Applications - Revision history

imported>Cnewburn: /* Categories of Hierarchical Algorithms */

2017-04-17T17:01:00Z

Categories of Hierarchical Algorithms

@@ Line 54: / Line 54: @@
 = Categories of Hierarchical Algorithms =
-David Keyes volunteered to offer several such categories.
+David Keyes, Kaust
 = Charm++ =

imported>Richards12 at 20:48, April 12, 2017

2017-04-12T20:48:43Z

← Older revision		Revision as of 20:48, April 12, 2017
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	either have more resources, or similar.		either have more resources, or similar.

	= ~~Tranport~~ Monte Carlo =		= Transport Monte Carlo =

			Point of Contact: [mailto:richards12@llnl.gov David Richards]

	== Description ==		== Description ==

imported>Richards12 at 16:15, April 11, 2017

2017-04-11T16:15:59Z

@@ Line 141: / Line 141: @@
 a GPU and onto the available host CPUs, or a different subset of GPUs that
 either have more resources, or similar.
 = Add more =

imported>Cnewburn: /* VMD */

2017-03-21T12:16:33Z

VMD

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 VMD has a large user community with over 100,000 registered users, and it is used on a broad range of hardware and operating system platforms that covers tablets, conventional laptops, PCs, as well as large clusters and supercomputers, and systems with and without GPU accelerators, recent many-core CPUs with wide vector units and some that have extensive hardware SMT.  On clusters and supercomputers, VMD uses MPI for distributed memory execution.  VMD implements a diverse range of internal algorithms and makes increasing use of third party software components and external libraries, particularly those that offer high performance platform-optimized algorithms, e.g., for linear algebra, FFTs, sequence alignment (bioinformatics), and others.  VMD currently makes use of a light weight tasking system that originated in the Tachyon ray tracing library (that VMD also uses), which provides cross-platform APIs for threading, synchronization, and tasking.  The tasking implementation in VMD and Tachyon incorporates special features to allow consideration of jobs that span both CPUs and GPU accelerators, handle unexpected runtime errors, and allow retry or relocation of task indices that contained work that could not be executed by an accelerator and migrates them to host CPUs, etc.
 It should be a short-term effort (under a week?) to reimplement the key parts of the VMD/Tachyon tasking abstractions on top of a HiHat implementation, which would enable performance and usability comparisons to be made even with early HiHat implementations.
 = Add more =

imported>Cnewburn at 11:52, March 21, 2017

2017-03-21T11:52:49Z

← Older revision		Revision as of 11:52, March 21, 2017
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	[http://charm.cs.illinois.edu/ Charm++] is a task-based asynchronous parallel runtime system used in applications such as [http://www-hpcc.astro.washington.edu/tools/changa.html ChaNGa] and [https://www-s.ks.uiuc.edu/Research/namd/ NAMD], among others. The runtime system has mechanisms to control the placement, scheduling, and execution target of the program, all of which could use HHAT components when available. A key property is that these properties are generally decided dynamically during execution by the runtime system, and retaining this dynamism is critical for the ideology of the software. Of particular interest is infrastructure for managing heterogeneous execution, data location, network communication, and (user and system level) threading.		[http://charm.cs.illinois.edu/ Charm++] is a task-based asynchronous parallel runtime system used in applications such as [http://www-hpcc.astro.washington.edu/tools/changa.html ChaNGa] and [https://www-s.ks.uiuc.edu/Research/namd/ NAMD], among others. The runtime system has mechanisms to control the placement, scheduling, and execution target of the program, all of which could use HHAT components when available. A key property is that these properties are generally decided dynamically during execution by the runtime system, and retaining this dynamism is critical for the ideology of the software. Of particular interest is infrastructure for managing heterogeneous execution, data location, network communication, and (user and system level) threading.

			= VMD =
			VMD is a tool for preparing, analyzing, and visualizing molecular dynamics simulations and lattice cell simulations.

			VMD has a large user community with over 100,000 registered users, and it is used on a broad range of hardware and operating system platforms that covers tablets, conventional laptops, PCs, as well as large clusters and supercomputers, and systems with and without GPU accelerators, recent many-core CPUs with wide vector units and some that have extensive hardware SMT. On clusters and supercomputers, VMD uses MPI for distributed memory execution. VMD implements a diverse range of internal algorithms and makes increasing use of third party software components and external libraries, particularly those that offer high performance platform-optimized algorithms, e.g., for linear algebra, FFTs, sequence alignment (bioinformatics), and others. VMD currently makes use of a light weight tasking system that originated in the Tachyon ray tracing library (that VMD also uses), which provides cross-platform APIs for threading, synchronization, and tasking. The tasking implementation in VMD and Tachyon incorporates special features to allow consideration of jobs that span both CPUs and GPU accelerators, handle unexpected runtime errors, and allow retry or relocation of task indices that contained work that could not be executed by an accelerator and migrates them to host CPUs, etc.

			It should be a short-term effort (under a week?) to reimplement the key parts of the VMD/Tachyon tasking abstractions on top of a HiHat implementation, which would enable performance and usability comparisons to be made even with early HiHat implementations.

	= Add more =		= Add more =

imported>Rabuch2 at 09:41, March 21, 2017

2017-03-21T09:41:03Z

← Older revision		Revision as of 09:41, March 21, 2017
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	= Categories of Hierarchical Algorithms =		= Categories of Hierarchical Algorithms =
	David Keyes volunteered to offer several such categories.		David Keyes volunteered to offer several such categories.

			= Charm++ =

			[http://charm.cs.illinois.edu/ Charm++] is a task-based asynchronous parallel runtime system used in applications such as [http://www-hpcc.astro.washington.edu/tools/changa.html ChaNGa] and [https://www-s.ks.uiuc.edu/Research/namd/ NAMD], among others. The runtime system has mechanisms to control the placement, scheduling, and execution target of the program, all of which could use HHAT components when available. A key property is that these properties are generally decided dynamically during execution by the runtime system, and retaining this dynamism is critical for the ideology of the software. Of particular interest is infrastructure for managing heterogeneous execution, data location, network communication, and (user and system level) threading.

	= Add more =		= Add more =

imported>Cnewburn at 04:29, March 20, 2017

2017-03-20T04:29:26Z

← Older revision		Revision as of 04:29, March 20, 2017
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	The propose of this page is to gather user applications that serve as poster children for HHAT.		The propose of this page is to gather user applications that serve as poster children for HHAT.

			A link to get back up to the parent page is [[Hierarchical_Heterogeneous_Asynchronous_Tasking \| here]].

	Please this this approach		Please this this approach

imported>Mbianco: /* PASC apps */

2017-03-15T16:31:12Z

PASC apps

← Older revision		Revision as of 16:31, March 15, 2017
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	= PASC apps =		= PASC apps =
	Platform for Advanced Scientific Computing, Switzerland		Platform for Advanced Scientific Computing, Switzerland

			Shortly there will be more information about this initiative, but generally speaking Switzerland is founding development projects for libraries and applications with strong performance-portable code in areas like weather and climate, material science and molecular dynamics. Many computational kernels there are based on linear algebra, some other are based on finite differences.


			=== GridTools ===
			One of the projects is producing a set of (C++ header-based) libraries for finite differences in weather and climate applications. The main idea is to allow for the numeric operators to be expressed in a possibly grid-agnostic way, while the grid, wether representing a local region or the whole globe, is plugged in a second step. The libraries provide means of composition for the different operators , so that to allow to increase the computation-intensity of otherwise memory bound stencils, allowing specification of boundary conditions in a very flexible way, perform nearest neighbor communication operations, and domain decomposition. The central component of the set of libraries is the '''composition''' of different operators. All of the libraries, however, have '''backends''' to execute the requested tasks on specific architectures. Currently supported are x86-based multicores and nVidia GPUs. Xeon Phi is in a early stage of implementation. A plan to orchestrate the different activities (stencil execution, boundary conditions, communications) using some for of dynamic scheduling is one of the goals we are pursuing. Employing a more dynamic execution policy for each computational phase is not currently considered a urgent matter, since the scheduling of the operations is basically known at compile time. Future directions may include adopting a more dynamic approach in both high- and low-levels if such an integration is beneficial for performance.

	= ISV apps =		= ISV apps =

imported>Max.grossman at 21:22, February 27, 2017

2017-02-27T21:22:11Z

← Older revision		Revision as of 21:22, February 27, 2017
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	= Habanero Tasking Micro-Benchmark Suite =		= Habanero Tasking Micro-Benchmark Suite =

	[https://github.com/habanero-rice/tasking-micro-benchmark-suite]		[https://github.com/habanero-rice/tasking-micro-benchmark-suite Github page]

	This micro-benchmarking suite is a work-in-progress intended to compare low-level overheads across low-level tasking runtimes (e.g. Realm, OCR). The above Github page includes a high-level description of each micro-benchmark, as well as source code for each micro-benchmark across a variety of low-level runtimes. These micro-benchmarks were curated across performance regression suites from a variety of tasking runtimes, and so is intended to enable one-to-one comparison of runtime efficiencies (as much as possible).		This micro-benchmarking suite is a work-in-progress intended to compare low-level overheads across low-level tasking runtimes (e.g. Realm, OCR). The above Github page includes a high-level description of each micro-benchmark, as well as source code for each micro-benchmark across a variety of low-level runtimes. These micro-benchmarks were curated across performance regression suites from a variety of tasking runtimes, and so is intended to enable one-to-one comparison of runtime efficiencies (as much as possible).

imported>Max.grossman at 21:21, February 27, 2017

2017-02-27T21:21:40Z

← Older revision		Revision as of 21:21, February 27, 2017
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	* CoMD		* CoMD
	* HPGMG		* HPGMG

			= Habanero Tasking Micro-Benchmark Suite =

			[https://github.com/habanero-rice/tasking-micro-benchmark-suite]

			This micro-benchmarking suite is a work-in-progress intended to compare low-level overheads across low-level tasking runtimes (e.g. Realm, OCR). The above Github page includes a high-level description of each micro-benchmark, as well as source code for each micro-benchmark across a variety of low-level runtimes. These micro-benchmarks were curated across performance regression suites from a variety of tasking runtimes, and so is intended to enable one-to-one comparison of runtime efficiencies (as much as possible).

	= Categories of Hierarchical Algorithms =		= Categories of Hierarchical Algorithms =