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
GPU-accelerated co-design of induced dimension reduction: algorithmic fusion and kernel overlap
Hartwig Anzt,Eduardo Ponce,Gregory D. Peterson,Jack Dongarra +3 more
- 15 Nov 2015
TL;DR: An optimized GPU co-design of the Induced Dimension Reduction (IDR) algorithm for solving linear systems is presented and it is revealed that the interplay between them can succeed in cutting the overall runtime by up to about one third.
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Impacts of optimization strategies on performance, power/energy consumption of a GPU based parallel reduction
TL;DR: This work evaluates performance and power/energy consumption of a well-known application running on different commercial GPU devices with the different optimization strategies and shows that effective GPU optimization strategies can improve the application performance significantly without increasing power and energy consumption.
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Auto-Tuning of Thread Assignment for Matrix-Vector Multiplication on GPUs
TL;DR: A novel auto-tuning method for matrix-vector multiplication on GPUs, where the number of assigned threads that are used to compute one element of the result vector can beAuto-tuned according to the size of matrix is proposed.
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A GPU memory efficient speed-up scheme for training ultra-deep neural networks: poster
Jinrong Guo,Wantao Liu,Wang Wang,Qu Lu,Songlin Hu,Jizhong Han,Ruixuan Li +6 more
- 16 Feb 2019
TL;DR: This paper presents a scheme that dedicates to make the utmost use of finite GPU memory resource to speed up the training process for UDNN and verifies the effectiveness of the scheme in both single and distributed GPU mode.
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