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
Optimal loop unrolling for GPGPU programs
Giridhar Sreenivasa Murthy,Mahesh Ravishankar,Muthu Baskaran,P. Sadayappan +3 more
- 19 Apr 2010
TL;DR: This paper develops a semi-automatic, compile-time approach for identifying optimal unroll factors for suitable loops in GPGPU programs and proposes techniques for reducing the number of un roll factors evaluated, based on the characteristics of the program being compiled and the device being compiled to.
Optimizing CUDA code by kernel fusion: application on BLAS
TL;DR: This paper shows how kernels performing map, reduce or their nested combinations can be fused automatically by the source-to-source compiler and shows how the performance of their sequences can be improved by fusions.
73
An efficient GPU-based parallel tabu search algorithm for hardware/software co-design
TL;DR: Experiments show that GPTS outperforms state-of-the-art work of tabu search and is competitive with other methods for HW/SW partitioning.
Branch and data herding: reducing control and memory divergence for error-tolerant GPU applications
John Sartori,Rakesh Kumar +1 more
- 19 Sep 2012
TL;DR: This paper proposes a static analysis and compiler framework to prevent exceptions when control and data errors are introduced, a profiling framework that aims to maximize performance while maintaining acceptable output quality, and hardware optimizations to improve the performance benefits of exploiting error tolerance through branch and data herding.
Branch and Data Herding: Reducing Control and Memory Divergence for Error-Tolerant GPU Applications
John Sartori,Rakesh Kumar +1 more
TL;DR: This paper proposes a static analysis and compiler framework to prevent exceptions when control and data errors are introduced, a profiling framework that aims to maximize performance while maintaining acceptable output quality, and hardware optimizations to improve the performance benefits of exploiting error tolerance through branch and data herding.
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