Automated Reasoning Support for Invasive Interactive Parallelization
To parallelize a sequential source code, a parallelization strategy must be defined that transforms the sequential source code into an equivalent parallel version. Since parallelizing compilers can sometimes transform sequential loops and other well-structured codes into parallel ones automatically,...
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Linköpings universitet, Institutionen för datavetenskap
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ndltd-UPSALLA1-oai-DiVA.org-liu-848302014-10-09T04:48:52ZAutomated Reasoning Support for Invasive Interactive ParallelizationengMoshir Moghaddam, KianoshLinköpings universitet, Institutionen för datavetenskapLinköpings universitet, Tekniska högskolan2012Multi-processorsDependence AnalysisCode parallelizationSemi-automatic parallelizationIIPISCOpenMPMPIArtificial IntelligenceReasoningDecision TreeDivide and Conquer (D&C) algorithmsTo parallelize a sequential source code, a parallelization strategy must be defined that transforms the sequential source code into an equivalent parallel version. Since parallelizing compilers can sometimes transform sequential loops and other well-structured codes into parallel ones automatically, we are interested in finding a solution to parallelize semi-automatically codes that compilers are not able to parallelize automatically, mostly because of weakness of classical data and control dependence analysis, in order to simplify the process of transforming the codes for programmers.Invasive Interactive Parallelization (IIP) hypothesizes that by using anintelligent system that guides the user through an interactive process one can boost parallelization in the above direction. The intelligent system's guidance relies on a classical code analysis and pre-defined parallelizing transformation sequences. To support its main hypothesis, IIP suggests to encode parallelizing transformation sequences in terms of IIP parallelization strategies that dictate default ways to parallelize various code patterns by using facts which have been obtained both from classical source code analysis and directly from the user.In this project, we investigate how automated reasoning can supportthe IIP method in order to parallelize a sequential code with an acceptable performance but faster than manual parallelization. We have looked at two special problem areas: Divide and conquer algorithms and loops in the source codes. Our focus is on parallelizing four sequential legacy C programs such as: Quick sort, Merge sort, Jacobi method and Matrix multipliation and summation for both OpenMP and MPI environment by developing an interactive parallelizing assistance tool that provides users with the assistanceneeded for parallelizing a sequential source code. Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-84830application/pdfinfo:eu-repo/semantics/openAccess |
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English |
format |
Others
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Multi-processors Dependence Analysis Code parallelization Semi-automatic parallelization IIP ISC OpenMP MPI Artificial Intelligence Reasoning Decision Tree Divide and Conquer (D&C) algorithms |
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Multi-processors Dependence Analysis Code parallelization Semi-automatic parallelization IIP ISC OpenMP MPI Artificial Intelligence Reasoning Decision Tree Divide and Conquer (D&C) algorithms Moshir Moghaddam, Kianosh Automated Reasoning Support for Invasive Interactive Parallelization |
description |
To parallelize a sequential source code, a parallelization strategy must be defined that transforms the sequential source code into an equivalent parallel version. Since parallelizing compilers can sometimes transform sequential loops and other well-structured codes into parallel ones automatically, we are interested in finding a solution to parallelize semi-automatically codes that compilers are not able to parallelize automatically, mostly because of weakness of classical data and control dependence analysis, in order to simplify the process of transforming the codes for programmers.Invasive Interactive Parallelization (IIP) hypothesizes that by using anintelligent system that guides the user through an interactive process one can boost parallelization in the above direction. The intelligent system's guidance relies on a classical code analysis and pre-defined parallelizing transformation sequences. To support its main hypothesis, IIP suggests to encode parallelizing transformation sequences in terms of IIP parallelization strategies that dictate default ways to parallelize various code patterns by using facts which have been obtained both from classical source code analysis and directly from the user.In this project, we investigate how automated reasoning can supportthe IIP method in order to parallelize a sequential code with an acceptable performance but faster than manual parallelization. We have looked at two special problem areas: Divide and conquer algorithms and loops in the source codes. Our focus is on parallelizing four sequential legacy C programs such as: Quick sort, Merge sort, Jacobi method and Matrix multipliation and summation for both OpenMP and MPI environment by developing an interactive parallelizing assistance tool that provides users with the assistanceneeded for parallelizing a sequential source code. |
author |
Moshir Moghaddam, Kianosh |
author_facet |
Moshir Moghaddam, Kianosh |
author_sort |
Moshir Moghaddam, Kianosh |
title |
Automated Reasoning Support for Invasive Interactive Parallelization |
title_short |
Automated Reasoning Support for Invasive Interactive Parallelization |
title_full |
Automated Reasoning Support for Invasive Interactive Parallelization |
title_fullStr |
Automated Reasoning Support for Invasive Interactive Parallelization |
title_full_unstemmed |
Automated Reasoning Support for Invasive Interactive Parallelization |
title_sort |
automated reasoning support for invasive interactive parallelization |
publisher |
Linköpings universitet, Institutionen för datavetenskap |
publishDate |
2012 |
url |
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-84830 |
work_keys_str_mv |
AT moshirmoghaddamkianosh automatedreasoningsupportforinvasiveinteractiveparallelization |
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1716716386640723968 |