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
AdELL: An Adaptive Warp-Balancing ELL Format for Efficient Sparse Matrix-Vector Multiplication on GPUs
Marco Maggioni,Tanya Y. Berger-Wolf +1 more
- 01 Oct 2013
TL;DR: A novel ELL-based matrix format called Adaptive ELL (AdELL) is proposed to improve the state-of-the-art of the SpMV on Graphic Processing Units (GPUs) and a loop unrolling heuristic is introduced that optimizes theSpMV performance by selecting the best unrolling factor based on the warp workload.
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TSM2X: High-performance tall-and-skinny matrix–matrix multiplication on GPUs
TL;DR: This paper proposes two efficient algorithms---TSM2R and TSM2L---for two classes of tall-and-skinny matrix-matrix multiplications on GPUs that focus on optimizing linear algebra operation with at least one of the input matrices is tall- and- Skinny.
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Fireiron: A Data-Movement-Aware Scheduling Language for GPUs
Bastian Hagedorn,Archibald Samuel Elliott,Henrik Barthels,Rastislav Bodik,Vinod Grover +4 more
- 30 Sep 2020
TL;DR: This paper introduces Fireiron, a scheduling language aimed at performance experts that provides high-level abstractions for expressing GPU optimizations that are unavailable to compilers today and which so far must be written in assembly.
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SVM with CUDA accelerated kernels for big sparse problems
Krzysztof Sopyła,Paweł Drozda,Przemysław Górecki +2 more
- 29 Apr 2012
TL;DR: The following paper presents the CSR-GPU-SVM algorithm which accelerates SVM training for large and sparse problems with the use of the CUDA technology and allows us to perform efficient classification of big datasets, for which classification with dense representation is not possible.
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Performance portable GPU code generation for matrix multiplication
Toomas Remmelg,Thibaut Lutz,Michel Steuwer,Christophe Dubach +3 more
- 12 Mar 2016
TL;DR: This paper develops in a previous paper a functional data-parallel language which allows applications to be expressed in a device neutral way and produces high-performance OpenCL code for GPUs with a well-studied, well-understood application: matrix multiplication.