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 GPU Memory Transactions for Convolution Operations
Gangzhao Lu,Weizhe Zhang,Zheng Wang +2 more
- 01 Sep 2020
TL;DR: This paper presents a novel approach to optimize memory access for convolution operations, specifically targeting GPU execution, that leverages two optimization techniques to reduce the number of memory operations for Convolution operations performed on the width and height dimensions.
Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures
Kaushik Datta,Mark Murphy,Vasily Volkov,Samuel Williams,Jonathan Carter,Leonid Oliker,David A. Patterson,John Shalf,Katherine Yelick +8 more
- 15 Nov 2008
TL;DR: This work explores multicore stencil (nearest-neighbor) computations --- a class of algorithms at the heart of many structured grid codes, including PDF solvers, and develops a number of effective optimization strategies, and builds an auto-tuning environment that searches over these strategies to minimize runtime, while maximizing performance portability.
Work-Efficient Parallel GPU Methods for Single-Source Shortest Paths
Andrew Davidson,Sean Baxter,Michael Garland,John D. Owens +3 more
- 19 May 2014
TL;DR: It is shown that in general the Near-Far method has the highest performance on modern GPUs, outperforming other parallel methods, and also explores a variety of parallel load-balanced graph traversal strategies and apply them towards the SSSP solver.
Optimizing 2D and 3D structured Euler CFD solvers on Graphical Processing Units
TL;DR: These guidelines are applied to the implementation on a GPU of a 2D Euler equations solver on a structured grid and its tridimensional extension on multiple GPUs.
Optimized implementation of the Lattice Boltzmann Method on a graphics processing unit towards real-time fluid simulation
TL;DR: The implementation of an optimized 3D real-time thermal and turbulent fluid flow solver with a performance of half a billion lattice node updates per second is described in detail.