A survey of architectural approaches for improving GPGPU performance, programmability and heterogeneity

Khairy, Mahmoud; Wassal, Amr G.; Zahran, Mohamed

doi:10.1016/j.jpdc.2018.11.012

Cited by 18 publications

(8 citation statements)

References 119 publications

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“…Together, the sets V , F and S constitute a signature = (F , V , S, arity, st, ar). 1 The set of terms T over a signature is inductively defined as the smallest set satisfying: A Many-sorted Term Rewrite System (MTRS) over a signature is a set of rules. Each rule is a pair of terms from T , namely a left-hand side and a right-hand side.…”

Section: Many-sorted Term Rewritingmentioning

confidence: 99%

“…Graphics Processing Units (GPUs) have enormous computational power and performance-per-watt compared to (multicore) CPUs [1]. GPUs are optimised for the highly parallel and regular computations that occur in graphics processing, but they become more and more interesting for general purpose computations (for instance, see [2][3][4][5][6][7]).…”

Section: Introductionmentioning

confidence: 99%

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Innermost many-sorted term rewriting on GPUs

Eerd

Groote

Hijma

et al. 2023

Science of Computer Programming

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Section: Many-sorted Term Rewritingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Innermost many-sorted term rewriting on GPUs

Eerd

Groote

Hijma

et al. 2023

Science of Computer Programming

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“…However, it was soon observed that the computing power of a GPU was not fully utilized at all times, depending on what has been displayed on the device. This resulted in general-purpose computing on graphics processing units (GPGPU), allowing computing tasks that are normally carried out on a CPU to be performed on the GPU [ 57 ]. Given that images contain a large number of pixels, and most image processing techniques require a repetition of operations on some or all of these pixels, a GPU is a good candidate for handling image processing tasks.…”

Section: Mobile Devices As Image Processorsmentioning

confidence: 99%

Image and video processing on mobile devices: a survey

Morikawa¹,

Kobayashi²,

Satoh³

et al. 2021

Vis Comput

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Image processing and computer vision on mobile devices have a wide range of applications such as digital image enhancement and augmented reality. While images acquired by cameras on mobile devices can be processed with generic image processing algorithms, there are numerous constraints and external issues that call for customized algorithms for such devices. In this paper, we survey mobile image processing and computer vision applications while highlighting these constraints and explaining how the algorithms have been modified/adapted to meet accuracy and performance demands. We hope that this paper will be a useful resource for researchers who intend to apply image processing and computer vision algorithms to real-world scenarios and applications that involve mobile devices.

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“…Nowadays, as GPUs show more and more powerful performance in terms of massively parallel computing [1], heterogeneous CPU-GPU architecture has become the current mainstream architecture. However, this structure also faces many challenges [2].…”

Section: Introductionmentioning

confidence: 99%

Resource Scheduling Strategy for Performance Optimization Based on Heterogeneous CPU-GPU Platform

Fang¹,

Zhou²,

Zhang³

et al. 2022

Computers, Materials &Amp; Continua

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In recent years, with the development of processor architecture, heterogeneous processors including Center processing unit (CPU) and Graphics processing unit (GPU) have become the mainstream. However, due to the differences of heterogeneous core, the heterogeneous system is now facing many problems that need to be solved. In order to solve these problems, this paper try to focus on the utilization and efficiency of heterogeneous core and design some reasonable resource scheduling strategies. To improve the performance of the system, this paper proposes a combination strategy for a single task and a multi-task scheduling strategy for multiple tasks. The combination strategy consists of two sub-strategies, the first strategy improves the execution efficiency of tasks on the GPU by changing the thread organization structure. The second focuses on the working state of the efficient core and develops more reasonable workload balancing schemes to improve resource utilization of heterogeneous systems. The multi-task scheduling strategy obtains the execution efficiency of heterogeneous cores and global task information through the processing of task samples. Based on this information, an improved ant colony algorithm is used to quickly obtain a reasonable task allocation scheme, which fully utilizes the characteristics of heterogeneous cores. The experimental results show that the combination strategy reduces task execution time by 29.13% on average. In the case of processing multiple tasks, the multi-task scheduling strategy reduces the execution time by up to 23.38% based on the combined strategy. Both strategies can make better use of the resources of heterogeneous systems and significantly reduce the execution time of tasks on heterogeneous systems.

show abstract

A survey of architectural approaches for improving GPGPU performance, programmability and heterogeneity

Cited by 18 publications

References 119 publications

Innermost many-sorted term rewriting on GPUs

Innermost many-sorted term rewriting on GPUs

Image and video processing on mobile devices: a survey

Resource Scheduling Strategy for Performance Optimization Based on Heterogeneous CPU-GPU Platform

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