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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Published in:Journal of signal processing systems Vol. 91; no. 3-4; pp. 235 - 245
Main Authors: Hwang, Yuan-Shin, Lin, Hsih-Hsin, Pai, Shen-Hung, Tu, Chia-Heng
Format: Journal Article
Language:English
Published: New York Springer US 01.03.2019
Springer Nature B.V
Subjects:
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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Abstract 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.
AbstractList 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.
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.
Author Pai, Shen-Hung
Lin, Hsih-Hsin
Tu, Chia-Heng
Hwang, Yuan-Shin
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  organization: Department of Computer Science and Information Engineering, National Cheng Kung University
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SubjectTerms 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
Title GPUBlocks: GUI Programming Tool for CUDA and OpenCL
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