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 string matching for database applications
Evangelia Sitaridi,Kenneth A. Ross +1 more
- 01 Oct 2016
TL;DR: This work focuses on the efficient implementation of string matching operators common in SQL queries and studies the cache memory efficiency of single- and multi-pattern string matching algorithms for conventional and pivoted string layouts in the GPU memory.
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Architecture-aware optimization targeting multithreaded stream computing
Byunghyun Jang,Synho Do,Homer Pien,David Kaeli +3 more
- 08 Mar 2009
TL;DR: This work begins with disassembled machine code and collects program statistics provided by the AMD Graphics Shader Analyzer (GSA) profiling toolset, and explores optimizations targeting three different computing resources: ALUs, fetch bandwidth, and 3) thread usage, and presents optimization techniques that consider how to better utilize each resource.
A Unified Optimization Approach for Sparse Tensor Operations on GPUs
TL;DR: This work proposes a unified tensor representation called F-COO, which provides highly-optimized implementations of sparse tensor computations on GPUs and implements a CANDECOMP/PARAFAC (CP) decomposition and achieves up to 14.9 times speedup using the unified method over state-of-the-art libraries on NVIDIA Titan-X GPUs.
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A control-structure splitting optimization for GPGPU
Snaider Carrillo,Jakob Siegel,Xiaoming Li +2 more
- 18 May 2009
TL;DR: Novel techniques to transform control statements so that they can be executed efficiently on GPUs are proposed and can lead to an increase in occupancy and a drastic improvement in performance compared to non-split version of the programs.
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