skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Collaborative Proposal: MCREX: Using Monte Carlo Algorithms to Achieve Resiliency and Performance at Scale for Linear and Non-Linear Solver Applications

Abstract

Final report on research activities carried out by the PI (Michele Benzi) with his PhD student Massimilano Lupo Pasini and other collaborators under the grant.

Authors:
 [1]
  1. Emory Univ., Atlanta, GA (United States)
Publication Date:
Research Org.:
Emory Univ., Atlanta, GA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1339216
Report Number(s):
DOE-Emory-10271-1
DOE Contract Number:
SC0010271
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Hybrid deterministic-stochastic solvers; resilience; iterative methods; parallel solvers; preconditioning techniques

Citation Formats

Benzi, Michele. Collaborative Proposal: MCREX: Using Monte Carlo Algorithms to Achieve Resiliency and Performance at Scale for Linear and Non-Linear Solver Applications. United States: N. p., 2017. Web.
Benzi, Michele. Collaborative Proposal: MCREX: Using Monte Carlo Algorithms to Achieve Resiliency and Performance at Scale for Linear and Non-Linear Solver Applications. United States.
Benzi, Michele. Sun . "Collaborative Proposal: MCREX: Using Monte Carlo Algorithms to Achieve Resiliency and Performance at Scale for Linear and Non-Linear Solver Applications". United States. doi:.
@article{osti_1339216,
title = {Collaborative Proposal: MCREX: Using Monte Carlo Algorithms to Achieve Resiliency and Performance at Scale for Linear and Non-Linear Solver Applications},
author = {Benzi, Michele},
abstractNote = {Final report on research activities carried out by the PI (Michele Benzi) with his PhD student Massimilano Lupo Pasini and other collaborators under the grant.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Jan 15 00:00:00 EST 2017},
month = {Sun Jan 15 00:00:00 EST 2017}
}

Technical Report:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that may hold this item. Keep in mind that many technical reports are not cataloged in WorldCat.

Save / Share:
  • The goal of this work is to develop a fast computed tomography (CT) reconstruction algorithm based on graphics processing units (GPU) that achieves significant improvement over traditional central processing unit (CPU) based implementations. The main challenge in developing a CT algorithm that is capable of handling very large datasets is parallelizing the algorithm in such a way that data transfer does not hinder performance of the reconstruction algorithm. General Purpose Graphics Processing (GPGPU) is a new technology that the Science and Technology (S&T) community is starting to adopt in many fields where CPU-based computing is the norm. GPGPU programming requiresmore » a new approach to algorithm development that utilizes massively multi-threaded environments. Multi-threaded algorithms in general are difficult to optimize since performance bottlenecks occur that are non-existent in single-threaded algorithms such as memory latencies. If an efficient GPU-based CT reconstruction algorithm can be developed; computational times could be improved by a factor of 20. Additionally, cost benefits will be realized as commodity graphics hardware could potentially replace expensive supercomputers and high-end workstations. This project will take advantage of the CUDA programming environment and attempt to parallelize the task in such a way that multiple slices of the reconstruction volume are computed simultaneously. This work will also take advantage of the GPU memory by utilizing asynchronous memory transfers, GPU texture memory, and (when possible) pinned host memory so that the memory transfer bottleneck inherent to GPGPU is amortized. Additionally, this work will take advantage of GPU-specific hardware (i.e. fast texture memory, pixel-pipelines, hardware interpolators, and varying memory hierarchy) that will allow for additional performance improvements.« less
  • These are slides for a presentation on using nuclear theory, data and uncertainties in Monte Carlo transport applications. The following topics are covered: nuclear data (experimental data versus theoretical models, data evaluation and uncertainty quantification), fission multiplicity models (fixed source applications, criticality calculations), uncertainties and their impact (integral quantities, sensitivity analysis, uncertainty propagation).