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Tracing and profiling machine learning dataflow applications on GPU

Pierre Zins and Michel Dagenais

Article (2019)

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Abstract

In this paper, we propose a profiling and tracing method for dataflow applications with GPU acceleration. Dataflow models can be represented by graphs and are widely used in many domains like signal processing or machine learning. Within the graph, the data flows along the edges, and the nodes correspond to the computing units that process the data. To accelerate the execution, some co-processing units, like GPUs, are often used for computing intensive nodes. The work in this paper aims at providing useful information about the execution of the dataflow graph on the available hardware, in order to understand and possibly improve the performance. The collected traces include low-level information about the CPU, from the Linux Kernel (system calls), as well as mid-level and high-level information respectively about intermediate libraries like CUDA, HIP or HSA, and the dataflow model. This is followed by post-mortem analysis and visualization steps in order to enhance the trace and show useful information to the user. To demonstrate the effectiveness of the method, it was evaluated for TensorFlow, a well-known machine learning library that uses a dataflow computational graph to represent the algorithms. We present a few examples of machine learning applications that can be optimized with the help of the information provided by our proposed method. For example, we reduce the execution time of a face recognition application by a factor of 5X. We suggest a better placement of the computation nodes on the available hardware components for a distributed application. Finally, we also enhance the memory management of an application to speed up the execution.

Subjects: 2700 Information technology > 2700 Information technology
2700 Information technology > 2706 Software engineering
2700 Information technology > 2715 Optimization
2800 Artificial intelligence > 2805 Learning and inference theories
Department: Department of Computer Engineering and Software Engineering
Funders: CRSNG/NSERC, Google, Ciena, EfficiOS, Prompt
Grant number: CRDPJ468687-14
PolyPublie URL: https://publications.polymtl.ca/4213/
Journal Title: International Journal of Parallel Programming (vol. 47, no. 5-6)
Publisher: Springer
DOI: 10.1007/s10766-019-00630-5
Official URL: https://doi.org/10.1007/s10766-019-00630-5
Date Deposited: 09 Mar 2020 12:52
Last Modified: 13 May 2023 19:51
Cite in APA 7: Zins, P., & Dagenais, M. (2019). Tracing and profiling machine learning dataflow applications on GPU. International Journal of Parallel Programming, 47(5-6), 973-1013. https://doi.org/10.1007/s10766-019-00630-5

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