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
Double precision stencil computations on Kepler GPUs
Anamaria Vizitiu,Lucian Mihai Itu,Laszlo Lazar,Constantin Suciu +3 more
- 15 Dec 2014
TL;DR: This work focuses on stencil based double precision computations for scientific computations, and introduces two basic implementations, which use two-dimensional and three-dimensional thread organization respectively.
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YuenyeungSpTRSV: A Thread-Level and Warp-Level Fusion Synchronization-Free Sparse Triangular Solve
TL;DR: YuenyeungSpTRSV as discussed by the authors is a thread-level and wrap-level fusion synchronization-free SpTRSVM algorithm, which handles the rows with a large number of nonzero elements at warp-level while the rows had a low number of zero elements at thread level, and it can achieve very good performance on the most popular sparse matrix storage, compressed sparse row (CSR) format.
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Solving dense symmetric indefinite systems using GPUs
TL;DR: This paper studies the performance of different algorithms for solving a dense symmetric indefinite linear system of equations on multicore CPUs with a Graphics Processing Unit (GPU) and demonstrates that good performance can be obtained by avoiding the pivoting and using the lower precision arithmetics, respectively.
Compact data structure and scalable algorithms for the sparse grid technique
TL;DR: The sparse grid discretization technique enables a compressed representation of higher-dimensional functions as mentioned in this paper, but it relies heavily on recursion and complex data structures, thus it is computationally inefficient.
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Generalized GPU acceleration for applications employing finite-volume methods
Jingheng Xu,Haohuan Fu,Lin Gan,Chao Yang,Wei Xue,Shizhen Xu,Wenlai Zhao,Xinliang Wang,Bingwei Chen,Guangwen Yang +9 more
- 16 May 2016
TL;DR: This paper aims at optimizing FVM solver based on the structured mesh with two generalized tuning techniques including an explicit cache mechanism as well as an inner-thread rescheduling method that tries to achieve a suitable mapping between the algorithm feature and the platform architecture.
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