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
Efficient Sparse-Dense Matrix-Matrix Multiplication on GPUs Using the Customized Sparse Storage Format
Shaohuai Shi,Qiang Wang,Xiaowen Chu +2 more
- 01 Dec 2020
TL;DR: GCOOSpDM as mentioned in this paper exploits coalescent global memory access, fast shared memory reuse, and more operations per byte of global memory traffic to optimize the performance of SpDM on modern GPUs.
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Improving performance of GPU code using novel features of the NVIDIA kepler architecture
TL;DR: A judicious use of these two techniques, eliminating repeated operations and synchronizations, results in significantly better performance, and a technique to trade off the allocation of various device resources to find the parameters that offer the best performance is presented.
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GPU code optimization using abstract kernel emulation and sensitivity analysis
Changwan Hong,Aravind Sukumaran-Rajam,Jinsung Kim,Prashant Singh Rawat,Sriram Krishnamoorthy,Louis-Noël Pouchet,Fabrice Rastello,P. Sadayappan +7 more
- 11 Jun 2018
TL;DR: An approach to GPU kernel optimization is developed by focusing on identification of bottleneck resources and determining optimization parameters that can alleviate the bottleneck.
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A Performance Study of CUDA UVM versus Manual Optimizations in a Real-World Setup: Application to a Monte Carlo Wave-Particle Event-Based Interaction Model
TL;DR: The performance of a Monte Carlo model for the simulation of electromagnetic wave propagation in particle-filled atmospheres has been conducted for different CUDA versions and design approaches, showing a high degree of parallelism which allows favorable implementation in a GPU.
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GPU Multisplit: an extended study of a parallel algorithm
TL;DR: This work provides a parallel model and multiple implementations for the multisplit problem, and achieves comparable performance to the fastest GPU sort routines, sorting 32-bit keys and key-value pairs with a throughput of 3.0Gkeys/s and 2.1Gpair/s.
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