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 Improved Magma Gemm For Fermi Graphics Processing Units
Rajib Nath,Stanimire Tomov,Jack Dongarra +2 more
- 01 Nov 2010
TL;DR: An improved matrix—matrix multiplication routine (General Matrix Multiply [GEMM]) in the MAGMA BLAS library that targets the NVIDIA Fermi graphics processing units (GPUs) using Compute Unified Data Architecture (CUDA).
An efficient implementation of Smith Waterman algorithm on GPU using CUDA, for massively parallel scanning of sequence databases
Lukasz Ligowski,Witold R. Rudnicki +1 more
- 23 May 2009
TL;DR: This work presents an efficient implementation of the Smith Waterman algorithm for sequence alignment on the Nvidia GPU, reaching more than 70% of theoretical hardware performance.
An Overview of Cache Optimization Techniques and Cache-Aware Numerical Algorithms
Markus Kowarschik,Christian Weiß +1 more
TL;DR: In this article, the authors focus on optimization techniques for enhancing cache performance by hiding both the low main memory bandwidth and the latency of main memory accesses which is slow in contrast to the floating-point performance of the CPUs.
180
Fast sparse matrix-vector multiplication on GPUs for graph applications
Arash Ashari,Naser Sedaghati,John Eisenlohr,Srinivasan Parthasarathy,P. Sadayappan +4 more
- 16 Nov 2014
TL;DR: ACSR is presented, an adaptive SpMV algorithm that uses the standard CSR format but reduces thread divergence by combining rows into groups which have a similar number of non-zero elements, and thus avoids significant preprocessing overheads.
174
Fast scan algorithms on graphics processors
Yuri Dotsenko,Naga K. Govindaraju,Peter-Pike Sloan,Charles N. Boyd,John L. Manferdelli +4 more
- 07 Jun 2008
TL;DR: This work uses novel data representations that map well to the GPU architecture to exploit shared memory to improve memory performance and improves the performance of algorithms for scan and segmented scan by eliminating shared-memory bank conflicts and reducing the overheads.