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 techniques for sparse matrix-vector multiplication on GPUs
TL;DR: This paper proposes an efficient data structure named AdELL+ for optimizing the SpMV kernel on GPUs, focusing on performance bottlenecks of sparse computation, and includes a warp-balancing heuristic and auto-tuning approach into a cohesive framework.
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Benchmarking the GPU memory at the warp level
Minquan Fang,Jianbin Fang,Weimin Zhang,Haifang Zhou,Jianxing Liao,Yuangang Wang +5 more
- 01 Jan 2018
TL;DR: This work discloses the characteristics of GPU memories at the warp-level, and leads to optimization guidelines, and summarizes the optimization guidelines for different types of memories, and builds an optimization framework on GPU memories.
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Efficient CPU-GPU cooperative computing for solving the subset-sum problem
TL;DR: An efficient CPU‐GPU cooperative computing scheme for solving the subset‐sum problem is proposed, which enables the full utilization of all the computing power of both CPUs and GPUs and achieves a significant performance benefit over the CPU‐only or GPU‐only computing.
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JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure
TL;DR: Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure, which enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.
Predicting an Optimal Sparse Matrix Format for SpMV Computation on GPU
B. Neelima,G. Ram Mohana Reddy,Prakash S. Raghavendra +2 more
- 19 May 2014
TL;DR: This paper proposes a method to chose an optimal sparse matrix format, focusing on the applications where CPU to GPU communication time and pre-processing time are nontrivial, and results show that the predicted format matches with that of the actual high performing format.
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