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
A Strategy for Automatic Performance Tuning of Stencil Computations on GPUs
TL;DR: A strategy that uses machine learning to determine the best way to use memory followed by a heuristic that divides the remaining optimizations into groups and exhaustively explores one group at a time achieves a reduction in the number of configurations explored and finds better performing configurations.
•Posted Content
KBLAS: An Optimized Library for Dense Matrix-Vector Multiplication on GPU Accelerators
TL;DR: Considering symmetric and Hermitian matrices, the KBLAS performance outperforms existing state-of-the-art implementations on all matrix sizes and achieves asymptotically up to 50p and 60p speedup against the best competitor on single GPU and multi-GPUs systems, respectively.
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Optimizing Krylov Subspace Solvers on Graphics Processing Units
Hartwig Anzt,William Sawyer,Stanimire Tomov,Piotr Luszczek,Ichitaro Yamazaki,Jack Dongarra +5 more
- 19 May 2014
TL;DR: This paper targets the acceleration of the BiCGSTAB solver for GPUs, showing that significant improvement can be achieved by reformulating the method and developing application-specific kernels instead of using the generic CUBLAS library provided by NVIDIA.
GPU-accelerated 3-D Finite Volume Particle Method
Siamak Alimirzazadeh,Ebrahim Jahanbakhsh,Ebrahim Jahanbakhsh,Audrey Maertens,Sebastián Leguizamón,François Avellan +5 more
TL;DR: Algorithms and optimization procedures that allowed to significantly accelerate computations by taking advantage of the computational power of Graphics Processing Units (GPUs) are presented and a case study of water jet deviation by the rotating buckets in a Pelton turbine is presented.
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Reducing thread divergence in GPU-based bees swarm optimization applied to association rule mining
TL;DR: Three approaches based on database reorganization are proposed, aiming to reduce thread divergence in GPU‐based bees swarm optimization metaheuristic for ARM, respectively, named block‐based reordering, transactions‐basedReordering, and transactions‐ based reordering with median value.
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