Chapter 10
Parallel Patterns: Sparse Matrix–Vector MultiplicationAn Introduction to Compaction and Regularization in Parallel Algorithms
Chapter Outline
10.1 Background
10.2 Parallel SpMV Using CSR
10.3 Padding and Transposition
10.4 Using Hybrid to Control Padding
10.5 Sorting and Partitioning for Regularization
10.6 Summary
10.7 Exercises
Our next parallel pattern is sparse matrix computation. In a sparse matrix, the vast majority of the elements are zeros. Storing and processing these zero elements are wasteful in terms of memory, time, and energy. Many important real-world problems involve sparse matrix computations that are highly parallel in nature. Due to the importance of these problems, several sparse matrix storage formats ...
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