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Scaling Dalton, a molecular electronic structure program
KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC.ORCID-id: 0000-0001-9693-6265
KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC.ORCID-id: 0000-0002-5415-1248
KTH, Skolan för bioteknologi (BIO), Teoretisk kemi och biologi. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Parallelldatorcentrum, PDC.ORCID-id: 0000-0002-9123-8174
Barcelona Supercomputing Center, Universitat Politecnica de Catalunya, Barcelona, Spain.
Visa övriga samt affilieringar
2011 (Engelska)Ingår i: Seventh International Conference on e-Science, e-Science 2011, 5-8 December 2011, Stockholm, Sweden, IEEE conference proceedings, 2011, s. 256-262Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Dalton is a molecular electronic structure program featuring common methods of computational chemistry that are based on pure quantum mechanics (QM) as well as hybrid quantum mechanics/molecular mechanics (QM/MM). It is specialized and has a leading position in calculation of molecular properties with a large world-wide user community (over 2000 licenses issued). In this paper, we present a characterization and performance optimization of Dalton that increases the scalability and parallel efficiency of the application. We also propose asolution that helps to avoid the master/worker design of Daltonto become a performance bottleneck for larger process numbers and increase the parallel efficiency.

Ort, förlag, år, upplaga, sidor
IEEE conference proceedings, 2011. s. 256-262
Nyckelord [en]
Chemistry, Image color analysis, Libraries, Measurement, Optimization, Quantum mechanics, Wave functions
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:kth:diva-50421DOI: 10.1109/eScience.2011.43Scopus ID: 2-s2.0-84856350618ISBN: 978-1-4577-2163-2 (tryckt)OAI: oai:DiVA.org:kth-50421DiVA, id: diva2:461904
Konferens
Seventh International Conference on e-Science, e-Science 2011, 5-8 December 2011, Stockholm, Sweden
Forskningsfinansiär
Swedish e‐Science Research Center, OpCoReSEU, FP7, Sjunde ramprogrammet, INFSO RI-261523Swedish e‐Science Research Center
Anmärkning
Copyright 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. QC 20120110Tillgänglig från: 2012-01-10 Skapad: 2011-12-05 Senast uppdaterad: 2018-01-12Bibliografiskt granskad
Ingår i avhandling
1. Towards Scalable Performance Analysis of MPI Parallel Applications
Öppna denna publikation i ny flik eller fönster >>Towards Scalable Performance Analysis of MPI Parallel Applications
2015 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

  A considerably fraction of science discovery is nowadays relying on computer simulations. High Performance Computing  (HPC) provides scientists with the means to simulate processes ranging from climate modeling to protein folding. However, achieving good application performance and making an optimal use of HPC resources is a heroic task due to the complexity of parallel software. Therefore, performance tools  and runtime systems that help users to execute  applications in the most optimal way are of utmost importance in the landscape of HPC.  In this thesis, we explore different techniques to tackle the challenges of collecting, storing, and using  fine-grained performance data. First, we investigate the automatic use of real-time performance data in order to run applications in an optimal way. To that end, we present a prototype of an adaptive task-based runtime system that uses real-time performance data for task scheduling. This runtime system has a performance monitoring component that provides real-time access to the performance behavior of anapplication while it runs. The implementation of this monitoring component is presented and evaluated within this thesis. Secondly, we explore lossless compression approaches  for MPI monitoring. One of the main problems that  performance tools face is the huge amount of fine-grained data that can be generated from an instrumented application. Collecting fine-grained data from a program is the best method to uncover the root causes of performance bottlenecks, however, it is unfeasible with extremely parallel applications  or applications with long execution times. On the other hand, collecting coarse-grained data is scalable but  sometimes not enough to discern the root cause of a performance problem. Thus, we propose a new method for performance monitoring of MPI programs using event flow graphs. Event flow graphs  provide very low overhead in terms of execution time and  storage size, and can be used to reconstruct fine-grained trace files of application events ordered in time.

Ort, förlag, år, upplaga, sidor
Stockholm: KTH Royal Institute of Technology, 2015. s. viii, 39
Serie
TRITA-CSC-A, ISSN 1653-5723 ; 2015:05
Nyckelord
parallel computing, performance monitoring, performance tools, event flow graphs
Nationell ämneskategori
Datorsystem
Forskningsämne
Datalogi
Identifikatorer
urn:nbn:se:kth:diva-165043 (URN)978-91-7595-518-6 (ISBN)
Presentation
2015-05-20, The Visualization Studio, room 4451, Lindstedtsvägen 5, KTH, Stockholm, 10:00 (Engelska)
Opponent
Handledare
Anmärkning

QC 20150508

Tillgänglig från: 2015-05-08 Skapad: 2015-04-21 Senast uppdaterad: 2015-05-08Bibliografiskt granskad

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