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Title: Analyzing PICL trace data with MEDEA

Abstract

Execution traces and performance statistics can be collected for parallel applications on a variety of multiprocessor platforms by using the Portable Instrumented Communication Library (PICL). The static and dynamic performance characteristics of performance characteristics of performance data can be analyzed easily and effectively with the facilities provided within the MEasurements Description Evaluation and Analysis tool (MEDEA). A case study is then outlined that uses PICL and MEDEA to characterize the performance of a parallel benchmark code executed on different hardware platforms and using different parallel algorithms and communication protocols.

Authors:
 [1];  [2]
  1. Pavia Univ., (Italy). Dipt. Informatica e Sistemistica
  2. Oak Ridge National Lab., TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab., TN (United States)
Sponsoring Org.:
USDOE, Washington, DC (United States); Consiglio Nazionale delle Ricerche, Rome (Italy); Ministero dell`Universita` e della Ricerca Scientifica e Tecnologica (Italy)
OSTI Identifier:
10137707
Report Number(s):
CONF-9405115-1
ON: DE94009173
DOE Contract Number:
AC05-84OR21400
Resource Type:
Conference
Resource Relation:
Conference: 7. international conference on modeling techniques and tools for computer performance evaluation,Vienna (Austria),4-6 May 1994; Other Information: PBD: [1994]
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; PARALLEL PROCESSING; M CODES; BENCHMARKS; COMPUTERS; DATA PROCESSING; COMPUTER CALCULATIONS; ALGORITHMS; 990200; MATHEMATICS AND COMPUTERS

Citation Formats

Merlo, A.P., and Worley, P.H. Analyzing PICL trace data with MEDEA. United States: N. p., 1994. Web.
Merlo, A.P., & Worley, P.H. Analyzing PICL trace data with MEDEA. United States.
Merlo, A.P., and Worley, P.H. Fri . "Analyzing PICL trace data with MEDEA". United States. doi:. https://www.osti.gov/servlets/purl/10137707.
@article{osti_10137707,
title = {Analyzing PICL trace data with MEDEA},
author = {Merlo, A.P. and Worley, P.H.},
abstractNote = {Execution traces and performance statistics can be collected for parallel applications on a variety of multiprocessor platforms by using the Portable Instrumented Communication Library (PICL). The static and dynamic performance characteristics of performance characteristics of performance data can be analyzed easily and effectively with the facilities provided within the MEasurements Description Evaluation and Analysis tool (MEDEA). A case study is then outlined that uses PICL and MEDEA to characterize the performance of a parallel benchmark code executed on different hardware platforms and using different parallel algorithms and communication protocols.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Apr 01 00:00:00 EST 1994},
month = {Fri Apr 01 00:00:00 EST 1994}
}

Conference:
Other availability
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  • Execution traces and performance statistics can be collected for parallel applications on a variety of multiprocessor platforms by using the Portable Instrumented Communication Library (PICL). The static and dynamic performance characteristics of performance data can be analyzed easily and effectively with the facilities provided within the MEasurements Description Evaluation and Analysis tool (MEDEA). This report describes the integration of the PICL trace file format into MEDEA. A case study is then outlined that uses PICL and MEDEA to characterize the performance of a parallel benchmark code executed on different hardware platforms and using different parallel algorithms and communication protocols.
  • A trace file format is described that will be used in future releases of the Portable Instrumented Communication Library (PICL) and ParaGraph. The new format provides improved support for tracing and profiling PICL communication primitives and user-defined events. The new format is also easily extended and may be useful in other instrumentation packages and performance visualization tools.
  • A trace file format is described that will be used in future releases of the Portable Instrumented Communication Library (PICL) and ParaGraph. The new format provides improved support for tracing and profiling PICL communication primitives and user-defined events. The new format is also easily extended and may be useful in other instrumentation packages and performance visualization tools.
  • Abstract not provided.
  • Many challenges in systems biology have to do with analyzing data within the framework of molecular phenomena and cellular pathways. How does this relate to thermodynamics that we know govern the behavior of molecules? Making progress in relating data analysis to thermodynamics is essential in systems biology if we are to build predictive models that enable the field of synthetic biology. This report discusses work at the crossroads of thermodynamics and data analysis, and demonstrates that statistical mechanical free energy is a multinomial log likelihood. Applications to systems biology are presented.