GPUBlocks: GUI Programming Tool for CUDA and OpenCL

Recent advances in general-purpose graphics processing units (GPGPUs) have resulted in massively parallel hardware that is widely available to achieve high performance in desktop, notebook, and even mobile computer systems. While multicore technology has become the norm of modern computers, programm...

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Vydáno v:Journal of signal processing systems Ročník 91; číslo 3-4; s. 235 - 245
Hlavní autoři: Hwang, Yuan-Shin, Lin, Hsih-Hsin, Pai, Shen-Hung, Tu, Chia-Heng
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.03.2019
Springer Nature B.V
Témata:
Algorithms
Circuits and Systems
Computer Imaging
Domains
Electrical Engineering
Engineering
Graphical user interface
Graphics processing units
Handheld computers
Hardware
Image Processing and Computer Vision
Mobile computing
Optimization
Parallel programming
Pattern Recognition
Pattern Recognition and Graphics
Programmers
Programming environments
Prototyping
Signal,Image and Speech Processing
Vision
CUDA
Programming tool
GUI
OpenCL
Heterogeneous computing
GPGPU
ISSN:1939-8018, 1939-8115
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Popis
Shrnutí:Recent advances in general-purpose graphics processing units (GPGPUs) have resulted in massively parallel hardware that is widely available to achieve high performance in desktop, notebook, and even mobile computer systems. While multicore technology has become the norm of modern computers, programming such systems requires the understanding of underlying hardware architecture and hence posts a great challenge for average programmers, who might be professionals in specific domains, but not experts in parallel programming. This paper presents a GUI tool called GPUBlocks that can facilitate parallel programming on multicore computer systems. GPUBlocks is developed based on the OpenBlocks framework, an extendable tool for graphical programming, to construct the GUI-based programming environment for CUDA and OpenCL parallel computing platforms. Programmers simply need to drag-n-drop blocks, fill the fields of the blocks, and connect them according to array or matrix computations that are specified by algorithms. GPUBlocks can then translate block-based code to CUDA or OpenCL programs. Furthermore, a couple of optimization constructs have also been offered for rapid program optimization. Experimental results have shown that the generated CUDA and OpenCL programs can achieve reasonable speedups on GPUs. Consequently, GPUBlocks can be used as a tool for fast prototyping of GPU applications or a platform for educational parallel programming.
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ISSN:1939-8018
1939-8115
DOI:10.1007/s11265-018-1395-2