Type of Document Thesis Author Krishnan, Siddharth Author's Email Address firstname.lastname@example.org URN etd-08152011-233631 Title Dynamic Load Balancing for Peta-Scale Quantum Monte Carlo Applications Degree Master of Science Department Computer Science, Department of Advisory Committee
Advisor Name Title Ashok Srinivasan Committee Chair Xin Yuan Committee Member Piyush Kumar University Representative Keywords
- Dynamic Load Balancing
- Quantum Monte Carlo
- Paralle Computing
Date of Defense 2011-06-28 Availability unrestricted AbstractDiffusion Monte Carlo is the most popular Quantum Monte Carlo method used for obtaining accurate results. Unlike with simpler Monte Carlo techniques, load imbalance can be a significant factor affecting its performance on massively parallel machines. We propose a new dynamic load balancing technique and evaluate it theoretically and empirically. An important feature of this algorithm is that the load can be perfectly balanced with each process receiving at most one message. It is also optimal in the maximum size of messages received by any process.
We optimize its implementation to reduce network contention, and provide empirical results on
the peta-flop Jaguar supercomputer at ORNL showing up to 30% improvement in performance
on 120,000 cores com pared with existing methods for this problem . The contribution of this
work lies in proposing an efficient load balancing algorithm which can be used by applications
dealing with independent tasks requiring identical computational effort.
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