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
Enhancing data parallelism for Ant Colony Optimization on GPUs
TL;DR: This paper deals with a GPU implementation of Ant Colony Optimization (ACO), a population-based optimization method which comprises two major stages: tour construction and pheromone update, and proposes a new mechanism called I-Roulette to replicate the classic roulette wheel while improving GPU parallelism.
Batched one-sided factorizations of tiny matrices using GPUs: Challenges and countermeasures
TL;DR: This work presents GPU design and optimization techniques for high-performance batched one-sided factorizations of millions of tiny matrices (of size 32 and less), and quantifies the effects and relevance of different techniques in order to select the best-performing LU, QR, and Cholesky factorization designs.
Computing Strongly Connected Components in Parallel on CUDA
Jiri Barnat,Petr Bauch,Luboš Brim,Milan Ceka +3 more
- 16 May 2011
TL;DR: This paper designs a new CUDA-aware procedure for pivot selection and adapt selected parallel algorithms for CUDA accelerated computation and experimentally demonstrates that with a single GTX 480 GPU card, this paper can easily outperform the optimal serial CPU implementation by an order of magnitude.
Accelerating CUDA Graph Algorithms at Maximum Warp
Sungpack Hong,Sang Kyun Kim,Tayo Oguntebi,Kunle Olukotun +3 more
TL;DR: Researchers propose a virtual warp-centric programming method to accelerate CUDA graph algorithms, achieving up to 9x speedup on irregular graphs and 30% improvement on regular graphs, and also yielding significant speedups on GPU benchmark applications.
A large-scale cross-architecture evaluation of thread-coarsening
Alberto Magni,Christophe Dubach,Michael O'Boyle +2 more
- 17 Nov 2013
TL;DR: This paper considers a data parallel compiler transformation - thread-coarsening - and evaluates its effects across a range of devices by developing a source-to-source OpenCL compiler based on LLVM and uses statistical regression to analyse and explain program performance in terms of hardware-based performance counters.