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Fast Solver for Large Scale Multistate Bennett Acceptance Ratio Equations.

Xinqiang Ding¹, Jonah Z Vilseck², Charles L Brooks^1,2,3

¹Department of Computational Medicine & Bioinformatics , University of Michigan , Ann Arbor , Michigan 48109 , United States.

Journal of Chemical Theory and Computation

|January 29, 2019

Summary

This summary is machine-generated.

A new Python module, FastMBAR, accelerates the calculation of relative free energies using the multistate Bennett acceptance ratio (MBAR) and unbinned weighted histogram analysis method (UWHAM). This computational tool offers significant speedups, especially on GPUs, for complex thermodynamic state analyses.

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Area of Science:

Computational Chemistry
Statistical Mechanics

Background:

Multistate Bennett acceptance ratio (MBAR) and unbinned weighted histogram analysis method (UWHAM) are crucial for calculating relative free energies across multiple thermodynamic states.
High-throughput computing enables sampling from thousands of states, necessitating efficient solutions for large-scale MBAR/UWHAM equations.

Purpose of the Study:

To develop a fast solver for large-scale MBAR/UWHAM equations.
To implement and distribute the solver as a Python module named FastMBAR.

Main Methods:

The solver leverages the derivation of MBAR/UWHAM equations as a Rao-Blackwell estimator.
Benchmarking against the existing pymbar solver on both CPU and GPU.

Main Results:

FastMBAR demonstrates a speedup of over 2x compared to pymbar on CPUs.
On GPUs, FastMBAR achieves a speedup exceeding 100x compared to pymbar.
The computational efficiency is particularly beneficial for bootstrapping uncertainty estimations.

Conclusions:

FastMBAR provides a significantly faster method for solving large-scale MBAR/UWHAM equations.
The GPU-accelerated performance of FastMBAR enhances the feasibility of complex free energy calculations and uncertainty quantification.