Scaling physics and material science applications on a massively parallel Blue Gene/L system

Authors:
George Almasi;Gyan Bhanot;Alan Gara;Manish Gupta;James Sexton;Bob Walkup;Vasily V. Bulatov;Andrew W. Cook;Bronis R. de Supinski;James N. Glosli;Jeffrey A. Greenough;Francois Gygi;Alison Kubota;Steve Louis;Thomas E. Spelce;Frederick H. Streitz;Peter L. Williams;Robert K. Yates;Charles Archer;Jose Moreira;Charles Rendleman
Affiliations:
IBM Thomas J. Watson Research Center, Yorktown Heights, NY;IBM Thomas J. Watson Research Center, Yorktown Heights, NY;IBM Thomas J. Watson Research Center, Yorktown Heights, NY;IBM Thomas J. Watson Research Center, Yorktown Heights, NY;IBM Thomas J. Watson Research Center, Yorktown Heights, NY;IBM Thomas J. Watson Research Center, Yorktown Heights, NY;Lawrence Livermore National Laboratory, Livermore, CA;Lawrence Livermore National Laboratory, Livermore, CA;Lawrence Livermore National Laboratory, Livermore, CA;Lawrence Livermore National Laboratory, Livermore, CA;Lawrence Livermore National Laboratory, Livermore, CA;Lawrence Livermore National Laboratory, Livermore, CA;Lawrence Livermore National Laboratory, Livermore, CA;Lawrence Livermore National Laboratory, Livermore, CA;Lawrence Livermore National Laboratory, Livermore, CA;Lawrence Livermore National Laboratory, Livermore, CA;Lawrence Livermore National Laboratory, Livermore, CA;Lawrence Livermore National Laboratory, Livermore, CA;IBM Systems and Technology Group, Rochester, MN;IBM Systems and Technology Group, Rochester, MN;Lawrence Berkeley National Laboratory, Berkeley, CA
Venue:
Proceedings of the 19th annual international conference on Supercomputing
Year:
2005

Citing 8
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Abstract

Blue Gene/L represents a new way to build supercomputers, using a large number of low power processors, together with multiple integrated interconnection networks. Whether real applications can scale to tens of thousands of processors (on a machine like Blue Gene/L) has been an open question. In this paper, we describe early experience with several physics and material science applications on a 32,768 node Blue Gene/L system, which was installed recently at the Lawrence Livermore National Laboratory. Our study shows some problems in the applications and in the current software implementation, but overall, excellent scaling of these applications to 32K nodes on the current Blue Gene/L system. While there is clearly room for improvement, these results represent the first proof point that MPI applications can effectively scale to over ten thousand processors. They also validate the scalability of the hardware and software architecture of Blue Gene/L.