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Sparse linear algebra is fundamental to numerous areas of applied mathematics, science and engineering. In this paper, we propose an efficient data structure named AdELL+ for optimizing the SpMV kernel on GPUs, focusing on performance bottlenecks of sparse computation. The foundation of our work is an ELL-based adaptive format which copes with matrix irregularity using balanced warps composed using...
Numerous applications in science and engineering rely on sparse linear algebra. The efficiency of a fundamental kernel such as the Sparse Matrix-Vector multiplication (SpMV) is crucial for solving increasingly complex computational problems. However, the SpMV is notorious for its extremely low arithmetic intensity and irregular memory patterns, posing a challenge for optimization. Over the last few...
The sparse matrix-vector multiplication (SpMV) is a fundamental computational kernel used in science and engineering. As a result, the performance of a large number of applications depends on the efficiency of the SpMV. This kernel is, in fact, a bandwidth-limited operation and poses a challenge for optimization when the matrix has an irregular structure. The literature on implementing SpMV on throughput-oriented...
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