Optimization Techniques for GPU Programming
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TL;DR: In this article , a survey discusses various optimization techniques found in 450 articles published in the last 14 years and analyzes the optimizations from different perspectives which shows that the various optimizations are highly interrelated, explaining the need for techniques such as auto-tuning.
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Abstract: In the past decade, Graphics Processing Units have played an important role in the field of high-performance computing and they still advance new fields such as IoT, autonomous vehicles, and exascale computing. It is therefore important to understand how to extract performance from these processors, something that is not trivial. This survey discusses various optimization techniques found in 450 articles published in the last 14 years. We analyze the optimizations from different perspectives which shows that the various optimizations are highly interrelated, explaining the need for techniques such as auto-tuning.
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References
Optimization of GPU-based Sparse Matrix Multiplication for Large Sparse Networks
Jeongmyung Lee,Seokwon Kang,Yongseung Yu,Yong-Yeon Jo,Sang-Wook Kim,Yongjun Park +5 more
- 20 Apr 2020
TL;DR: A new optimization pass called Block Reorganizer is proposed, which balances the total computations of each computing unit on target GPUs, based on the outer-product-based expansion process, and reduces the memory pressure during the merge process.
Optimizing Sparse Matrix Operations on GPUs Using Merge Path
Steven Dalton,Sean Baxter,Duane Merrill,Luke N. Olson,Michael Garland +4 more
- 25 May 2015
TL;DR: This paper develops a parallel processing scheme to compute segmented row-wise operations on sparse matrices that exposes parallelism at the granularity of individual nonzero entries and achieves competitive performance across many diverse problems while maintaining predictable behaviour dependent only on the computational work.