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
Design space exploration of the turbo decoding algorithm on GPUs
Dongwon Lee,Marilyn Wolf,Hyesoon Kim +2 more
- 24 Oct 2010
TL;DR: The GPU can be considered as another type of coprocessor for Turbo decoding implementations in mobile devices and find a performance bottleneck at the finally optimized case is global memory access latency.
A Note on Auto-tuning GEMM for GPUs
Yinan Li,Jack Dongarra,Stanimire Tomov +2 more
- 20 May 2009
TL;DR: Some GPU GEMM auto-tuning optimization techniques that allow the development of high performance dense linear algebra to keep up with changing hardware by rapidly reusing, rather than reinventing, the existing ideas are described.
Efficient 3D stencil computations using CUDA
Marcin Krotkiewski,Marcin Dabrowski +1 more
- 01 Oct 2013
TL;DR: It is demonstrated that in the implementation the memory overhead due to the halos is largely eliminated by good reuse of the halo data in the memory caches, and that the method of reading the data is close to optimal in terms of memory bandwidth usage.
Novel HPC techniques to batch execution of many variable size BLAS computations on GPUs
Ahmad Abdelfattah,Azzam Haidar,Stanimire Tomov,Jack Dongarra +3 more
- 14 Jun 2017
TL;DR: This paper presents a software framework for solving large numbers of relatively small matrix problems using GPUs that combines novel and existing HPC techniques to methodically apply performance analysis, kernel design, low-level optimizations, and autotuning to exceed in performance proprietary vendor libraries.
CUDASW++ 2.0: enhanced Smith-Waterman protein database search on CUDA-enabled GPUs based on SIMT and virtualized SIMD abstractions
TL;DR: The latest release of the CUDASW++ software is described, which makes new contributions to Smith-Waterman protein database searches using compute unified device architecture (CUDA) and a partitioned vectorized Smith- waterman algorithm using CUDA based on the virtualized single instruction, multiple data (SIMD) abstraction is investigated.