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-SFFT: A GPU based parallel algorithm for computing the Sparse Fast Fourier Transform (SFFT) of k-sparse signals
Oswaldo Artiles,Fahad Saeed +1 more
- 01 Dec 2019
TL;DR: A highly scalable GPU-based parallel algorithm called GPU-SFFT for computing the SFFT of k-sparse signals, based on parallel optimizations that leads to enormous speedups and designed CPU-GPU specific optimizations lead to enormous decrease in the run times.
An Efficient CUDA Implementation of the Tree-Based Barnes Hut n-Body Algorithm
Martin Burtscher,Keshav Pingali +1 more
- 01 Jan 2011
TL;DR: This chapter describes the first CUDA implementation of the classical Barnes Hut n-body algorithm that runs entirely on the GPU, concluding that GPUs can be used to accelerate irregular codes, not just regular codes.
A new approach for sparse matrix vector product on NVIDIA GPUs
TL;DR: This work proposes and evaluates a new implementation of SpMV for NVIDIA GPUs based on a new format, ELLPACK‐R, that allows storage of the sparse matrix in a regular manner and shows that significant speedup factors are achieved with GPUs.
Performance evaluation of kernel fusion BLAS routines on the GPU: iterative solvers as case study
TL;DR: This paper provides an extensive analysis of the impact of fusing vector operations [level 1 of Basic Linear Algebra Subprograms (BLAS)] on the performance of the GPU and shows that this optimization provides noticeable improvement especially for kernels with lower memory requirements and on more modern GPUs.
Accelerating explicit ODE methods on GPUs by kernel fusion
Matthias Korch,Tim Werner +1 more
TL;DR: In this paper, the authors generalize the description of explicit ODE methods by using data flow graphs consisting of basic operations that are suitable to cover the types of computations occurring in all common explicit methods.