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
A software technique to enhance register utilization of Convolutional Neural Networks on GPGPUs
Che-Huai Lin,An-Ting Cheng,Bo-Cheng Lai +2 more
- 21 Jul 2017
TL;DR: This paper proposes a software-based loop-unrolling technique to enhance the data usage on the registers and significantly improve the overall performance of GPGPUs.
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Optimizations with CUDA: A Case Study on 3D Curve-Skeleton Extraction from Voxelized Models
Jesús M. Pérez Jiménez,Juan Ruiz de Miras +1 more
- 01 Jan 2013
TL;DR: This paper details step by step a comprehensive collection of optimizations to be considered in this class of algorithms, and in general in any CUDA implementation, when working with algorithms that have high data-sharing or data-dependence requirements.
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Increasing GPU-Speedup of Volume Rendering for Images with High Complexity
Sang-Soo Jun,Ok-Kyoon Ha +1 more
- 25 Nov 2015
TL;DR: This paper empirically proved that this RBDV algorithm increases the GPU-speedup of transparent voxel skipping at least by 14%, improving it from x17.5 up to x20.0 or more, on average, for complex target images.
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CUDA Memory Techniques for Matrix Multiplication on Quadro 4000
Tekesha Athil,Richard Christian,Yenumula B. Reddy +2 more
- 07 Apr 2014
TL;DR: This paper will take a critical look at different performance techniques such as tiling, memory coalescing, perfecting, and loop unrolling, in trying to evaluate which method is the most efficient approach for the authors' problem set (matrix operation of n size matrices).
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Memory Access Optimization of High-Order CFD Stencil Computations on GPU
Shengxiang Wang,Zhuoqian Li,Yonggang Che +2 more
- 28 Dec 2020
TL;DR: In this paper, the issue of memory access optimization for the key stencil computations of a high-order CFD program on the NVidia GPU has been studied, and two methods are used to optimize the performance.
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