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.
Edited by:
Hartmut Mix,
Christoph Niethammer,
Huan Zhou,
Wolfgang E. Nagel,
Michael M. Resch
Imprint: Springer Nature Switzerland AG
Country of Publication: Switzerland
Edition: 2021 ed.
Dimensions:
Height: 235mm,
Width: 155mm,
Weight: 433g
ISBN: 9783030660598
ISBN 10: 3030660591
Pages: 270
Publication Date: 24 May 2022
Audience:
Professional and scholarly
,
Undergraduate
Format: Paperback
Publisher's Status: Active
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.
