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
moDNN: Memory optimal DNN training on GPUs
Xiaoming Chen,Danny Z. Chen,Xiaobo Sharon Hu +2 more
- 19 Mar 2018
TL;DR: MoDNN as mentioned in this paper proposes a framework to optimize the memory usage in DNN training by taking full advantage of overlapping computations and data transfers, and develops heuristics to judiciously schedule data offloading and prefetching, together with training algorithm selection.
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Resource Conscious Reuse-Driven Tiling for GPUs
Prashant Singh Rawat,Changwan Hong,Mahesh Ravishankar,Vinod Grover,Louis-Noël Pouchet,Atanas Rountev,P. Sadayappan +6 more
- 11 Sep 2016
TL;DR: An effective resource-constraint-driven approach for automated GPU code generation for stencils is developed, and a fusion technique that judiciously fuses stencil computations to minimize data movement, while controlling computational redundancy and maximizing resource usage is presented.
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Optimizing SpMV for Diagonal Sparse Matrices on GPU
Xiangzheng Sun,Yunquan Zhang,Ting Wang,Xianyi Zhang,Liang Yuan,Li Rao +5 more
- 13 Sep 2011
TL;DR: This paper proposes a new storage format for diagonal sparse matrices, defined as Compressed Row Segment with Diagonal-pattern (CRSD), and designs diagonal patterns to represent the diagonal distribution in CRSD.
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DPF-ECC: Accelerating Elliptic Curve Cryptography with Floating-Point Computing Power of GPUs
Lili Gao,Fangyu Zheng,Niall Emmart,Jiankuo Dong,Jingqiang Lin,Charles C. Weems +5 more
- 18 May 2020
TL;DR: This contribution is the first to show that floating-point-based ECC implementations can outperform the integer-based ones by a huge margin, and the experimental result in Tesla P100 achieves over double performance of the existing fastest integer work on the same platform.
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Fast Two Dimensional Convex Hull on the GPU
Srikanth Srungarapu,Durga Prasad Reddy,Kishore Kothapalli,P. J. Narayanan +3 more
- 22 Mar 2011
TL;DR: This paper presents a GPU-optimized implementation for finding the convex hull of a two dimensional point set and achieves a speedup of up to 14 over the standard sequential CPU implementation.
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