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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Zhuoqian Li,Yonggang Che +1 more
- 27 Jun 2020
TL;DR: A packing/unpacking based method to decrease the volume of data transferred and the number of data transfer, and use the Pinned memory to improve data transfer rate is designed to reduce the data transfer overhead between the CPU and GPU and the MPI communication overhead.
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Improving ODE Integration on Graphics Processing Units by Reducing Thread Divergence
Thomas Kovac,Tom Haber,Frank Van Reeth,Niel Hens +3 more
- 12 Jun 2019
TL;DR: A general-purpose integrator that runs massively parallel on graphics processing units by minimizing thread divergence and bundling similar tasks using linear regression, execution time can be reduced by 40–80% when compared to a naive GPU implementation.
1
Performance Optimization Strategies of High Performance Computing on GPU
TL;DR: This work analyzes some key performance characters of GPU in detail, and presents three performance optimization strategies: Prefetching, Streamlizing, and Task Division.
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Performance Modeling of Atomic Additions on GPU Scratchpad Memory
Juan Gómez-Luna,José María González-Linares,José Ignacio Benavides Benítez,Nicolás Guil Mata +3 more
TL;DR: This paper presents an exhaustive microbenchmark-based analysis of atomic additions in shared memory that quantifies the impact of access conflicts on latency and throughput and proposes a performance model to estimate the latency penalties due to collisions by position or bank conflicts.
Improving GPGPU Concurrency with Elastic Kernels
Sreepathi Pai,Matthew J. Thazhuthaveetil,R. Govindarajan +2 more
TL;DR: Researchers propose elastic kernels to improve GPGPU concurrency, allowing fine-grained control over resource allocation, and evaluate their approach on NVIDIA Fermi GPUs, achieving 1.21x increase in system throughput and 3.73x improvement in turnaround time.