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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Jamin Naghmouchi,Daniele Paolo Scarpazza,Mladen Berekovic +2 more
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TL;DR: This work describes an optimization path of the kernel of flex to four nVidia GPGPU models, with decisions based on quantitative micro-benchmarking, performance counters and simulator runs, and achieves a tokenization throughput that exceeds the results obtained by the GPG PU-based string matching solutions presented so far, and compares well with solutions obtained on any architecture.
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Yi Yang,Huiyang Zhou +1 more
TL;DR: This paper presents the experience in developing an optimizing compiler for general purpose computation on graphics processing units (GPGPU) based on the Cetus compiler framework, which achieves very high performance, either superior or very close to highly fine-tuned libraries.
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An Auto-tuning Solution to Data Streams Clustering in OpenCL
Jianbin Fang,Ana Lucia Varbanescu,Henk Sips +2 more
- 24 Aug 2011
TL;DR: This paper provides an OpenCL implementation for data streams clustering, and then presents several optimizations for it, which make it more efficient in terms of memory usage.
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CoAdELL: Adaptivity and Compression for Improving Sparse Matrix-Vector Multiplication on GPUs
Marco Maggioni,Tanya Y. Berger-Wolf +1 more
- 19 May 2014
TL;DR: This paper proposes to efficiently combine adaptivity and compression into an ELL-based sparse format in order to improve the state-of-the-art of the SpMV on Graphic Processing Units (GPUs) and provides a highly-optimized novel sparse matrix format known as Compressed Adaptive ELL (CoAdELL).
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Performance evaluation and optimization of HBM-Enabled GPU for data-intensive applications
Maohua Zhu,Youwei Zhuo,Chao Wang,Wenguang Chen,Yuan Xie +4 more
- 27 Mar 2017
TL;DR: This paper implements two representative dataintensive applications, convolutional neural network (CNN) and breadth-first search (BFS) on an HBM-enabled GPU to evaluate the improvement brought by the adoption of the HBM, and designs two programming techniques to improve the utilization of memory bandwidth for BFS application.
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