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 multisplit
Saman Ashkiani,Andrew Davidson,Ulrich Meyer,John D. Owens +3 more
- 27 Feb 2016
TL;DR: This work provides a parallel model and multiple implementations for the multisplit problem, and uses warp-synchronous programming models to avoid branch divergence and reduce memory usage, as well as hierarchical reordering of input elements to achieve better coalescing of global memory accesses.
Designing efficient sorting algorithms for manycore GPUs
Nadathur Satish,Mark J. Harris,Michael Garland +2 more
- 23 May 2009
TL;DR: The design of high-performance parallel radix sort and merge sort routines for manycore GPUs, taking advantage of the full programmability offered by CUDA, are described, which are the fastest GPU sort and the fastest comparison-based sort reported in the literature.
Implementing Smith-Waterman Algorithm with Two-Dimensional Cache on GPUs
Xiaowen Feng,Hai Jin,Ran Zheng,Zhiyuan Shao,Lei Zhu +4 more
- 01 Nov 2012
TL;DR: A new method to implement Smith-Waterman algorithm with two-dimensional cache is proposed, which aims at accelerating the first stage of Smith- waterman algorithm and coalesced writing back the corresponding results to GPU global memory.
High Performance Direct Gravitational N-body Simulations on Graphics Processing Units
Simon Portegies Zwart,Robert G. Belleman,Peter M. Geldof +2 more
TL;DR: Direct N-body simulations on NVIDIA GPUs outperform GRAPE-6Af special purpose hardware for large particle numbers (N ≤ 10^4), offering a low-cost alternative with comparable scaling and energy conservation, despite single-precision calculations.
Model-driven autotuning of sparse matrix-vector multiply on GPUs
Jee Choi,Amik Singh,Richard Vuduc +2 more
- 09 Jan 2010
TL;DR: A performance model-driven framework for automated performance tuning (autotuning) of sparse matrix-vector multiply (SpMV) on systems accelerated by graphics processing units (GPU) and shows that the model can identify the implementations that achieve within 15% of those found through exhaustive search.