This project is developing PAPI, which will provide tool designers and application engineers with a consistent interface and methodology for the use of low-level performance counter hardware found across the entire compute system (i.e. CPUs, GPUs, on/off-chip memory, interconnects, I/O system, energy/power, etc.). PAPI will enable users to see, in near real time, the relations between software performance and hardware events across the entire computer system.
Exa-PAPI builds on the latest PAPI project and will be extended with:
The objective is to enable monitoring of both types of performance events—hardware- and software-related events—in a uniform way, through one consistent PAPI interface. Third-party tools and application developers will have to handle only a single hook to PAPI in order to access all hardware performance counters in a system, including the new software-defined events.
PAPI 6.0.0 was released March 4, 2020. This release includes a new API for SDEs (Software Defined Events), a major revision of the 'high-level API', and several new components, including ROCM and ROCM_SMI (for AMD GPUs), powercap_ppc and sensors_ppc (for IBM Power9 and later), SDE, and the IO component (exposes I/O statistics exported by the Linux kernel). Furthermore, PAPI 6.0 ships CAT, a new Counter Analysis Toolkit that assists with native performance counter disambiguation through micro-benchmarks.
For specific and detailed information on changes made for this release, see ChangeLogP600.txt for filenames or keywords of interest and change summaries, or go directly to the PAPI git repository.
This release is the result of efforts from many people. The PAPI team would like to express special Thanks to Vince Weaver, Stephane Eranian (for libpfm4), William Cohen, Steve Kaufmann, Phil Mucci, Kevin Huck, Yunqiang Su, Carl Love, Andreas Beckmann, Al Grant and Evgeny Shcherbakov.
To verify the integrity of the download, check the MD5 hash 'md5sum papi-6.0.0.tar.gz':
|Effortless Monitoring of Arithmetic Intensity with PAPIâs Counter Analysis Toolkit,” 13th International Workshop on Parallel Tools for High Performance Computing, Dresden, Germany, Springer International Publishing, September 2020.“|
|Roadmap for Refactoring classic PAPI to PAPI++: Part II: Formulation of Roadmap based on Survey Results,” PAPI++ Working Notes, no. 2, ICL-UT-20-09: Innovative Computing Laboratory, University of Tennessee, July 2020.“|
|Redesigning PAPIâs High-Level API,” Innovative Computing Laboratory Technical Report, no. ICL-UT-20-03: University of Tennessee, February 2020.“|
|Formulation of Requirements for new PAPI++ Software Package: Part I: Survey Results,” PAPI++ Working Notes, no. 1, ICL-UT-20-02: Innovative Computing Laboratory, University of Tennessee Knoxville, January 2020.“|
|PAPI Software-Defined Events for in-Depth Performance Analysis,” The International Journal of High Performance Computing Applications, vol. 33, issue 6, pp. 1113-1127, November 2019.“|
|What it Takes to keep PAPI Instrumental for the HPC Community,” 1st Workshop on Sustainable Scientific Software (CW3S19), Collegeville, Minnesota, July 2019.“|
|Software-Defined Events through PAPI,” 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Rio de Janeiro, Brazil, IEEE, May 2019. DOI: 10.1109/IPDPSW.2019.00069“|
|Counter Inspection Toolkit: Making Sense out of Hardware Performance Events,” 11th International Workshop on Parallel Tools for High Performance Computing, Dresden, Germany, Cham, Switzerland: Springer, February 2019. DOI: 10.1007/978-3-030-11987-4_2“|
|Investigating Power Capping toward Energy-Efficient Scientific Applications,” Concurrency Computation: Practice and Experience, vol. 2018, issue e4485, pp. 1-14, April 2018. DOI: 10.1002/cpe.4485“|
|Performance Analysis and Debugging Tools at Scale,” Exascale Scientific Applications: Scalability and Performance Portability: Chapman & Hall / CRC Press, pp. 17-50, November 2017. DOI: 10.1201/b21930“|
|Power-aware Computing: Measurement, Control, and Performance Analysis for Intel Xeon Phi,” 2017 IEEE High Performance Extreme Computing Conference (HPEC'17), Best Paper Finalist, Waltham, MA, IEEE, September 2017. DOI: 10.1109/HPEC.2017.8091085“|
|PAPI's new Software-Defined Events for in-depth Performance Analysis , Dresden, Germany, 13th Parallel Tools Workshop, September 2019.|
|Does your tool support PAPI SDEs yet? , Tahoe City, CA, 13th Scalable Tools Workshop, July 2019.|
|What it Takes to keep PAPI Instrumental for the HPC Community , Collegeville, MN, The 2019 Collegeville Workshop on Sustainable Scientific Software (CW3S19), July 2019.|
|Is your scheduling good? How would you know? , Bordeaux, France, 14th Scheduling for Large Scale Systems Workshop, June 2019.|
|Understanding Native Event Semantics , Knoxville, TN, 9th JLESC Workshop, April 2019.|
|PAPI's New Software-Defined Events for In-Depth Performance Analysis , Lyon, France, CCDSC 2018: Workshop on Clusters, Clouds, and Data for Scientific Computing, September 2018.|
|Software-Defined Events through PAPI for In-Depth Analysis of Application Performance , Basel, Switzerland, 5th Platform for Advanced Scientific Computing Conference (PASC18), July 2018.|
|PAPI: Counting outside the Box , Barcelona, Spain, 8th JLESC Meeting, April 2018.|
Exa-PAPI is part of ICL's involvment in the Exascale Computing Project (ECP). The ECP was established with the goals of maximizing the benefits of high-performance computing (HPC) for the United States and accelerating the development of a capable exascale computing ecosystem. Exascale refers to computing systems at least 50 times faster than the nation’s most powerful supercomputers in use today.
The ECP is a collaborative effort of two U.S. Department of Energy organizations – the Office of Science (DOE-SC) and the National Nuclear Security Administration (NNSA).