Application performance evaluation using Deep Learning
CovenanteeBarcelona Supercomputing Center
Document typeMaster thesis
Rights accessOpen Access
Developing software for exascale systems will become even more challenging than for today’s systems. Methods for evaluating the performance of applications and identifying potential weaknesses are essential for reaching optimal performance. Though the tools available today are not widely used, and generally require some expert knowledge. In recent years different deep learning techniques have enjoyed great success in various fields, and especially in image recognition. Though it is still to find its way in to the area of application performance evaluation. This work will take the first step towards introducing deep learning to the area of HPC performance evaluation, opening the door for others. Convolutional neural networks will be fed images of timeline views of HPC applications and will identify the intrinsic behavior of the application and return some principal performance metrics. The results show that deep learning techniques indeed can be utilized for evaluating the performance of parallel applications, with the main limitation for its success being the sizes of the data sets available. Furthermore a number of exciting directions for taking the next step utilizing deep learning techniques with performance evaluation are suggested.
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