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
Optimizing Image Sharpening Algorithm on GPU
Mengran Fan,Haipeng Jia,Yunquan Zhang,Xiaojing An,Ting Cao +4 more
- 01 Sep 2015
TL;DR: This paper proposes a complete solution to implement and optimize sharpness on GPU and includes five major and effective techniques: Data Transfer Optimization, Kernel Fusion, Vectorization for Data Locality, Border and Reduction Optimization.
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Data reordering for minimizing threads divergence in GPU-based evaluating association rules
Youcef Djenouri,Ahcene Bendjoudi,Malika Mehdi,Zineb Habbas,Nadia Nouali-Taboudjemat +4 more
- 01 Jun 2015
TL;DR: This paper proposes an intelligent strategy called Transactions-based Reordering ”TR” allowing an efficient evaluation of association rules on GPU by minimizing threads divergence, based on data base re-organization.
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Impact of Vectorization Over 16-bit Data-Types on GPUs
Luís Paulo Reis,Ricardo Nobre,João M. P. Cardoso +2 more
- 23 Jan 2018
TL;DR: It is found that, on an AMD Vega 10 XT GPU, half-precision vectorization leads to performance improvements over the scalar version using the same precision (geometric mean speedup of 1.50x), which can be attributed to the GPU being able to make use of native native support for arithmetic over packed half- Precision data.
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Evaluation of Splitting-Up Conjugate Gradient Method on GPUs
Akiyoshi Wakatani
- 04 Apr 2016
TL;DR: In order to enhance the memory bandwidth to the global memory of GPUs, the implementation utilizes a pseudo matrix transposition before and after a tridiagonal matrix solver, which results in coalesced memory accesses.
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