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A large-scale sparse matrix multiplication method based on streaming matrix to GPUs

A large-scale sparse matrix multiplication method based on streaming matrix to GPUs
Translated Title
GPU 에서 대규모 희소 행렬 곱셈을 처리하기 위한 스트리밍 방법
Yuk, Ji Hwan
DGIST Authors
Yuk, Ji Hwan; Kim, Min SooDaehee Hwang
Kim, Min Soo
Hwang, Dae Hee
Issue Date
Available Date
Degree Date
2017. 2
Matrix multiplicationLarge-scale sparse matrixGPGPUStream행렬 곱셈대규모 희소 행렬스트림
Sparse matrices are widely used to analyze a complex system which requires lots of linear algebraic operations such as computer graphics, recommender systems, machine learning, and information retrieval. As the size of real graphs are increasing rapidly, fast and scalable methods for handling such large-scale sparse matrices have become harder than before since the size of matrices increases as well. There have been various studies on implementing GPU-based methods for SpGEMM. However, most of those methods have faced two major challenges. First, the irregularity of matrices causes poor load balancing among threads. Second, many matrices do not fit in the GPU device memory.We propose a scalable and efficient GPU-based sparse-sparse matrices multiplication method called MStream which exploits streaming technology, which could hide memory latency of copy between main memory and device memory. To fully exploit GPU streams technology, we explore the design choice of an efficient data structure called slotted page format which divides a matrix into small fixed-size units instead of performing on Compressed Sparse Row (CSR) or Compressed Sparse Column (CSC) formats which are widely used to represent graphs with a sparse matrix in memory. ⓒ 2017 DGIST
Table Of Contents
Ⅰ. INTRODUCTION 1 -- II. PRELIMINARIES . 5 -- 2.1 Many-core GPU 5 -- 2.2 Sparse Format for Matrix 6 -- 2.3 Slotted Page for Representing Sparse Matrix 6 -- 2.4 Two general schemes for matrix multiplication 8 -- 2.5 Observations 10 -- III. MSTREAM METHOD . 14 -- 3.1 Analysis of Algorithms for MStream 14 -- 3.2 Streaming Technique using GPU streams 20 -- 3.3 Overall Framework of MStream 22 -- IV. EXPERIMENTS . 25 -- 4.1 Experimental setup 25 -- 4.2 Comparison with cuSparse 26 -- 4.3 Characteristic of MStream . 27 -- V. CONCLUSIONS 29
Information and Communication Engineering
Related Researcher
  • Author Kim, Min-Soo InfoLab
  • Research Interests Big Data Systems; Big Data Mining & Machine Learning; Big Data Bioinformatics; 데이터 마이닝 및 빅데이터 분석; 바이오인포메틱스 및 뉴로인포메틱스; 뇌-기계 인터페이스(BMI)
Department of Information and Communication EngineeringThesesMaster

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