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
Optimization principles and application performance evaluation of a multithreaded GPU using CUDA
Shane Ryoo,Christopher I. Rodrigues,Sara S. Baghsorkhi,Sam S. Stone,David B. Kirk,Wen-mei W. Hwu +5 more
- 20 Feb 2008
TL;DR: This work discusses the GeForce 8800 GTX processor's organization, features, and generalized optimization strategies, and achieves increased performance by reordering accesses to off-chip memory to combine requests to the same or contiguous memory locations and apply classical optimizations to reduce the number of executed operations.
Implementing sparse matrix-vector multiplication on throughput-oriented processors
Nathan Bell,Michael Garland +1 more
- 14 Nov 2009
TL;DR: This work explores SpMV methods that are well-suited to throughput-oriented architectures like the GPU and which exploit several common sparsity classes, including structured grid and unstructured mesh matrices.
Compiler transformations for high-performance computing
TL;DR: This survey is a comprehensive overview of the important high-level program restructuring techniques for imperative languages, such as C and Fortran, and describes the purpose of each transformation, how to determine if it is legal, and an example of its application.
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Benchmarking GPUs to tune dense linear algebra
TL;DR: It is argued that modern GPUs should be viewed as multithreaded multicore vector units and exploit blocking similarly to vector computers and heterogeneity of the system by computing both on GPU and CPU.
Scan primitives for GPU computing
Shubhabrata Sengupta,Mark J. Harris,Yao Zhang,John D. Owens +3 more
- 04 Aug 2007
TL;DR: Using the scan primitives, this work shows novel GPU implementations of quicksort and sparse matrix-vector multiply, and analyzes the performance of the scanPrimitives, several sort algorithms that use the scan Primitives, and a graphical shallow-water fluid simulation using the scan framework for a tridiagonal matrix solver.