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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TL;DR: A comprehensive study establishes connections between programming models, architectures and applications using a two-level character recognition network and an architectural performance comparison of the SNN application running on Nvidia's Fermi and AMD/ATi's Radeon is done.
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Design and evaluation of a parallel k-nearest neighbor algorithm on CUDA-enabled GPU
Shenshen Liang,Ying Liu,Cheng Wang,Liheng Jian +3 more
- 21 Oct 2010
TL;DR: The experimental results demonstrate that CUKNN outperforms the serial KNN on an HP xw8600 workstation significantly, achieving up to 46.7IX speedup on the synthetic dataseis and 42.49X on the physical simulation dataset including I/O cost.
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Accelerating Protein Sequence Search in a Heterogeneous Computing System
Shucai Xiao,Heshan Lin,Wu-chun Feng +2 more
- 16 May 2011
TL;DR: An implementation of the BLAST algorithm for searching protein sequences in a heterogeneous computing system that delivers a seven-fold speedup over the sequential BLASTP for the most computationally intensive phase on a NVIDIA Fermi C2050 GPU.
A benchmark set of highly-efficient CUDA and OpenCL kernels and its dynamic autotuning with Kernel Tuning Toolkit
Filip Petrovič,David Střelák,David Střelák,Jana Hozzová,Jaroslav Ol’ha,Richard Trembecký,Siegfried Benkner,Jiří Filipovič,Jiří Filipovič +8 more
TL;DR: The Kernel Tuning Toolkit as discussed by the authors enables applications to re-tune performance-critical kernels at runtime whenever needed, for example, when input data changes, which is key to performance portability.
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Sampled Dense Matrix Multiplication for High-Performance Machine Learning
Israt Nisa,Aravind Sukumaran-Rajam,Sureyya Emre Kurt,Changwan Hong,P. Sadayappan +4 more
- 01 Dec 2018
TL;DR: The development of cuSDDMM, a multi-node GPU-accelerated implementation for Sampled Dense-Dense Matrix Multiplication improves significantly over the best currently available GPU implementation of SDDMM (in the BIDMach Machine Learning library).
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