PIPER - Revision history

Pinfold at 05:03, July 10, 2023

2023-07-10T05:03:18Z

← Older revision		Revision as of 05:03, July 10, 2023
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	\| website =		\| website =
	\| team-members = LLNL, PNNL, Rice Uni., U. of Maryland, U. of Utah, U. of Wisconsin		\| team-members = LLNL, PNNL, Rice Uni., U. of Maryland, U. of Utah, U. of Wisconsin
	\| pi = Martin Schulz ~~(LLNL)~~		\| pi = [[Martin Schulz]]
	\| co-pi = Peer-Timo Bremer, Todd Gamblin, Jeff Hollingsworth, John Mellor-Crummey, Barton Miller, Valerio Pascucci, Nathan Tallent}}		\| co-pi = Peer-Timo Bremer, Todd Gamblin, Jeff Hollingsworth, John Mellor-Crummey, Barton Miller, Valerio Pascucci, Nathan Tallent}}

imported>Sonia at 01:39, September 21, 2016

2016-09-21T01:39:59Z

← Older revision		Revision as of 01:39, September 21, 2016
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	To provide this essential functionality for the extreme-scale software stack, we are developing new abstractions, techniques, and novel tools for data measurement, analysis, attribution, diagnostic feedback, and visualization. This enables Performance Insights for Programmers and Exascale Runtimes (PIPER). This project cuts across the entire software stack by collecting data in all system components through novel abstractions and integrated introspection, providing data attribution and analysis in a system-wide context and across programming models, enabling global correlations of performance data from independent sources in different domains, and delivering dynamic feedback to run-time systems and applications through auto-tuning as well as interactive visualizations.		To provide this essential functionality for the extreme-scale software stack, we are developing new abstractions, techniques, and novel tools for data measurement, analysis, attribution, diagnostic feedback, and visualization. This enables Performance Insights for Programmers and Exascale Runtimes (PIPER). This project cuts across the entire software stack by collecting data in all system components through novel abstractions and integrated introspection, providing data attribution and analysis in a system-wide context and across programming models, enabling global correlations of performance data from independent sources in different domains, and delivering dynamic feedback to run-time systems and applications through auto-tuning as well as interactive visualizations.

	== Project Impact == ~~[https://xstackwiki.modelado.org/images/b/b8/PIPER-Impact_summary.pdf]~~		== Project Impact ==

	* [https://xstackwiki.modelado.org/images/b/b8/PIPER-Impact_summary.pdf PIPER Project Impact]		* [https://xstackwiki.modelado.org/images/b/b8/PIPER-Impact_summary.pdf PIPER Project Impact]

imported>Sonia at 01:39, September 21, 2016

2016-09-21T01:39:33Z

← Older revision		Revision as of 01:39, September 21, 2016
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	To provide this essential functionality for the extreme-scale software stack, we are developing new abstractions, techniques, and novel tools for data measurement, analysis, attribution, diagnostic feedback, and visualization. This enables Performance Insights for Programmers and Exascale Runtimes (PIPER). This project cuts across the entire software stack by collecting data in all system components through novel abstractions and integrated introspection, providing data attribution and analysis in a system-wide context and across programming models, enabling global correlations of performance data from independent sources in different domains, and delivering dynamic feedback to run-time systems and applications through auto-tuning as well as interactive visualizations.		To provide this essential functionality for the extreme-scale software stack, we are developing new abstractions, techniques, and novel tools for data measurement, analysis, attribution, diagnostic feedback, and visualization. This enables Performance Insights for Programmers and Exascale Runtimes (PIPER). This project cuts across the entire software stack by collecting data in all system components through novel abstractions and integrated introspection, providing data attribution and analysis in a system-wide context and across programming models, enabling global correlations of performance data from independent sources in different domains, and delivering dynamic feedback to run-time systems and applications through auto-tuning as well as interactive visualizations.

	== Project Impact ==		== Project Impact == [https://xstackwiki.modelado.org/images/b/b8/PIPER-Impact_summary.pdf]

	* [https://xstackwiki.modelado.org/images/b/b8/PIPER-Impact_summary.pdf PIPER Project Impact]		* [https://xstackwiki.modelado.org/images/b/b8/PIPER-Impact_summary.pdf PIPER Project Impact]

imported>Sonia at 01:38, September 21, 2016

2016-09-21T01:38:27Z

← Older revision		Revision as of 01:38, September 21, 2016
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	To provide this essential functionality for the extreme-scale software stack, we are developing new abstractions, techniques, and novel tools for data measurement, analysis, attribution, diagnostic feedback, and visualization. This enables Performance Insights for Programmers and Exascale Runtimes (PIPER). This project cuts across the entire software stack by collecting data in all system components through novel abstractions and integrated introspection, providing data attribution and analysis in a system-wide context and across programming models, enabling global correlations of performance data from independent sources in different domains, and delivering dynamic feedback to run-time systems and applications through auto-tuning as well as interactive visualizations.		To provide this essential functionality for the extreme-scale software stack, we are developing new abstractions, techniques, and novel tools for data measurement, analysis, attribution, diagnostic feedback, and visualization. This enables Performance Insights for Programmers and Exascale Runtimes (PIPER). This project cuts across the entire software stack by collecting data in all system components through novel abstractions and integrated introspection, providing data attribution and analysis in a system-wide context and across programming models, enabling global correlations of performance data from independent sources in different domains, and delivering dynamic feedback to run-time systems and applications through auto-tuning as well as interactive visualizations.

	== Project Impact --		== Project Impact ==

	* [https://xstackwiki.modelado.org/images/b/b8/PIPER-Impact_summary.pdf PIPER Project Impact]		* [https://xstackwiki.modelado.org/images/b/b8/PIPER-Impact_summary.pdf PIPER Project Impact]

imported>Sonia at 01:37, September 21, 2016

2016-09-21T01:37:39Z

← Older revision		Revision as of 01:37, September 21, 2016
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	To provide this essential functionality for the extreme-scale software stack, we are developing new abstractions, techniques, and novel tools for data measurement, analysis, attribution, diagnostic feedback, and visualization. This enables Performance Insights for Programmers and Exascale Runtimes (PIPER). This project cuts across the entire software stack by collecting data in all system components through novel abstractions and integrated introspection, providing data attribution and analysis in a system-wide context and across programming models, enabling global correlations of performance data from independent sources in different domains, and delivering dynamic feedback to run-time systems and applications through auto-tuning as well as interactive visualizations.		To provide this essential functionality for the extreme-scale software stack, we are developing new abstractions, techniques, and novel tools for data measurement, analysis, attribution, diagnostic feedback, and visualization. This enables Performance Insights for Programmers and Exascale Runtimes (PIPER). This project cuts across the entire software stack by collecting data in all system components through novel abstractions and integrated introspection, providing data attribution and analysis in a system-wide context and across programming models, enabling global correlations of performance data from independent sources in different domains, and delivering dynamic feedback to run-time systems and applications through auto-tuning as well as interactive visualizations.

			== Project Impact --

	== ~~Approch~~ ==		* [https://xstackwiki.modelado.org/images/b/b8/PIPER-Impact_summary.pdf PIPER Project Impact]

			== Approach ==

	PIPER consists of four thrust areas, organized into three phases.		PIPER consists of four thrust areas, organized into three phases.

imported>Schulzm at 14:20, December 7, 2015

2015-12-07T14:20:13Z

← Older revision		Revision as of 14:20, December 7, 2015
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	* [https://github.com/scalability-llnl/ravel Ravel - MPI trace visualization using logical timelines]		* [https://github.com/scalability-llnl/ravel Ravel - MPI trace visualization using logical timelines]
	* [https://github.com/scalability-llnl/MemAxes MemAxes/Mitos - Visualization of on-node memory traffic]		* [https://github.com/scalability-llnl/MemAxes MemAxes/Mitos - Visualization of on-node memory traffic]


	=== Auto-Tuning ===		=== Auto-Tuning ===

	* [http://www.dyninst.org/harmony Active Harmony 4.5 - Multiparameter Tuning Systme]		* [http://www.dyninst.org/harmony Active Harmony 4.5 - Multiparameter Tuning Systme]

imported>Schulzm at 14:14, December 7, 2015

2015-12-07T14:14:58Z

← Older revision		Revision as of 14:14, December 7, 2015
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	analysis reporting tools informing the user. Such a system also enables dynamic tuning,		analysis reporting tools informing the user. Such a system also enables dynamic tuning,
	and feedback-directed optimization.		and feedback-directed optimization.


			== Released Software ==

			=== Infrastructure Elements ===

			* [http://www.paradyn.org/html/dyninst9.0.3-features.html Dyninst 9.0 - Dynamic Instrumentation Library]
			* [http://www.paradyn.org/html/mrnet5.0.0-features.html MRNet 5.0 - Tree-based Overlay Network]
			* [https://github.com/OpenMPToolsInterface OMPT/OMPD - Tool Interfaces for OpenMP]

			=== Bottleneck Detection / Analysis ===

			* [http://hpctoolkit.org/ HPCToolkit - Sampling centric analysis and blame shifting]

			=== Performance Visualization ===

			* [https://computation.llnl.gov/project/performance-analysis-through-visualization/software.php Boxfish - Visual performance analysis through data centric mappings]
			* [https://github.com/scalability-llnl/ravel Ravel - MPI trace visualization using logical timelines]
			* [https://github.com/scalability-llnl/MemAxes MemAxes/Mitos - Visualization of on-node memory traffic]


			=== Auto-Tuning ===

			* [http://www.dyninst.org/harmony Active Harmony 4.5 - Multiparameter Tuning Systme]

imported>Schulzm at 07:59, September 28, 2013

2013-09-28T07:59:46Z

← Older revision		Revision as of 07:59, September 28, 2013
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	\| co-pi = Peer-Timo Bremer, Todd Gamblin, Jeff Hollingsworth, John Mellor-Crummey, Barton Miller, Valerio Pascucci, Nathan Tallent}}		\| co-pi = Peer-Timo Bremer, Todd Gamblin, Jeff Hollingsworth, John Mellor-Crummey, Barton Miller, Valerio Pascucci, Nathan Tallent}}

	The PIPER project is developing new techniques for measuring, analyzing, attributing, and presenting performance data on exascale systems.		The PIPER (Performance Insight for Programmers and Exascale Runtimes) project is developing new techniques for measuring, analyzing, attributing, and presenting performance data on exascale systems.

	== Team Members ==		== Team Members ==

imported>Schulzm at 07:59, September 28, 2013

2013-09-28T07:59:01Z

← Older revision		Revision as of 07:59, September 28, 2013
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	\| co-pi = Peer-Timo Bremer, Todd Gamblin, Jeff Hollingsworth, John Mellor-Crummey, Barton Miller, Valerio Pascucci, Nathan Tallent}}		\| co-pi = Peer-Timo Bremer, Todd Gamblin, Jeff Hollingsworth, John Mellor-Crummey, Barton Miller, Valerio Pascucci, Nathan Tallent}}

	~~Description goes here~~		The PIPER project is developing new techniques for measuring, analyzing, attributing, and presenting performance data on exascale systems.

	== Team Members ==		== Team Members ==

imported>Schulzm: /* Architecture and Interaction with the Software Stack */

2013-09-28T07:53:42Z

Architecture and Interaction with the Software Stack

← Older revision		Revision as of 07:53, September 28, 2013
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	== Architecture and Interaction with the Software Stack ==		== Architecture and Interaction with the Software Stack ==

	We ~~will~~ implement our research in a set of modular components that can be deployed		We implement our research in a set of modular components that can be deployed
	across various execution and programming models, covering both legacy (MPI+X) models and new models developed in other X-Stack2 projects. Wherever possible, we ~~will~~ leverage the extensive		across various execution and programming models, covering both legacy (MPI+X) models and new models developed in other X-Stack2 projects. Wherever possible, we leverage the extensive
	tool infrastructures available through prior work in our project team and integrate		tool infrastructures available through prior work in our project team and integrate
	the results of our research back into these existing production-level tool sets.		the results of our research back into these existing production-level tool sets.
	Furthermore, we ~~will~~ make the results of our research available to a broad audience		Furthermore, we make the results of our research available to a broad audience
	and work with the larger tools community to achieve a wider adaption.		and work with the larger tools community to achieve a wider adaption.
	The figure below provides an initial high-level sketch of our envisioned architecture		The figure below provides an initial high-level sketch of our envisioned architecture
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	[[File:Piper_architecture.png]]		[[File:Piper_architecture.png]]

	We ~~will~~ target measurements from the entire hardware/software stack. That is,		We target measurements from the entire hardware/software stack. That is,
	we expect to use measurements from both the underlying system hardware as well		we expect to use measurements from both the underlying system hardware as well
	as custom measurements derived from the application. The measurements themselves		as custom measurements derived from the application. The measurements themselves
	~~will~~ leverage a series of adaptive instrumentation techniques. As part of the		leverage a series of adaptive instrumentation techniques. As part of the
	measurement operation, we ~~will~~ associate the measurements with a local		measurement operation, we associate the measurements with a local
	call stack. The correlated local stack/performance measurement data ~~will feed~~		call stack. The correlated local stack/performance measurement data feeds
	an analysis pipeline consisting of both node-local analysis methods, and		an analysis pipeline consisting of both node-local analysis methods, and
	distributed, wider-context analysis methods.		distributed, wider-context analysis methods.
	The resulting data		The resulting data
	store ~~will support~~ a high-level query interface used by visualization and data		store supports a high-level query interface used by visualization and data
	analysis reporting tools informing the user. Such a system also enables dynamic tuning,		analysis reporting tools informing the user. Such a system also enables dynamic tuning,
	and feedback-directed optimization.		and feedback-directed optimization.