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Performance Characterization of State-Of-The-Art Deep Learning Workloads on an IBM "Minsky" Platform

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Title: Performance Characterization of State-Of-The-Art Deep Learning Workloads on an IBM "Minsky" Platform
Authors: Guignard, Mauricio
Schild, Marcelo
Bederián, Carlos S.
Wolovick, Nicolás
Vega, Augusto J.
Keywords: Frontiers in AI and Software Engineering
DNN, Fathom Workload, IBM Minsky, performance characterization.
Issue Date: 03 Jan 2018
Abstract: Deep learning algorithms are known to demand significant computing horsepower, in particular when it comes to training these models. The capability of developing new algorithms and improving the existing ones is in part determined by the speed at which these models can be trained and tested. One alternative to attain significant performance gains is through hardware acceleration. However, deep learning has evolved into a large variety of models, including but not limited to fully-connected, convolutional, recurrent and memory networks. Therefore, it appears difficult that a single solution can provide effective acceleration for this entire deep learning ecosystem. This work presents detailed characterization results of a set of archetypal state-of-the-art deep learning workloads on a last-generation IBM POWER8 system with NVIDIA Tesla P100 GPUs and NVLink interconnects. The goal is to identify the performance bottlenecks (i.e. the accelerable portions) to provide a thorough study that can guide the design of prospective acceleration platforms in a more effective manner. In addition, we analyze the role of the GPU (as one particular type of acceleration engine) and its effectiveness as a function of the size of the problem.
Pages/Duration: 8 pages
ISBN: 978-0-9981331-1-9
DOI: 10.24251/HICSS.2018.702
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Frontiers in AI and Software Engineering

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