Visual Tool for Learning GPU Programming
Visual Tool for Learning GPU Programming

Summary/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.

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