SC20 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Keynote: Michael Garland - Programming Systems of Data


Workshop:Machine Learning in HPC Environments

Authors: Michael Garland (Nvidia Corporation)


Abstract: Machine learning and data analysis thrive on mass quantities of data. At the same time, the cost of data distribution and movement is among the most critical factors determining the performance of applications at scale. Consequently, scalable high-performance machine learning and data analysis requires software environments that support the careful management of data. Whereas modern cloud systems provide data stores and services that help support efficient delivery of data to applications, the tools at hand for developers to efficiently manage distributed data within a running application are considerably more limited. It is particularly challenging to deliver high-performance execution across distributed nodes while maintaining software modularity and composability. In this talk, I will focus on developments in the design of scalable programming systems that help address these challenges by providing data-centric interfaces that provide a convenient notation to the developer and dynamic information to the runtime system tasked with scheduling the application at peak efficiency.


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