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Wednesday, April 15 • 10:00am - 10:50am
Hadoop Applications on High Performance Computing(HPC) - Devaraj Kavali, Intel

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High performance workloads have expanded. Today’s HPC users are demanding application frameworks to analyze vast amounts of data created by complex simulations. As the most widely deployed file system for HPC, Lustre software can play a critical role for these data-intensive applications. HPC Adapter for Lustre(HAL) provides adaptability to run Hadoop File System operations on HPC environment without any changes to applications. HPC Adapter for Mapreduce/Yarn(HAM) allows users to run their MapReduce/Yarn applications—without changes—directly on shared, fast, Lustre-powered storage. This optimizes the performance of MapReduce/Yarn tasks, while delivering faster, more scalable, easier-to-manage storage. This session explains the architecture and design level technical details to run Mapreduce and Yarn applications on HPC native schedulers like Slurm, MOAB, etc.

avatar for Devaraj Kavali

Devaraj Kavali

Intel Corporation
Devaraj Kavali is an Apache Hadoop Committer and contributor to the Hadoop Yarn & Mapreduce. He is currently working with Intel Corporation. He has been working on various distributed platforms/applications for more than 8 years.

Wednesday April 15, 2015 10:00am - 10:50am CDT
Texas II

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