Dynamic Partitioning Method for Near-Memory Parallel Processing of Sparse Matrix-Vector Multiplication

Abstract

Near-memory processing (NMP), which places lightweight processing units near the DRAM memory, has been actively studied to speed up the execution of memory-intensive applications by reducing the amount of data traffic between the DRAM and the CPU. Sparse matrix-vector multiplication (SpMV) is a representative memory-bound kernel used in various applications such as graph analytics, scientific computing, and machine learning. There are prior works to accelerate SpMV by NMP employing a fixed partitioning scheme that hides random access of SpMV using a parallel NMP core. However, due to the various distributions of the matrix, the fixed partitioning of prior works causes a load imbalance in which a sparse matrix is unevenly allocated to processing units for NMP. To resolve this, dynamic partitioning methods to distribute matrices and vectors to the NMP processing units can be effective. In this paper, we propose a dynamic partitioning algorithm (DPA) that analyzes the distribution of non-zero elements in a sparse matrix to classify it into three types (even distribution, skewed distribution, and power-law distribution) and partitions the matrix according to each distribution. Our proposed distribution scheme alleviates load imbalance by up to 73% when compared to static distribution schemes, and such improvement achieves an average speed-up 1.37x (up to 1.84x) over the NMP architecture with static distribution schemes.