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 Generation of Correctly Rounded Elementary Functions
TL;DR: This article proposes an analysis of the Lefèvre hard-to-round argument search using the concept of continued fractions, and proposes a new parallel search algorithm that is much more efficient on GPUs thanks to its more regular control flow.
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•Posted Content
Optimizing the Linear Fascicle Evaluation Algorithm for Multi-Core and Many-Core Systems
Karan Aggarwal,Uday Bondhugula +1 more
TL;DR: In this article, target-independent optimizations were proposed to optimize sparse matrix-vector multiplication (SVMV) operations on both CPU and GPU. But the performance of the SpMV operation often depends on exploiting regularity patterns in the matrix.
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Optimization for Multi-Join Queries on the GPU
TL;DR: Experimental results show that the multi- join query optimization proposed in this paper improves the efficiency of multi-join queries, especially in the case of high load and complex join queries, achieving higher throughput than that of previous optimization algorithms.
Optimization techniques for OpenCL-based linear algebra routines
TL;DR: Preliminary results from this work confirm that optimizations are not portable from one device to the next, and show the benefits of automatic tuning.
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The Approximate Discrete Radon Transform: A Case Study in Auto-Tuning of OpenCL Implementations
H. Martin Bücker,Ralf Seidler,David Neuhauser,Tobias Beier +3 more
- 23 Sep 2015
TL;DR: This work introduces a novel auto-tuning framework to generate OpenCL programs and reports on a case study computing an approximate discrete Radon transform, indicating that, for a wide range of problem sizes and input parameters, the execution times of the auto- Tuned OpenCL Programs are smaller than those of three hand-tuned CUDA implementations.
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