HILO: Quasi Diffusion Accelerated Monte Carlo on Hybrid Architectures
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HILO: Quasi Diffusion Accelerated Monte Carlo on Hybrid Architectures


The Boltzmann transport equation provides high fidelity simulation of a diverse range of kinetic systems. Classical methods to solve the equation are computationally and data intensive. Existing stochastic solutions to the Boltzmann equation map well to traditional large multi-core and many-node architectures but suffer performance degradations on graphics processing units (GPUs) due to heavy thread divergence. We present a a novel algorithm, Quasi-Diffusion Accelerated Monte Carlo (QDA-MC), which improves performance on heterogeneous CPU/GPU architectures.

An equally important aspect of this project is the joint development of QDA-MC through collaboration between the computational and computer science communities. This collaboration identified computational platforms and features that best suit the algorithm, and influenced algorithmic details which improve its computational efficiency. In addition to algorithm details and implementation results, we present the code optimizations and the design decisions that were critical to the co-design process.


This code is released under LA-CC-11-076. The license is BSD-ish with a "modifications must be indicated" clause. See http://github.com/losalamos/HILO/blob/master/LICENSE for the full text.


A Los Alamos technical report (LA-UR 11-05596) has been written for the project and is available in this repository as a PDF file.


Student authors developed code while interns at Los Alamos during the Summer 2011 Co-Design School (http://codesign.lanl.gov)


Mahesh Ravishankar ravishan@cse.ohio-state.edu
Jeffrey Willert jawiller@ncsu.edu
Paul Sathre sath6220@cs.vt.edu
Han Dong handong32@gmail.com
Michael Sullivan mbsullivan@mail.utexas.edu
William Taitano williamtaitano1208@hotmail.com

Los Alamos Mentors

Tim Germann
Dana Knoll
Bryan Lally
Patrick McCormick
Allen McPherson
Scott Pakin