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
High Performance Stereo Vision Designed for Massively Data Parallel Platforms
TL;DR: A stereo system on GPU which pushes the Pareto-efficiency frontline in the accuracy and speed tradeoff space, based on a hardware-aware algorithm design approach and adopting code optimization methodologies from the performance tuning community, and applies them to this specific application.
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Efficient sparse matrix-vector multiplication on cache-based GPUs
Istvan R eguly,Michael B. Giles +1 more
- 13 May 2012
TL;DR: This paper discusses efficient implementations of sparse matrix-vector multiplication on NVIDIA's Fermi architecture, the first to introduce conventional L1 caches to GPUs, and focuses on the compressed sparse row (CSR) format for developing general purpose code.
Optimization of Sparse Matrix-Vector Multiplication with Variant CSR on GPUs
Xiaowen Feng,Hai Jin,Ran Zheng,Kan Hu,Jingxiang Zeng,Zhiyuan Shao +5 more
- 07 Dec 2011
TL;DR: A new storage format, which is called Compressed Sparse Row with Segmented Interleave Combination (SIC), is proposed, which employs an interleave combination pattern that combines certain amount of CSR rows to form a new SIC row.
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Free launch: optimizing GPU dynamic kernel launches through thread reuse
Guoyang Chen,Xipeng Shen +1 more
- 05 Dec 2015
TL;DR: This work proposes free launch, a new software approach to overcoming the shortcomings of both methods for exploiting dynamic parallelism on GPU, which employs a novel compiler-based code transformation named subkernel launch removal to replace the subkernel launches with the reuse of parent threads.
A highly efficient GPU implementation for variational optic flow based on the euler-lagrange framework
Pascal Gwosdek,Henning Zimmer,Sven Grewenig,Andrés Bruhn,Joachim Weickert +4 more
- 10 Sep 2010
TL;DR: This work presents the first approach that solves the EL equations of state-of-the-art methods on sequences with $640 \!\times\!