Copertina

Visual Tool for Learning GPU Programming

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Pubblicato in:The International Scientific Conference eLearning and Software for Education vol. 1 (2019), p. 429
Autore principale: Vulcan, Alexandru Mihai
Altri autori: Nicolaie, Maximilian, Pietraru, Radu Nicolae
Pubblicazione:
"Carol I" National Defence University
Soggetti:
International conferences
Data centers
Parallel processing
Software
Students
Computer science
Curricula
Graphics processing units
Multimedia
Microprocessors
Science education
Cloud computing
Programmers
Programming
Algorithms
Learning curves
Machine learning
Slopes
Domains
Employment
Manufacturing
Student Needs
Architecture
Learning Processes
Mathematics
Visual Aids
Science Curriculum
Computer Software
Libraries
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Abstract:Graphic Processing Units (GPUs) are unanimously considered as powerful computational resources. General-purpose computing on GPU (GPGPU), as well, is the de facto infrastructure for most of the today computationally intensive problems that researchers all over the globe dill with. High Performance Computing (HPC) facilities use state of the art GPUs. Many domains like deep learning, machine learning, and computational finance uses GPU's for decreasing the execution time. GPUs are widely used in data centers for high performance computing where virtualization techniques are intended for optimizing the resource utilization (e.g. GPU cloud computing). The GPU programming model requires for all the data to be stored in a global memory before it is used. This limits the dimension of the problem a GPU can handle. A system utilizing a cluster of GPU would have a bigger level of parallelization but also would eliminate the memory limitation imposed by a single GPU. These being just a few of the problems a programmer needs to handle. However, the ratio between specialists that are able to efficiently program such processors and the rest of programmers is very small. One important reason for this situation is the steepness of the GPU programming learning curve due to the complex parallel architecture of the processor. Therefore, the tool presented in this article aims to provide visual support for a better understanding of the execution on GPU. With it, the programmers can easily observe the trace of the parallel execution on their own algorithm and, from that, they could determine the unused GPU capacity that could be better exploited.
DOI:10.12753/2066-026X-19-057
Fonte:Education Database