2023/07/08 by Vikas Natesh, Natesh, Vikas, Andrew Sabot +5 · 1 citation
Computer Science · #Advanced Neural Network Applications #FOS: Computer and information sciences #Hardware Architecture (cs.AR) #Machine Learning (cs.LG) #Parallel Computing and Optimization Techniques #Performance (cs.PF) #Programming Languages (cs.PL) #Stochastic Gradient Optimization Techniques
paper · pdf · doi:10.48550/arxiv.2307.03930
openalex publication_date 2023/07/08 · openalex created_date 2023/07/12 · openalex updated_date 2026/07/28
We propose Rosko -- row skipping outer products -- for deriving sparse matrix multiplication (SpMM) kernels in reducing computation and memory access requirements of deep neural networks (DNNs). Rosko allows skipping of entire row computations during program execution with low sparsity-management overheads. We analytically derive sparse CPU kernels that adapt to given hardware characteristics to effectively utilize processor cores and minimize data movement without the need for auto-tuning or search space exploration. Rosko can be integrated with other outer product scheduling methods, allowing them to leverage row skipping by using Rosko's packing format to skip unnecessary computation. Rosko kernels outperform existing auto-tuning and search-based solutions as well as state-of-the-art vendor-optimized libraries on real hardware across a variety of neural network workloads. For matrices with sparsities ranging from 65% to 99.8% typically found in machine learning, Rosko kernels achieve up to a 6.5x runtime reduction on Intel and ARM CPUs.