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 Implementation of the Bellman-Ford Algorithm for Kepler GPU Architectures
Federico Busato,Nicola Bombieri +1 more
TL;DR: A parallel implementation of the Bellman-Ford algorithm that exploits the architectural characteristics of recent GPU architectures (i.e., NVIDIA Kepler, Maxwell) to improve both performance and work efficiency is presented.
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CLTune: A Generic Auto-Tuner for OpenCL Kernels
Cedric Nugteren,Valeriu Codreanu +1 more
TL;DR: CLTune as discussed by the authors is an auto-tuner for OpenCL kernels that evaluates and tunes kernel performance of a generic, user-defined search space of possible parameter-value combinations.
84
TriCore: parallel triangle counting on GPUs
Yang Hu,Hang Liu,H. Howie Huang +2 more
- 11 Nov 2018
TL;DR: TriCore, a scalable GPU-based triangle counting system that consists of three major techniques, design a binary search based algorithm that can increase both the thread parallelism and memory performance on Graphics Processing Units (GPUs), and develops a dynamic workload management technique to balance the workload across GPUs.
81
Demystifying Tensor Cores to Optimize Half-Precision Matrix Multiply
Da Yan,Wei Wang,Xiaowen Chu +2 more
- 18 May 2020
TL;DR: This paper demystify how Tensor Cores on NVIDIA Turing architecture work in great details, including the instructions used, the registers and data layout required, as well as the throughput and latency of Tensor Core operations.
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Atomic-free irregular computations on GPUs
Rupesh Nasre,Martin Burtscher,Keshav Pingali +2 more
- 16 Mar 2013
TL;DR: This paper presents two high-level methods to eliminate atomics in irregular programs by exploiting algebraic properties of algorithms to elide atomics, and illustrates the generality of the two methods by applying them to five irregular graph applications.