Paper Info
Title
Performance analysis of emerging data analytics and HPC workloads.
Abstract
Supercomputers are increasingly being used to run a data analytics workload in addition to a traditional simulation science workload. This mixed workload must be rigorously characterized to ensure that appropriately balanced machines are deployed. In this paper we analyze a suite of applications representing the simulation science and data workload at the NERSC supercomputing center. We show how time is spent in application compute, library compute, communication and I/O, and present application performance on both the Intel Xeon and Intel Xeon-Phi partitions of the Cori supercomputer. We find commonality in the libraries used, I/O motifs and methods of parallelism, and obtain similar node-to-node performance for the base application configurations. We demonstrate that features of the Intel Xeon-Phi node architecture and a Burst Buffer can improve application performance, providing evidence that an exascale-era energy-efficient platform can support a mixed workload.
Year
DOI
Venue
2017
10.1145/3149393.3149400
SC '17: The International Conference for High Performance Computing, Networking, Storage and Analysis Denver Colorado November, 2017
Keywords
DocType
ISBN
Workload characteristics, data analytics, big data, high performance computing
Conference
978-1-4503-5134-8
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Chris Daley1315.02
Prabhat245634.79
Sudip S. Dosanjh300.34
Nicholas J. Wright440827.79