Presentation
Pipirima: Predicting Patterns in Sparsity to Accelerate Matrix Algebra
DescriptionWhile sparsity, a feature of data in many applications, provides optimization opportunities such as reducing unnecessary computations, data transfers, and storage, it causes several challenges, too. For instance, even in state-of-the-art
sparse accelerators, sparsity can result in load imbalance; a performance bottleneck. To solve such challenges, our key insight
is that if while reading/streaming compressed sparse matrices we can quickly anticipate the locations of the non-zero values in
a sparse matrix, we can leverage this knowledge to accelerate processing sparse matrices. To enable this, we propose Pipirima, a lightweight prediction-based sparse accelerator. Inspired by traditional branch predictors, Pipirima uses resource-friendly simple counters to predict the patterns of non-zero values in the sparse matrices. We evaluate Pipirima based on sparse matrix vector multiplication (SpMV) and sparse matrix-dense matrix multiplication (SpMM) kernels on CSR compressed matrices derived from both scientific computing and transformer models. On average, our experiments show 6× and 4× speed up over Tensaurus for SpMM and SpMV, respectively on SuiteSparse workload. Pipirima also shows 40× speed up over ExTensor for SpMM. We achieve 8.3×, 48.2× over Tensaurus and ExTensor in lesser sparse transformer workloads. Piprima consumes 5.621mm2 area and 544.93mW power using 45nm technology with predictor related components as the least expensive ones.
sparse accelerators, sparsity can result in load imbalance; a performance bottleneck. To solve such challenges, our key insight
is that if while reading/streaming compressed sparse matrices we can quickly anticipate the locations of the non-zero values in
a sparse matrix, we can leverage this knowledge to accelerate processing sparse matrices. To enable this, we propose Pipirima, a lightweight prediction-based sparse accelerator. Inspired by traditional branch predictors, Pipirima uses resource-friendly simple counters to predict the patterns of non-zero values in the sparse matrices. We evaluate Pipirima based on sparse matrix vector multiplication (SpMV) and sparse matrix-dense matrix multiplication (SpMM) kernels on CSR compressed matrices derived from both scientific computing and transformer models. On average, our experiments show 6× and 4× speed up over Tensaurus for SpMM and SpMV, respectively on SuiteSparse workload. Pipirima also shows 40× speed up over ExTensor for SpMM. We achieve 8.3×, 48.2× over Tensaurus and ExTensor in lesser sparse transformer workloads. Piprima consumes 5.621mm2 area and 544.93mW power using 45nm technology with predictor related components as the least expensive ones.
Event Type
Research Manuscript
TimeMonday, June 233:30pm - 3:45pm PDT
Location3003, Level 3
DES3: Emerging Models of ComputatioN