Current advances in High Performance Computing (HPC) increasingly impact efficient software development workflows. Programmers for HPC applications need to consider trends such as increased core counts, multiple levels of parallelism, reduced memory per core, and I/O system challenges in order to derive well performing and highly scalable codes. At the same time, the increasing complexity adds further sources of program defects. While novel programming paradigms and advanced system libraries provide solutions for some of these challenges, appropriate supporting tools are indispensable. Such tools aid application developers in debugging, performance analysis, or code optimization and therefore make a major contribution to the development of robust and efficient parallel software. This book introduces a selection of the tools presented and discussed at the 7th International Parallel Tools Workshop, held in Dresden, Germany, September 3-4, 2013.
Mục lục
Juan Gonzalez, Judit Gimenez, and Jesus Labarta: Performance Analytics: Understanding Parallel Applications Using Cluster and Sequence Analysis.- Mahesh Lagadapati, Frank Mueller, and Christian Engelmann: Tools for Simulation and Benchmark Generation at Exascale.- Dirk Schmidl, Christian Terboven, Dieter an Mey, and Matthias S. Müller: Suitability of Performance Tools for Open MP Task-parallel Programs.- Yury Oleynik, Robert Mijaković, Isaías A. Comprés Ure˜na, Michael Firbach, and Michael Gerndt: Recent Advances in Periscope for Performance Analysis and Tuning.- Xingfu Wu, Valerie Taylor, Charles Lively, Hung-Ching Chang, Bo Li, Kirk Cameron, Dan Terpstra, and Shirley Moore: Mu MMI: Multiple Metrics Modeling Infrastructure.- Thomas M. Baumann and José Gracia: Cudagrind: Memory-Usage Checking for CUDA.- Trevor E. Carlson, Wim Heirman, Kenzo Van Craeynest, Lieven Eeckhout: Node Performance and Energy Analysis with the Sniper Multi-Core Simulator.- Alvaro Aguilera, Holger Mickler, Julian Kunkel, Michaela Zimmer, Marc Wiedemann, Ralph Müller-Pfefferkorn: A Comparison of Trace Compression Methods for Massively Parallel Applications in Context of the SIOX Project.- Zakaria Bendifallah, William Jalby, José Noudohouenou, Emmanuel Oseret, and Vincent Palomares: PAMDA: Performance Assessment using MAQAO Toolset and Differential Analysis.
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