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
Adaptive Optimization $$l_1$$l1-Minimization Solvers on GPU
TL;DR: This work proposes a novel warp-based implementation of the matrix-vector multiplication (Ax) on the graphics processing unit (GPU), called the GEMV kernel, and a novel thread- based implementation ofThe matrix- vector multiplication ($A^Tx$$ATx)on the GPU, called theGEMV-T kernel.
Reducing thread divergence in a GPU-accelerated branch-and-bound algorithm
TL;DR: The design and implementation of graphical processing unit (GPU)‐accelerated branch‐and‐bound algorithms (B&B) for solving flow‐shop scheduling optimization problems (FSP) dealing with thread divergence is addressed.
Communication Optimization on GPU: A Case Study of Sequence Alignment Algorithms
Jie Wang,Xinfeng Xie,Jason Cong +2 more
- 01 May 2017
TL;DR: This work deploys register shuffle in the application domain of sequence alignment (or similarly, string matching), and conducts a quantitative analysis of the opportunities and limitations of using register shuffle, which provides valuable insights for CUDA programmers into how to best use shuffle instructions for performance optimization.
Optimising lossless stages in a GPU-based MPEG encoder
TL;DR: The experiments show that optimising the amount of data transferred from GPU to CPU implementing the last sequential compression steps in the GPU, and using a parallel fast scan implementation of the zigzag scanning improve the overall performance of the system.
DeftNN: addressing bottlenecks for DNN execution on GPUs via synapse vector elimination and near-compute data fission
Parker Hill,Animesh Jain,Mason Hill,Babak Zamirai,Chang-Hong Hsu,Michael A. Laurenzano,Scott Mahlke,Lingjia Tang,Jason Mars +8 more
- 14 Oct 2017
TL;DR: DeftNN is a GPU DNN execution framework that targets the key architectural bottlenecks of DNNs on GPUs to automatically and transparently improve execution performance, and is composed of two novel optimization techniques– synapse vector elimination, a technique that identifies non-contributing synapses in the DNN and carefully transforms data and removes the computation and data movement.