XKaapi: A Runtime System for Data-Flow Task Programming on Heterogeneous Architectures

Gautier, Thierry; Lima, João Vicente Ferreira; Maillard, Nicolas; Raffin, Bruno

doi:10.1109/ipdps.2013.66

Cited by 153 publications

(135 citation statements)

References 31 publications

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“…We use the OpenMP library with task parallelization using two runtime implementations: libgomp [22], the GNU implementation of the OpenMP Application Programming Interface and libkomp an implementation of the OpenMP standard based on the XKaapi library [17]. Expressing parallelism using tasks allows the programmer to choose a finer grain parallelization.…”

Section: Introductionmentioning

confidence: 99%

Recursion based parallelization of exact dense linear algebra routines for Gaussian elimination

et al. 2016

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We present block algorithms and their implementation for the parallelization of sub-cubic Gaussian elimination on shared memory architectures. Contrarily to the classical cubic algorithms in parallel numerical linear algebra, we focus here on recursive algorithms and coarse grain parallelization. Indeed, sub-cubic matrix arithmetic can only be achieved through recursive algorithms making coarse grain block algorithms perform more efficiently than fine grain ones. This work is motivated by the design and implementation of dense linear algebra over a finite field, where fast matrix multiplication is used extensively and where costly modular reductions also advocate for coarse grain block decomposition. We incrementally build efficient kernels, for matrix multiplication first, then triangular system solving, on top of which a recursive PLUQ decomposition algorithm is built. We study the parallelization of these kernels using several algorithmic variants: either iterative or recursive and using different splitting strategies. Experiments show that recursive adaptive methods for matrix multiplication, hybrid recursive-iterative methods for triangular system solve and tile recursive versions of the PLUQ decomposition, together with various data mapping policies, provide the best performance on a 32 cores NUMA architecture. Overall, we show that the overhead of modular reductions is more than compensated by the fast linear algebra algorithms and that exact dense linear algebra matches the performance of full rank reference numerical software even in the presence of rank deficiencies.

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Section: Introductionmentioning

confidence: 99%

Recursion based parallelization of exact dense linear algebra routines for Gaussian elimination

et al. 2016

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“…Examples are [7], exclusively focused on linear algebra, [37], used in the backend of the PLASMA library, or [1], particularly focused on heterogeneous computing. There are also projects that provide both a library to build tasks that express their dependencies by means of its API datatypes [14] and a runtime for OpenMP [4].…”

Section: Related Workmentioning

confidence: 99%

A Comparison of Task Parallel Frameworks based on Implicit Dependencies in Multi-core Environments

Fraguela

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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Abstract-The larger flexibility that task parallelism offers with respect to data parallelism comes at the cost of a higher complexity due to the variety of tasks and the arbitrary patterns of dependences that they can exhibit. These dependencies should be expressed not only correctly, but optimally, i.e. avoiding over-constraints, in order to obtain the maximum performance from the underlying hardware. There have been many proposals to facilitate this non-trivial task, particularly within the scope of nowadays ubiquitous multi-core architectures. A very interesting family of solutions because of their large scope of application, ease of use and potential performance are those in which the user declares the dependences of each task, and lets the parallel programming framework figure out which are the concrete dependences that appear at runtime and schedule accordingly the parallel tasks. Nevertheless, as far as we know, there are no comparative studies of them that help users identify their relative advantages. In this paper we describe and evaluate four tools of this class discussing the strengths and weaknesses we have found in their use.

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“…The popularization of runtime systems such as X-Kaapi [8], StarPU [9] or OmpSs [10] have proven their efficiency on heterogeneous architectures. Among them, HARTS [2] relies on abstract concepts between application and hardware to distribute and manage the work flow and the associated data movements.…”

Section: Heterogeneous Abstract Runtime System (Harts)mentioning

confidence: 99%

Using Runtime Systems Tools to Implement Efficient Preconditioners for Heterogeneous Architectures

Roussel

Gratien

Gautier

2016

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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-Solving large sparse linear systems is a time-consuming step in basin modeling or reservoir simulation. The choice of a robust preconditioner strongly impact the performance of the overall simulation. Heterogeneous architectures based on General Purpose computing on Graphic Processing Units (GPGPU) or many-core architectures introduce programming challenges which can be managed in a transparent way for developer with the use of runtime systems. Nevertheless, algorithms need to be well suited for these massively parallel architectures. In this paper, we present preconditioning techniques which enable to take advantage of emerging architectures. We also present our task-based implementations through the use of the HARTS (Heterogeneous Abstract RunTime System) runtime system, which aims to manage the recent architectures. We focus on two preconditoners. The first is ILU(0) preconditioner implemented on distributing memory systems. The second one is a multi-level domain decomposition method implemented on a shared-memory system. Obtained results are then presented on corresponding architectures, which open the way to discuss on the scalability of such methods according to numerical performances while keeping in mind that the next step is to propose a massively parallel implementations of these techniques.Résumé -Utilisation de moteurs exécutifs pour une implémentation efficace de préconditionneurs sur architectures hétérogènes -La résolution de grands systèmes linéaires creux est une étape coûteuse de la modélisation de bassin ou la simulation de réservoir, et le choix de préconditionneurs robustes impacte fortement la performance de ceux-ci. Les architectures hétérogènes à base d'architectures de type General Purpose computing on Graphic Processing Units (GPGPU) ou manycoeurs introduisent de nouveaux challenges de programmation qui peuvent être gérés de manière transparente pour le développeur grâce à l'utilisation de moteurs exécutifs. Il faut néanmoins que les algorithmes soient adaptés à ce type d'architectures massivement parallèles. Nous présentons dans cet article les méthodes de préconditionnement permettant de tirer au maximum avantage des architectures émergentes. Nous présentons également une implémentation de celles-ci avec le moteur exécutif HARTS (Heterogeneous Abstract RunTime System) conçu pour gérer de manière efficace les architectures hétérogènes. Nous détaillons les implémentations de deux préconditionneurs. L'un est un préconditionneur ILU(0) porté sur architecture distribuée. L'autre est une méthode de décomposition de domaines multi-niveaux implémentée sur architecture à mémoire partagée. Après avoir détaillé ces deux préconditionneurs, une étude sur la portabilité de telles méthodes et leur scalabilité sur différents modèles d'architectures sera menée.

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XKaapi: A Runtime System for Data-Flow Task Programming on Heterogeneous Architectures

Cited by 153 publications

References 31 publications

Recursion based parallelization of exact dense linear algebra routines for Gaussian elimination

Recursion based parallelization of exact dense linear algebra routines for Gaussian elimination

A Comparison of Task Parallel Frameworks based on Implicit Dependencies in Multi-core Environments

Using Runtime Systems Tools to Implement Efficient Preconditioners for Heterogeneous Architectures

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