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
Accelerating the Dynamic Programming for the Matrix Chain Product on the GPU
Kazufumi Nishida,Yasuaki Ito,Koji Nakano +2 more
- 30 Nov 2011
TL;DR: The main contribution of this paper is to present an efficient parallel implementation of this Matrix Chain Product Problem optimization problem for finding parentheses of the matrix chain that gives the minimum total number of multiplications necessary to compute the product of the matrices chain on the GPU.
Performance models for CPU-GPU data transfers
B. van Werkhoven,Jason Maassen,Frank J. Seinstra,Henri E. Bal +3 more
- 26 May 2014
TL;DR: An analytical performance model is proposed that includes PCIe transfers and overlapping computation and communication and shows that the performance models are capable of correctly classifying the relative performance of the different implementations.
Optimization and architecture effects on GPU computing workload performance
John A. Stratton,Nasser Anssari,Christopher I. Rodrigues,I-Jui Sung,Nady Obeid,Li-Wen Chang,Geng Daniel Liu,Wen-mei W. Hwu +7 more
- 13 May 2012
TL;DR: This work demonstrates that certain architectural features make a huge difference in the performance of unoptimized code, such as the inclusion of a general cache which can improve performance by 2-4× in some situations and describes what optimization patterns have been most essential and widely applicable for improving performance for GPU computing workloads across all architecture generations.
Fast block distributed CUDA implementation of the Hungarian algorithm
TL;DR: In this implementation, the alternating path search phase of the algorithm is distributed by several blocks in a way to minimize global device synchronization, which results in a fast implementation for moderate size problems.
GPU Multisplit: An Extended Study of a Parallel Algorithm
Saman Ashkiani,Andrew Davidson,Ulrich Meyer,John D. Owens +3 more
- 23 Aug 2017
TL;DR: In this article, a warp-synchronous programming model and warp-wide communications are used to avoid branch divergence and reduce memory usage for multisplit-based sort.