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Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

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Parallel Discrete Molecular Dynamics Simulation With Speculation and In-Order Commitment.

Md Ashfaquzzaman Khan¹, Martin C Herbordt

¹Computer Architecture and Automated Design Laboratory, Department of Electrical and Computer Engineering, Boston University; Boston, MA 02215, www.bu.edu/caadlab.

Journal of Computational Physics

|August 9, 2011

Summary

This summary is machine-generated.

Parallelizing Discrete Molecular Dynamics (DMD) simulations using event-based decomposition significantly improves performance. This novel approach achieves substantial speed-ups on multi-core processors, overcoming previous scalability limitations in DMD.

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Area of Science:

Computational Physics
Molecular Dynamics Simulations
Parallel Computing

Background:

Discrete Molecular Dynamics (DMD) simulations offer computational efficiency by advancing events rather than fixed timesteps.
Existing DMD codes are predominantly serial, hindering scalability on modern multi-core processors.
The inherent nature of discrete event simulation presents challenges for parallelization.

Purpose of the Study:

To address the scalability limitations of Discrete Molecular Dynamics (DMD) simulations.
To introduce a novel method for parallelizing DMD using event-based decomposition.
To analyze the performance potential of this parallelization technique on shared-memory multiprocessors.

Main Methods:

Developed an event-based decomposition strategy for parallelizing DMD.
Employed microarchitecture-inspired speculative processing of events to expose parallelism.
Ensured correctness through in-order commitment of events.
Conducted extensive experimentation with scheduling and synchronization to mitigate serialization.

Main Results:

Achieved significant speed-ups for DMD simulations on multi-core processors.
Demonstrated nearly 6x speed-up on an 8-core and over 9x on a 12-core processor.
Verified analytical models correlating performance with concurrency and architectural limitations.

Conclusions:

The proposed event-based decomposition method effectively parallelizes DMD simulations.
This approach overcomes the serial nature of traditional DMD, enabling scalability.
The findings provide a foundation for accelerating large-scale molecular dynamics research.