Tools for High Performance Computing 2018 / 2019 (eBook, PDF)

Proceedings of the 12th and of the 13th International Workshop on Parallel Tools for High Performance Computing, Stuttgart, Germany, September 2018, and Dresden, Germany, September 2019
Redaktion: Mix, Hartmut; Resch, Michael M.; Nagel, Wolfgang E.; Zhou, Huan; Niethammer, Christoph

• Format: PDF

Produktbeschreibung

This book presents the proceedings of the 12th International Parallel Tools Workshop, held in Stuttgart, Germany, during September 17-18, 2018, and of the 13th International Parallel Tools Workshop, held in Dresden, Germany, during September 2-3, 2019. The workshops are a forum to discuss the latest advances in parallel tools for high-performance computing.

High-performance computing plays an increasingly important role for numerical simulation and modeling in academic and industrial research. At the same time, using large-scale parallel systems efficiently is becoming more difficult. A number of tools addressing parallel program development and analysis has emerged from the high-performance computing community over the last decade, and what may have started as a collection of a small helper scripts has now matured into production-grade frameworks. Powerful user interfaces and an extensive body of documentation together create a user-friendly environment for parallel tools.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 270
• Erscheinungstermin: 22. Mai 2021
• Englisch
• ISBN-13: 9783030660574
• Artikelnr.: 61929464

Inhaltsangabe

Detecting disaster before it strikes: On the challenges of automated building and testing in HPC environments.- Saving Energy Using the READEX Methodology.- The MPI Tool Interfaces: Past, Present, and Future-Capabilities and Prospects.- A tool for runtime analysis of performance and energy usage in NUMA systems.- Usage experiences of performance tools for modern C++ code analysis and optimization.- Performance Analysis of Complex Engineering Frameworks.- System-wide Low-frequency Sampling for Large HPC Systems.- Exploring Space-Time Trade-Off in Backtraces.- Enabling Performance Analysis of Kokkos Applications with Score-P.- Regional Profiling for Efficient Performance Optimization.- Effortless Monitoring of Arithmetic Intensity with PAPI's Counter Analysis Toolkit.- ONE View: a fully automatic method for aggregating key performance metrics and providing users with a synthetic view of HPC applications.- A picture is worth a thousand numbers - Enhancing Cube's analysis capabilities with plugins.- Advanced Python Performance Monitoring with Score-P.