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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Solving knapsack problems on GPU
TL;DR: A parallel implementation via CUDA of the dynamic programming method for the knapsack problem on NVIDIA GPU and in order to limit the communication between the CPU and the GPU, a compression technique is presented which decreases significantly the memory occupancy.
Sparse matrix-matrix multiplication on modern architectures
Kiran Kumar Matam,Siva Rama Krishna Bharadwaj Indarapu,Kishore Kothapalli +2 more
- 01 Dec 2012
TL;DR: This work evaluates three possible variations of matrix multiplication (Row-Column, Column-Row, Row-Row) and performs suitable optimizations targeted at sparse matrices and presents heuristics to find the right amount of work division between the CPU and the GPU.
Optimizing Dynamic Programming on Graphics Processing Units via Adaptive Thread-Level Parallelism
Chao-Chin Wu,Jenn-Yang Ke,Heshan Lin,Wu-chun Feng +3 more
- 07 Dec 2011
TL;DR: This paper presents the GPU acceleration of an important category of DP problems called nonserial polyadic dynamic programming (NPDP), and proposes a methodology that can adaptively adjust the thread-level parallelism in mapping a NPDP problem onto the GPU, thus providing sufficient and steady degrees of parallelism across different compute stages.
Fast Multiplication in Binary Fields on GPUs via Register Cache
Eli Ben-Sasson,Matan Hamilis,Mark Silberstein,Eran Tromer +3 more
- 01 Jun 2016
TL;DR: This work devise a new parallel algorithm optimized for execution on GPUs that makes it possible to multiply large number of finite field elements, and achieves high performance via bit-slicing and fine-grained parallelization.
Why GPUs are Slow at Executing NFAs and How to Make them Faster
Hongyuan Liu,Sreepathi Pai,Adwait Jog +2 more
- 09 Mar 2020
TL;DR: A new dynamic scheme is proposed that effectively balances compute utilization with reduced memory usage and enable current GPUs to outperform the domain-specific accelerator for NFAs across several applications while performing within an order of magnitude for the rest of the applications.