Platform Agnostic Streaming Data Application Performance Models

Workshop:RSDHA: Redefining Scalability for Diversely Heterogeneous Architectures

Authors: Clayton Faber, Tom Plano, Samatha Kodali, Zhili Xiao, Abhishek Dwaraki, Jeremy Buhler, and Roger Chamberlain (Washington University in St. Louis) and Anthony Cabrera (Oak Ridge National Laboratory (ORNL))

Abstract: The mapping of computational needs onto execution resources is, by and large, a manual task, and users are frequently guided simply by intuition and past experiences. We present a queueing theory based performance model for streaming data applications that takes steps towards a better understanding of resource mapping decisions, thereby assisting application developers to make good mapping choices. The performance model (and associated cost model) are agnostic to the specific properties of the compute resource and application, simply characterizing them by their achievable data throughput. We illustrate the model with a pair of applications, one chosen from the field of computational biology and the second is a classic machine learning problem.

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