Achieving Strong Scaling with NAM on Blue Gene/L

SciVee ◽  
2007 ◽  
Keyword(s):  
Strong Scaling ◽  
Blue Gene

scholarly journals Blue Matter: Strong Scaling of Molecular Dynamics on Blue Gene/L

2006 ◽  
pp. 846-854 ◽  
Author(s):  
Blake G. Fitch ◽  
Aleksandr Rayshubskiy ◽  
Maria Eleftheriou ◽  
T. J. Christopher Ward ◽  
Mark Giampapa ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Strong Scaling ◽  
Blue Gene

scholarly journals Strong scaling for numerical weather prediction at petascale with the atmospheric model NUMA

2018 ◽  
Vol 33 (2) ◽  
pp. 411-426 ◽  
Author(s):  
Andreas Müller ◽  
Michal A Kopera ◽  
Simone Marras ◽  
Lucas C Wilcox ◽  
Tobin Isaac ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Atmospheric Model ◽  
Spectral Element ◽  
Horizontal Resolution ◽  
Galerkin Methods ◽  
Double Precision ◽  
Numerical Weather ◽  
Strong Scaling ◽  
Blue Gene

Numerical weather prediction (NWP) has proven to be computationally challenging due to its inherent multiscale nature. Currently, the highest resolution global NWP models use a horizontal resolution of 9 km. At this resolution, many important processes in the atmosphere are not resolved. Needless to say, this introduces errors. In order to increase the resolution of NWP models, highly scalable atmospheric models are needed. The non-hydrostatic unified model of the atmosphere (NUMA), developed by the authors at the Naval Postgraduate School, was designed to achieve this purpose. NUMA is used by the Naval Research Laboratory, Monterey as the engine inside its next generation weather prediction system NEPTUNE. NUMA solves the fully compressible Navier–Stokes equations by means of high-order Galerkin methods (both spectral element as well as discontinuous Galerkin methods can be used). NUMA is capable of running middle and upper atmosphere simulations since it does not make use of the shallow-atmosphere approximation. This article presents the performance analysis and optimization of the spectral element version of NUMA. The performance at different optimization stages is analyzed using a theoretical performance model as well as measurements via hardware counters. Machine-independent optimization is compared to machine-specific optimization using Blue Gene (BG)/Q vector intrinsics. The best portable version of the main computations was found to be about two times slower than the best non-portable version. By using vector intrinsics, the main computations reach 1.2 PFlops on the entire IBM Blue Gene supercomputer Mira (12% of the theoretical peak performance). The article also presents scalability studies for two idealized test cases that are relevant for NWP applications. The atmospheric model NUMA delivers an excellent strong scaling efficiency of 99% on the entire supercomputer Mira using a mesh with 1.8 billion grid points. This allows running a global forecast of a baroclinic wave test case at a 3-km uniform horizontal resolution and double precision within the time frame required for operational weather prediction.

scholarly journals LAMMPS strong scaling performance optimization on Blue Gene/Q

2014 ◽  
Author(s):  
Paul Coffman ◽  
Wei Jiang ◽  
Nichols Romero
Keyword(s):  
Performance Optimization ◽  
Strong Scaling ◽  
Blue Gene

Anomaly Detection via Mining Numerical Workflow Relations from Logs

2020 ◽  
Author(s):  
Bo Zhang ◽  
Hongyu Zhang ◽  
Pablo Moscato
Keyword(s):  
Distributed Systems ◽  
Anomaly Detection ◽  
Text Messages ◽  
Distributed File System ◽  
Log Data ◽  
Sliding Windows ◽  
Novel Approach ◽  
Hadoop Distributed File System ◽  
Blue Gene ◽  
Online Anomaly Detection

<div>Complex software intensive systems, especially distributed systems, generate logs for troubleshooting. The logs are text messages recording system events, which can help engineers determine the system's runtime status. This paper proposes a novel approach named ADR (stands for Anomaly Detection by workflow Relations) that employs matrix nullspace to mine numerical relations from log data. The mined relations can be used for both offline and online anomaly detection and facilitate fault diagnosis. We have evaluated ADR on log data collected from two distributed systems, HDFS (Hadoop Distributed File System) and BGL (IBM Blue Gene/L supercomputers system). ADR successfully mined 87 and 669 numerical relations from the logs and used them to detect anomalies with high precision and recall. For online anomaly detection, ADR employs PSO (Particle Swarm Optimization) to find the optimal sliding windows' size and achieves fast anomaly detection.</div><div>The experimental results confirm that ADR is effective for both offline and online anomaly detection. </div>

Lattice QCD on GPU clusters, using the QUDA library and the Chroma software system

2012 ◽  
Vol 26 (4) ◽  
pp. 386-398 ◽  
Author(s):  
Bálint Joó ◽  
Mike A. Clark
Keyword(s):  
Quantum Chromodynamics ◽  
Graphics Processing Units ◽  
Lawrence Livermore National Laboratory ◽  
National Laboratory ◽  
Application Framework ◽  
Software System ◽  
Nuclear Forces ◽  
Lattice Quantum Chromodynamics ◽  
Lattice Quantum ◽  
Strong Scaling

The QUDA library for optimized lattice quantum chromodynamics using GPUs, combined with a high-level application framework such as the Chroma software system, provides a powerful tool for computing quark propagators, a key step in current calculations of hadron spectroscopy, nuclear structure, and nuclear forces. In this contribution we discuss our experiences, including performance and strong scaling of the QUDA library and Chroma on the Edge Cluster at Lawrence Livermore National Laboratory and on various clusters at Jefferson Lab. We highlight some scientific successes and consider future directions for graphics processing units in lattice quantum chromodynamics calculations.

scholarly journals A lightweight virtual machine monitor for Blue Gene/P

2012 ◽  
Vol 26 (2) ◽  
pp. 95-109 ◽  
Author(s):  
Jan Stoess ◽  
Udo Steinberg ◽  
Volkmar Uhlig ◽  
Jens Kehne ◽  
Jonathan Appavoo ◽  
...  
Keyword(s):  
Virtual Machine ◽  
Virtual Machine Monitor ◽  
Blue Gene

scholarly journals On Strong Scaling Open Source Tools for Mining Atom Probe Tomography Data

2019 ◽  
Vol 25 (S2) ◽  
pp. 298-299 ◽  
Author(s):  
Markus Kühbach ◽  
Priyanshu Bajaj ◽  
Andrew Breen ◽  
Eric A. Jägle ◽  
Baptiste Gault
Keyword(s):  
Open Source ◽  
Atom Probe Tomography ◽  
Atom Probe ◽  
Strong Scaling ◽  
Tomography Data

Tracking Nanostructural Evolution in Alloys: Large-Scale Analysis of Atom Probe Tomography Data on Blue Gene/L

2008 ◽  
Author(s):  
Sudip Seal ◽  
Michael Moody ◽  
Anna Ceguerra ◽  
Simon Ringer ◽  
Krishna Rajan ◽  
...  
Keyword(s):  
Atom Probe Tomography ◽  
Large Scale ◽  
Atom Probe ◽  
Scale Analysis ◽  
Large Scale Analysis ◽  
Blue Gene ◽  
Tomography Data
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