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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Citations
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Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines
Jonathan Ragan-Kelley,Connelly Barnes,Andrew Adams,Sylvain Paris,Frédo Durand,Saman Amarasinghe +5 more
- 16 Jun 2013
TL;DR: A systematic model of the tradeoff space fundamental to stencil pipelines is presented, a schedule representation which describes concrete points in this space for each stage in an image processing pipeline, and an optimizing compiler for the Halide image processing language that synthesizes high performance implementations from a Halide algorithm and a schedule are presented.
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