Implementing a Gaussian Process Learning Algorithm in Mixed Parallel Environment

Conference · Sat Jan 01 04:00:00 EST 2011

OSTI ID:1038517

Chandola, Varun ^[1]; Vatsavai, Raju ^[1]

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1. ORNL

In this paper, we present a scalability analysis of a parallel Gaussian process training algorithm to simultaneously analyze a massive number of time series. We study three different parallel implementations: using threads, MPI, and a hybrid implementation using threads and MPI. We compare the scalability for the multi-threaded implementation on three different hardware platforms: a Mac desktop with two quad-core Intel Xeon processors (16 virtual cores), a Linux cluster node with four quad-core 2.3 GHz AMD Opteron processors, and SGI Altix ICE 8200 cluster node with two quad-core Intel Xeon processors (16 virtual cores). We also study the scalability of the MPI based and the hybrid MPI and thread based implementations on the SGI cluster with 128 nodes (2048 cores). Experimental results show that the hybrid implementation scales better than the multi-threaded and MPI based implementations. The hybrid implementation, using 1536 cores, can analyze a remote sensing data set with over 4 million time series in nearly 5 seconds while the serial algorithm takes nearly 12 hours to process the same data set.

• Research Organization: Oak Ridge National Laboratory (ORNL)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1038517
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS
ALGORITHMS
IMPLEMENTATION
LEARNING
REMOTE SENSING
TRAINING