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

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dc.contributor.authorGuignard, Mauricio
dc.contributor.authorSchild, Marcelo
dc.contributor.authorBederián, Carlos S.
dc.contributor.authorWolovick, Nicolás
dc.contributor.authorVega, Augusto J.
dc.date.accessioned2017-12-28T02:19:13Z
dc.date.available2017-12-28T02:19:13Z
dc.date.issued2018-01-03
dc.description.abstractDeep 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.
dc.format.extent8 pages
dc.identifier.doi10.24251/HICSS.2018.702
dc.identifier.isbn978-0-9981331-1-9
dc.identifier.urihttp://hdl.handle.net/10125/50591
dc.language.isoeng
dc.relation.ispartofProceedings of the 51st Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectFrontiers in AI and Software Engineering
dc.subjectDNN, Fathom Workload, IBM Minsky, performance characterization.
dc.titlePerformance Characterization of State-Of-The-Art Deep Learning Workloads on an IBM "Minsky" Platform
dc.typeConference Paper
dc.type.dcmiText

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