ParILUT---A New Parallel Threshold ILU Factorization

Anzt, Hartwig; Chow, Edmond; Dongarra, Jack

doi:10.1137/16m1079506

Cited by 28 publications

(34 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To enhance the quality of incomplete factorization preconditioners, it is possible to interleave the fixed point iterations approximating the values in the incomplete factors with a strategy that dynamically adapts the sparsity pattern to the problem characteristics [16] (figure 1). In an iterative process based on highly parallel building blocks, this strategy not only allows us, for the first time, to generate threshold-based ILU factorizations on parallel shared-memory architectures, but also enables us to efficiently leverage streaming-based architectures like GPUs [17].…”

Section: Ginkgomentioning

confidence: 99%

Preparing sparse solvers for exascale computing

Anzt

Boman

Falgout

et al. 2020

Phil. Trans. R. Soc. A.

Self Cite

View full text Add to dashboard Cite

Sparse solvers provide essential functionality for a wide variety of scientific applications. Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi-physics and multi-scale simulations, especially as we target exascale platforms. This paper describes the challenges, strategies and progress of the US Department of Energy Exascale Computing project towards providing sparse solvers for exascale computing platforms. We address the demands of systems with thousands of high-performance node devices where exposing concurrency, hiding latency and creating alternative algorithms become essential. The efforts described here are works in progress, highlighting current success and upcoming challenges. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.

show abstract

Section: Ginkgomentioning

confidence: 99%

Preparing sparse solvers for exascale computing

Anzt

Boman

Falgout

et al. 2020

Phil. Trans. R. Soc. A.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the program code for this example given in Listing 1, the system matrix A, the right-hand side b, and the initial solution guess x, are initially read from the standard input using G 's 'read' utility (lines [9][10][11]. Next, the program creates a factory for a CG Krylov solver preconditioned with a block-Jacobi scheme (lines [13][14][15]). e solver is con gured to stop either a er 20 iterations or having improved the original residual by 15 orders of magnitude (lines [16][17][18][19].…”

Section: An Overview Of Ginkgo's Designmentioning

confidence: 99%

Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing

Anzt¹,

Cojean²,

Flegar³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

In this paper, we present G , a modern C++ math library for scienti c high performance computing. While classical linear algebra libraries act on matrix and vector objects, G 's design principle abstracts all functionality as "linear operators, " motivating the notation of a "linear operator algebra library. " G 's current focus is oriented towards providing sparse linear algebra functionality for high performance GPU architectures, but given the library design, this focus can be easily extended to accommodate other algorithms and hardware architectures. We introduce this sophisticated so ware architecture that separates core algorithms from architecture-speci c backends and provide details on extensibility and sustainability measures. We also demonstrate G 's usability by providing examples on how to use its functionality inside the MFEM and deal.ii nite element ecosystems. Finally, we o er a practical demonstration of G 's high performance on state-of-the-art GPU architectures.CCS Concepts: •Mathematics of computing → Mathematical so ware; •Computing methodologies → Massively parallel algorithms; •So ware and its engineering → So ware creation and management;

show abstract

“…We are spearheading the manycore-parallel computation of threshold-based incomplete factorization preconditioners [170,171].…”

Section: Parilut -A Parallel Threshold Ilumentioning

confidence: 99%

ECP Software Technology Capability Assessment Report

Heroux

Computing²,

Carter

et al. 2018

View full text Add to dashboard Cite

The Exascale Computing Project (ECP) Software Technology (ST) Focus Area is responsible for developing critical software capabilities that will enable successful execution of ECP applications, and for providing key components of a productive and sustainable Exascale computing ecosystem that will position the US Department of Energy (DOE) and the broader high performance (HPC) community with a firm foundation for future extreme-scale computing capabilities. This ECP ST Capability Assessment Report (CAR) provides an overview and assessment of current ECP ST capabilities and activities, giving stakeholders and the broader HPC community information that can be used to assess ECP ST progress and plan their own efforts accordingly. ECP ST leaders commit to updating this document on regular basis (every six to 12 months). Highlights from this version of the report are presented here. What is new in CAR V2.0: CAR V2.0 contains the following updates relative to CAR V1.5. • We introduce the FY20-23 project structure. ECP ST now consists of 6 (up from 5) level-3 (L3) technical areas, introducing the NNSA ST L3 area, which brings into one L3 the ECP open source development efforts at NNSA labs that are of particular importance to the rest of ECP. The number of ECP ST level-4 (L4) subprojects has been reduced from 55 to 33. The strategic aggregation of projects into fewer and larger units enables us to better manage L4 subprojects consistently as a portfolio. See Section 1.2. • We describe new and enhanced project management processes and resources including our iterative planning process, new KPP-3 capability integration process, a product dictionary and dependency management database. These new project features and related dashboards enable more insight and better information to effectively manage efforts across ECP. See Section 2. • The two-page summaries of each ECP L4 projects have been updated to reflect recent progress and next steps. See Section 4. • The Extreme-scale Scientific Software Stack (E4S) is further described. The third release, which is also the first major public release Version 1.0, was November 18, 2019. E4S is the primary integration and delivery vehicle for ECP ST capabilities. See Section 2.1.1. • The ECP ST SDK effort has further refined its groupings. See Section 2.1.2. The Exascale Computing Project Software Technology (ECP ST) focus area represents the key bridge between Exascale systems and the scientists developing applications that will run on those platforms. ECP ST efforts contribute to 70 software products (Section 2.1.3) in six technical areas (Table 1). Since the publishing of CAR V1.5, we have introduced a product dictionary of official product names, which enables more rigorous mapping of ECP ST deliverables to stakeholders (Section 2.1.4). Programming Models & Runtimes: In addition to developing key enhancements to MPI and OpenMP for scalable systems with accelerated node architectures, we are working on performance portability layers (Kokkos and RAJA) and participating in OpenMP and O...

show abstract

ParILUT---A New Parallel Threshold ILU Factorization

Cited by 28 publications

References 21 publications

Preparing sparse solvers for exascale computing

Preparing sparse solvers for exascale computing

Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing

ECP Software Technology Capability Assessment Report

Contact Info

Product

Resources

About