The Scalasca performance toolset architecture M Geimer, F Wolf, BJN Wylie, E Ábrahám, D Becker, B Mohr Concurrency and computation: Practice and experience 22 (6), 702-719, 2010 | 673 | 2010 |
Score-p: A joint performance measurement run-time infrastructure for periscope, scalasca, tau, and vampir A Knüpfer, C Rössel, D Mey, S Biersdorff, K Diethelm, D Eschweiler, ... Tools for High Performance Computing 2011: Proceedings of the 5th …, 2012 | 429 | 2012 |
Automatic performance analysis of hybrid MPI/OpenMP applications F Wolf, B Mohr Journal of Systems Architecture 49 (10-11), 421-439, 2003 | 219 | 2003 |
Using automated performance modeling to find scalability bugs in complex codes A Calotoiu, T Hoefler, M Poke, F Wolf Proceedings of the International Conference on High Performance Computing …, 2013 | 180 | 2013 |
KOJAK–A tool set for automatic performance analysis of parallel programs B Mohr, F Wolf Euro-Par 2003 Parallel Processing: 9th International Euro-Par Conference …, 2003 | 174 | 2003 |
Design and prototype of a performance tool interface for OpenMP B Mohr, AD Malony, S Shende, F Wolf The Journal of Supercomputing 23, 105-128, 2002 | 159 | 2002 |
Open trace format 2: The next generation of scalable trace formats and support libraries D Eschweiler, M Wagner, M Geimer, A Knüpfer, WE Nagel, F Wolf Applications, Tools and Techniques on the Road to Exascale Computing, 481-490, 2012 | 155 | 2012 |
Scalable parallel trace-based performance analysis M Geimer, F Wolf, BJN Wylie, B Mohr Recent Advances in Parallel Virtual Machine and Message Passing Interface …, 2006 | 137 | 2006 |
Scalable massively parallel I/O to task-local files W Frings, F Wolf, V Petkov Proceedings of the Conference on High Performance Computing Networking …, 2009 | 123 | 2009 |
Score-P: A unified performance measurement system for petascale applications DA Mey, S Biersdorf, C Bischof, K Diethelm, D Eschweiler, M Gerndt, ... Competence in High Performance Computing 2010: Proceedings of an …, 2012 | 105 | 2012 |
Identifying the root causes of wait states in large-scale parallel applications D Böhme, M Geimer, L Arnold, F Voigtlaender, F Wolf ACM Transactions on Parallel Computing (TOPC) 3 (2), 1-24, 2016 | 93 | 2016 |
Usage of the SCALASCA toolset for scalable performance analysis of large-scale parallel applications F Wolf, BJN Wylie, E Abraham, D Becker, W Frings, K Fürlinger, M Geimer, ... Tools for High Performance Computing: Proceedings of the 2nd International …, 2008 | 93 | 2008 |
Knowledge specification for automatic performance analysis T Fahringer, M Gerndt, B Mohr, F Wolf, G Riley, JL Träff Technical Report FZJ-ZAM-IB-2001-08, ESPRIT IV Working Group APART …, 2001 | 90 | 2001 |
A batch system with efficient adaptive scheduling for malleable and evolving applications S Prabhakaran, M Neumann, S Rinke, F Wolf, A Gupta, LV Kale 2015 IEEE international parallel and distributed processing symposium, 429-438, 2015 | 88 | 2015 |
Towards a performance tool interface for OpenMP: An approach based on directive rewriting B Mohr, AD Malony, S Shende, F Wolf Proceedings of the Third Workshop on OpenMP (EWOMP’01), 2001 | 86 | 2001 |
Interoperation of world‐wide production e‐Science infrastructures M Riedel, E Laure, T Soddemann, L Field, JP Navarro, J Casey, ... Concurrency and Computation: Practice and Experience 21 (8), 961-990, 2009 | 84 | 2009 |
A scalable tool architecture for diagnosing wait states in massively parallel applications M Geimer, F Wolf, BJN Wylie, B Mohr Parallel Computing 35 (7), 375-388, 2009 | 80 | 2009 |
Scalable critical-path based performance analysis D Böhme, F Wolf, BR de Supinski, M Schulz, M Geimer 2012 IEEE 26th International Parallel and Distributed Processing Symposium …, 2012 | 78 | 2012 |
An algebra for cross-experiment performance analysis F Song, F Wolf, N Bhatia, J Dongarra, S Moore International Conference on Parallel Processing, 2004. ICPP 2004., 63-72, 2004 | 77 | 2004 |
Fast multi-parameter performance modeling A Calotoiu, D Beckinsale, CW Earl, T Hoefler, I Karlin, M Schulz, F Wolf 2016 IEEE International Conference on Cluster Computing (CLUSTER), 172-181, 2016 | 72 | 2016 |