparse Row and Repetition Value (CSR &RV). This format stores each different value once and uses the indexes array to store the position of values, which reduces the storage space by compressing the value array. We conduct a series of experiments on an Intel Xeon processor and compare it with five other formats in 30 real-world matrices. Experimental results show that CSR &RV can achieve a speedup up to 3.86\(\times \) (1.66\(\times \) on average) and a speedup up to 12.42\(\times \) (3.12\(\times \) on average) for single-core and multi-core throughput, respectively. Meanwhile, our format can reduce the memory space by 48.57% on average.