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
An Efficient GPU General Sparse Matrix-Matrix Multiplication for Irregular Data
Weifeng Liu,Brian Vinter +1 more
- 19 May 2014
TL;DR: This work presents a GPU SpGEMM algorithm that particularly focuses on load balancing, memory pre-allocation for the result matrix, and parallel insert operations of the nonzero entries that is experimentally found to be the fastest GPU merge approach.
Parallel Ant Colony Optimization on Graphics Processing Units
TL;DR: A comparative experimental study highlights the performance impact of ACO parameters, GPU technical configuration, memory structures and parallelization granularity on a state-of-the-art Fermi GPU architecture.
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Program optimization carving for GPU computing
Shane Ryoo,Christopher I. Rodrigues,Sam S. Stone,John A. Stratton,Sain-Zee Ueng,Sara S. Baghsorkhi,Wen-mei W. Hwu +6 more
TL;DR: This work proposes program optimization carving, a technique that begins with a complete optimization space and prunes it down to a set of configurations that are likely to contain the global maximum, and shows that this approach is significantly superior to random sampling of the search space.
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Task management for irregular-parallel workloads on the GPU
Stanley Tzeng,Anjul Patney,John D. Owens +2 more
- 25 Jun 2010
TL;DR: It is demonstrated that dynamic scheduling and efficient memory management are critical problems in achieving high efficiency on irregular workloads and the preferred choice is task-donation because of comparable performance to task-stealing while using less memory overhead.
A unified sparse matrix data format for efficient general sparse matrix-vector multiply on modern processors with wide SIMD units
TL;DR: In this article, the authors proposed SELL-C-sigma, a SIMD-friendly data format which combines long-standing ideas from General Purpose Graphics Processing Units (GPGPUs) and vector computer programming.
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