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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A Fast Parallel Selection Algorithm on GPUs
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Improving Performance and Energy Efficiency of Geophysics Applications on GPU Architectures
Pablo José Pavan,Matheus da Silva Serpa,Emmanuell D. Carreno,Víctor Martínez,Edson Luiz Padoin,Philippe O. A. Navaux,Jairo Panetta,Jean-François Méhaut +7 more
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TL;DR: This paper optimized two option pricing algorithms on the APU making use of vectorization and loop unrolling for improved performance and study the power consumption of these architectures to determine how they compare to traditional CPU- and GPU- based systems.
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Fast Implementation of General Matrix-Vector Multiplication (GEMV) on Kepler GPUs
Daichi Mukunoki,Toshiyuki Imamura,Daisuke Takahashi +2 more
- 04 Mar 2015
TL;DR: A fast implementation method for the general matrix-vector multiplication (GEMV) routine, which is one of the level-2 Basic Linear Algebra Subprograms (BLAS) subroutines, for a column-major and non-transposed matrix on NVIDIA Kepler architecture graphics processing units (GPUs).
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