Tools for High Performance Computing 2015

Name: Tools for High Performance Computing 2015 | Proceedings of the 9th International Workshop on Parallel Tools for High Performance Computing, September 2015, Dresden, Germany
Brand: Springer
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Proceedings of the 9th International Workshop on Parallel Tools for High Performance Computing, September 2015, Dresden, Germany

Andreas Knüpfer Tobias Hilbrich Christoph Niethammer José Gracia Wolfgang E. Nagel Michael M. Resch(Editor)

Springer (Publisher)

1st Edition

Published on 27. July 2016

VIII, 181 pages

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978-3-319-39589-0 (ISBN)

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Content

Intro
Preface
Contents
1 Dyninst and MRNet: Foundational Infrastructure for Parallel Tools
1.1 Introduction
1.2 DyninstAPI and Components
1.3 MRNet
1.4 Performance Tools
1.4.1 Sampling Tools
1.4.2 Tracing Tools
1.5 Debugging Tools
1.5.1 Stack Trace Aggregation
1.5.2 Distributed Debuggers with MRNet
1.5.3 Dynamic Instrumentation for Debugging
1.6 Analysis Tools
1.6.1 Slicing
1.6.2 Binary Parsing
1.7 Modification Tools
1.8 Future Work
References
2 Validation of Hardware Events for Successful Performance Pattern Identification in High Performance Computing
2.1 Introduction and Related Work
2.2 Identification of Signatures for Performance Patterns
2.2.1 Bandwidth Saturation
2.2.2 Load Imbalance
2.2.3 False Cache Line Sharing
2.3 Useful Event Sets
2.4 Validation of Performance Patterns
2.5 Conclusion
References
3 Performance Optimization for the Trinity RNA-Seq Assembler
3.1 Introduction
3.2 Tool Infrastructure
3.2.1 Collectl
3.2.2 Score-P and Vampir
3.3 Analysis and Optimization
3.3.1 Identification of Optimization Targets
3.3.2 Optimization of Samtools and Scaffold_iworm_contigs
3.3.3 Optimization of Sort
3.3.4 Optimization of ReadsToTranscripts
3.3.5 Optimization of GraphFromFasta
3.3.6 Optimization Results
3.4 Tool Challenges and Restrictions
3.5 Conclusion
References
4 Power Management and Event Verification in PAPI
4.1 Introduction
4.2 Power Management Using the Intel RAPL Interface
4.2.1 Case Study: LU Factorization
4.3 Counter Inspection Toolkit
4.4 Related Work
4.5 Conclusion and Future Work
References
5 Gleaming the Cube: Online Performance Analysis and Visualization Using MALP
5.1 Introduction
5.1.1 From Performance Data Management to Performance Valorization
5.1.2 In-Place Performance Data Processing
5.1.3 Trace-Based Performance Data Processing
5.1.4 OnLine Performance Data Processing
5.1.5 Summary
5.2 MALP Architecture
5.3 Profiling with MALP
5.4 Introducing Parallel Application Dashboards with MALP
5.5 Conclusion an Future Work
References
6 Evaluation of Tool Interface Standards for Performance Analysis of OpenACC and OpenMP Programs
6.1 Introduction
6.2 OpenACC and OpenMP
6.2.1 OpenACC
6.2.2 OpenMP
6.3 The OpenACC Tools Interface
6.3.1 Runtime Events
6.3.2 Callback Signature and Low-Level API Interface
6.3.3 Tool Registration, Initialization and Control
6.4 OMPT---An OpenMP Tools Interface
6.4.1 Runtime States
6.4.2 Runtime Events and Callbacks
6.4.3 Tool Registration, Initialization and Control
6.5 Evaluation
6.5.1 Interface Design
6.5.2 Tool Integration
6.5.3 Comparison of OMPT and ACCT Key Facts
6.5.4 Extension Proposals
6.6 Conclusion
References
7 Extending MUST to Check Hybrid-Parallel Programs for Correctness Using the OpenMP Tools Interface
7.1 Introduction
7.2 OpenMP Error Classification
7.2.1 Syntactic Defects
7.2.2 Semantic Defects
7.2.3 Detectability
7.3 OpenMP Tools Interface
7.4 MUST Extensions
7.4.1 Thread-Safety
7.4.2 Extended Event Model
7.5 MUST Evaluation Run
7.6 Conclusion and Outlook
References
8 Event Flow Graphs for MPI Performance Monitoring and Analysis
8.1 Introduction
8.2 Event Flow Graphs (EFGs)
8.3 Temporal Event Flow Graphs (t-EFGs)
8.4 Three Different Use Case Scenarios for Event Flow Graphs
8.4.1 Event Flow Graphs and Trace Compression
8.4.2 Event Flow Graphs and Application Structure Detection
8.4.3 Event Flow Graphs and Visual Performance Analysis
8.5 Related Work
8.6 Future Work
8.7 Conclusion
References
9 Aura: A Flexible Dataflow Engine for Scalable Data Processing
9.1 Introduction
9.2 Related Work
9.2.1 Distributed Execution Engines for Parallel Dataflows
9.2.2 Programming Abstractions for Parallel Dataflows
9.3 System Architecture
9.3.1 Workload Manager
9.3.2 Task Manager
9.3.3 Client
9.4 Program Representation and Translation
9.4.1 Operator Plan
9.4.2 Physical Plan
9.4.3 Plan Translation and Deployment
9.5 Distributed Execution
9.5.1 Push/Pull-Based Data Streams
9.5.2 Control-Flow Execution
9.6 Summary
References
10 Parallel Code Analysis in HPC User Support
10.1 Introduction
10.2 The Competence Center for HPC in Hesse
10.2.1 Structure and Goals
10.2.2 User Support in a HPC Environment
10.3 Parallel Performance Analysis Tools: Requirements and User Insights
10.3.1 Motivations for Parallel Analysis
10.3.2 Requirements for Parallel Code Analysis Tools in HPC User Support
10.3.3 Parallel Analysis Tools in Use at the HKHLR
10.3.4 User Insights
10.4 Conclusion
References
11 PARCOACH Extension for Hybrid Applications with Interprocedural Analysis
11.1 Introduction
11.1.1 Motivating Examples
11.2 PARCOACH Static and Dynamic Analyses for Hybrid Applications
11.2.1 MPI Thread-Level Checking
11.2.2 MPI Collective Communication Verification
11.3 Interprocedural Analysis
11.4 Experimental Results
11.5 Conclusion
References
12 Enabling Model-Centric Debugging for Task-Based Programming Models---A Tasking Control Interface
12.1 Introduction
12.2 Background
12.2.1 Temanejo
12.2.2 OMPT
12.3 Tasking Control Interface
12.3.1 Preliminaries
12.3.2 Tool Data Structures
12.3.3 Control Request Functions
12.3.4 Initialization of Tools
12.3.5 Implementation By Example
12.4 Debugging for OmpSs with Temanejo Through TCA
12.5 Conclusions
References
13 Evaluating Out-of-Order Engine Limitations Using Uop Flow Simulation
13.1 Introduction
13.2 Motivating Example
13.3 Model Presentation
13.3.1 Limited Input
13.3.2 Model Overview
13.3.3 Simplified Front-End
13.3.4 Resource Allocation Table (RAT)
13.3.5 Out-of-Order Flow
13.3.6 Retirement
13.3.7 Overlooked Issues
13.4 Validation
13.4.1 Fidelity
13.4.2 Simulation Speed
13.5 Sensitivity Analyses
13.5.1 Latency Sensitivity Analysis
13.5.2 Resource Size Sensitivity Analysis
13.6 Related Work
13.7 Future Work
13.8 Conclusion
References

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