HAM-SpMSpV: an Optimized Parallel Algorithm for Masked Sparse Matrix-Sparse Vector Multiplications on multi-core CPUs

The efficiency of Sparse Matrix-Sparse Vector Multiplication (SpM-SpV) is critically important in fields such as machine learning and graph analytics. In certain algorithms, masked SpMSpV computes only a subset of the result entries. Despite its significance, this selective computation poses unique challenges, and existing algorithms often struggle to exploit the sparsity of the input and the mask vectors concurrently. To boost the efficiency of masked SpMSpV on shared memory architectures, we introduce a hybrid adaptive masked SpMSpV algorithm (HAM-SpMSpV) designed to select the efficient kernel automatically based on input features. This approach builds upon the foundation of a conventional algorithm, incorporating two novel masked SpMSpVs: the pre-bucketing masked SPA-based algorithm and the pre-masking bucketed hash-based algorithm. The newly proposed algorithms significantly expedite computation, especially in scenarios with high sparsity in input vectors and masks. Our evaluation involved extensive testing across a diverse range of real-world graphs, utilizing various sparsity of input vectors and masks. This rigorous testing confirmed that our approach notably outperforms existing solutions. Specifically, it achieves a speedup of up to 1.96 times compared to SuiteSparse:GraphBLAS and a remarkable 6.28 times relative to MKL Graph, demonstrating significant advancements in SpMSpV efficiency.