Choi, 'A Scalable Processing-in-Memory Accelerator for Parallel Graph Processing,' in ISCA, 2015. SMASH incurs a very modest hardware area overhead of up to 0.076% of an out-of-order CPU core. Our evaluations show that SMASH provides average performance improvements of 38% for Sparse Matrix Vector Multiplication and 44% for Sparse Matrix Matrix Multiplication, over a state-of-the-art CSR implementation, on a wide variety of matrices with different characteristics. We demonstrate the benefits of SMASH on four use cases that include sparse matrix kernels and graph analytics applications. SMASH exposes an expressive and rich ISA to communicate with the BMU, which enables its use in accelerating any sparse matrix computation.

We design a lightweight hardware unit, the Bitmap Management Unit (BMU), that buffers and scans the bitmap hierarchy to perform highly-efficient indexing of sparse matrices. At the same time, the bitmap encoding can be directly interpreted by the hardware. This encoding can be used to efficiently compress any sparse matrix, regardless of the extent and structure of sparsity. To this end, we devise a novel software encoding based on a hierarchy of bitmaps. The key idea of SMASH is to explicitly enable the hardware to recognize and exploit sparsity in data. We propose SMASH, a hardware-software cooperative mechanism that enables highly-efficient indexing and storage of sparse matrices. In this paper, we identify the discovery of the positions (i.e., indexing) of non-zero elements as a key bottleneck in sparse matrix-based workloads, which greatly reduces the benefits of compression. However, compression techniques like Compressed Sparse Row (CSR) that are widely used today introduce significant instruction overhead and expensive pointer-chasing operations to discover the positions of the non-zero elements. These operations use sparse matrix compression as an effective means to avoid storing zeros and performing unnecessary computation on zero elements.

Important workloads, such as machine learning and graph analytics applications, heavily involve sparse linear algebra operations.

Pb 2019 high compressed