12 research outputs found
Performance metrics and auditing framework using application kernels for high‐performance computer systems
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97468/1/cpe2871.pd
PaPaS: A Portable, Lightweight, and Generic Framework for Parallel Parameter Studies
The current landscape of scientific research is widely based on modeling and
simulation, typically with complexity in the simulation's flow of execution and
parameterization properties. Execution flows are not necessarily
straightforward since they may need multiple processing tasks and iterations.
Furthermore, parameter and performance studies are common approaches used to
characterize a simulation, often requiring traversal of a large parameter
space. High-performance computers offer practical resources at the expense of
users handling the setup, submission, and management of jobs. This work
presents the design of PaPaS, a portable, lightweight, and generic workflow
framework for conducting parallel parameter and performance studies. Workflows
are defined using parameter files based on keyword-value pairs syntax, thus
removing from the user the overhead of creating complex scripts to manage the
workflow. A parameter set consists of any combination of environment variables,
files, partial file contents, and command line arguments. PaPaS is being
developed in Python 3 with support for distributed parallelization using SSH,
batch systems, and C++ MPI. The PaPaS framework will run as user processes, and
can be used in single/multi-node and multi-tenant computing systems. An example
simulation using the BehaviorSpace tool from NetLogo and a matrix multiply
using OpenMP are presented as parameter and performance studies, respectively.
The results demonstrate that the PaPaS framework offers a simple method for
defining and managing parameter studies, while increasing resource utilization.Comment: 8 pages, 6 figures, PEARC '18: Practice and Experience in Advanced
Research Computing, July 22--26, 2018, Pittsburgh, PA, US
XSEDE: eXtreme Science and Engineering Discovery Environment Third Quarter 2012 Report
The Extreme Science and Engineering Discovery Environment (XSEDE) is the most advanced, powerful, and robust collection of integrated digital resources and services in the world. It is an integrated cyberinfrastructure ecosystem with singular interfaces for allocations, support, and other key services that researchers can use to interactively share computing resources, data, and expertise.This a report of project activities and highlights from the third quarter of 2012.National Science Foundation, OCI-105357
XSEDE: The Extreme Science and Engineering Discovery Environment (OAC 15-48562) Interim Project Report 13: Report Year 5, Reporting Period 2 August 1, 2020 – October 31, 2020
This is the Interim Project Report 13 (IPR13) for the NSF XSEDE project. It includes Key Performance Indicator data and project highlights for Reporting Year 5, Report Period 2 (August 1-October 31, 2020).NSF OAC 15-48562Ope
XSEDE: The Extreme Science and Engineering Discovery Environment Post-XSEDE 2.0 Preliminary Transition Plan
The XSEDE team is committed to a seamless transition with no interruption in services at the hand-off from current XSEDE 2.0 operations to potential follow-on award(s) and awardee(s). XSEDE is comprised of six Work Breakdown Structure (WBS) Level 2 sub-groups (L2s), and each of those is further divided into WBS Level 3 areas (L3s). This report includes specific documents and activities that would be transitioned in each of these L2/L3 areas to a follow-on award(s) or awardee(s).National Science Foundation grant number ACI-1548562Ope