Monte Carlo Simulation of Molecular Systems

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Monte Carlo Simulation of Molecular Systems

• Faycal Benmlih, John Sartori, Alan Kaatz
• in conjunction with
• John Stone, Peter Freddolino

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Overview

• Monte Carlo Simulation of Protein Conformation
• Deviation From Original Plan
• Implementation
• Energy Calculation
• Random Number generation and Perturbation
• CPU Orchestration
• Final Results and Summary

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Introduction

• What is conformation anyway?
• Why is it useful?

pharm.wayne.edu
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Deviation From Original Plan
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Deviation From Original Plan
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Who is doing what?
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Energy Calculation Breakdown

• Block size energy kernel
• Inter-block energy kernel
• Reduction kernel

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Optimizations

• Reduce the register usage
• Minimize global memory accesses.
• Move redundant computations outside of loops.
• Use a single array for both block size energy and
  inter-block energy calculations.
• Eliminates the need of combining the two array
  before reducing them.

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Monte Carlo Perturbation

• Agitate molecules

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Random Number Generation Implementations

• Generate the random numbers on the CPU and
  transfer them to the GPU
• Have a dedicated kernel generate numbers and
  store them in global memory
• Have each perturbing thread generate random
  numbers as needed

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GPU RNG Issues

• Loading/Saving RNG state vector
• Initial seeding of every RNG thread
• RNG computational complexity
• Quality of random numbers

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Random Number Generators

• Linear Congruential Generator
• Linear Feedback Shift Register

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Random Number Generation
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Random Number Generators
The LEcuyer paper -- http//www.iro.umontreal.ca/
simardr/testu01/tu01.html
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GPU vs. CPU Speedup Bottlenecks?

• Lack of inter-block or global memory
  synchronization requires MC to have multiple
  kernels per simulation step
• Must send some information periodically back to
  host or have one-thread kernel
• Cannot mask all global memory transfers in energy
  calculation and perturbation

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