Presentation
Polaris: Enabling PIM-Optimized Low-Energy SpGEMM Using a Remote-Access-Aware Mapping Solution
DescriptionSparse kernels are widely used in applications ranging from scientific computing to machine learning. Among them, SpGEMM is particularly challenging due to irregular memory accesses, making it highly memory-bound and dominated by data movement. Processing-in-Memory architectures can mitigate this cost by performing computation within memory banks, but irregular sparsity often causes load imbalance and costly cross-bank accesses. To address these challenges, we propose Polaris, a PIM-aware map-
ping solution for efficient SpGEMM on HBM-based systems. Polaris leverages a row-wise product formulation and a sparsity-aware mapping strategy to improve locality and balance workloads across banks, reducing data movement and achieving on average, 2× lower energy consumption and up to 8× speedup over prior designs.
ping solution for efficient SpGEMM on HBM-based systems. Polaris leverages a row-wise product formulation and a sparsity-aware mapping strategy to improve locality and balance workloads across banks, reducing data movement and achieving on average, 2× lower energy consumption and up to 8× speedup over prior designs.
Event Type
Late Breaking Results
TimeMonday, July 275:32pm - 5:35pm PDT
LocationExhibit Hall