The EPiGRAM Project: Preparing Parallel Programming Models for Exascale

Träff, Jesper Larsson; Rougier, Antoine; Bartsch, V.; Machado, Rui; Rahn, Mirko; Hart, Alistair; Holmes, Daniel J.; Bull, Mark; Laure, Erwin

doi:10.1007/978-3-319-46079-6_5

Cited by 10 publications

(8 citation statements)

References 31 publications

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“…Chapel [89] is a PGAS-based programming language by Cray Inc. UPC++ [16] is a library based on asynchronous PGAS, adding the ability to spawn and coordinate tasks. The EPiGRAM project [121] aims to improve the interoperability of MPI and PGAS.…”

Section: Mpi and Openmp According To Gropp And Snirmentioning

confidence: 99%

The Landscape of Exascale Research

et al. 2020

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The next generation of supercomputers will break the exascale barrier. Soon we will have systems capable of at least one quintillion (billion billion) floating-point operations per second (10 18 FLOPS). Tremendous amounts of work have been invested into identifying and overcoming the challenges of the exascale era. In this work, we present an overview of these efforts and provide insight into the important trends, developments, and exciting research opportunities in exascale computing. We use a three-stage approach in which we (1) discuss various exascale landmark studies, (2) use data-driven techniques to analyze the large collection of related literature, and (3) discuss eight research areas in depth based on influential articles. Overall, we observe that great advancements have been made in tackling the two primary exascale challenges: energy efficiency and fault tolerance. However, as we look forward, we still foresee two major concerns: the lack of suitable programming tools and the growing gap between processor performance and data bandwidth (i.e., memory, storage, networks). Although we will certainly reach exascale soon, without additional research, these issues could potentially limit the applicability of exascale computing.

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Section: Mpi and Openmp According To Gropp And Snirmentioning

confidence: 99%

The Landscape of Exascale Research

et al. 2020

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“…Exascale systems force new requirements on programming systems to target platforms with hundreds of homogeneous and heterogeneous cores. Evolutionary models have been recently proposed for Exascale programming that extend or adapt traditional parallel programming models like MPI (e.g., EPiGRAM [15] that uses a library-based approach, Open MPI for Exascale in the ECP initiative), OpenMP (e.g., OmpSs [8] that exploits an annotation-based approach, the SOLLVE project), and MapReduce (e.g., Pig Latin [22] that implements a domain-specific complete language). These new frameworks limit the communication overhead in message passing paradigms or limit the synchronization control if a shared-memory model is used [11].…”

Section: Exascale Programming Systemsmentioning

confidence: 99%

A view of programming scalable data analysis: from clouds to exascale

Talia

2019

J Cloud Comp

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Scalability is a key feature for big data analysis and machine learning frameworks and for applications that need to analyze very large and real-time data available from data repositories, social media, sensor networks, smartphones, and the Web. Scalable big data analysis today can be achieved by parallel implementations that are able to exploit the computing and storage facilities of high performance computing (HPC) systems and clouds, whereas in the near future Exascale systems will be used to implement extreme-scale data analysis. Here is discussed how clouds currently support the development of scalable data mining solutions and are outlined and examined the main challenges to be addressed and solved for implementing innovative data analysis applications on Exascale systems.

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“…Porting an MPI-based application to GPI-2 can be done incrementally, by identifying independent MPI communication sections and replacing them gradually with GPI-2 communication sections, taking advantage of the ability of GPI-2 to work in mixed-mode model with MPI (Markidis et al, 2016). An important rule to follow here is to preserve the application’s logic.…”

Section: State-of-the-art Interoperability Of Gaspi and Mpimentioning

confidence: 99%

Interoperability strategies for GASPI and MPI in large-scale scientific applications

Simmendinger¹,

Iakymchuk

Cebamanos

et al. 2018

The International Journal of High Performance Computing Applica

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One of the main hurdles of PGAS approaches is the dominance of MPI, which as a de-facto standard appears in the code basis of many applications. To take advantage of the PGAS APIs like GASPI without a major change in the code basis, interoperability between MPI and PGAS approaches needs to be ensured. In this article we consider an interoperable GASPI/MPI implementation for the communication/performance crucial parts of the Ludwig and iPIC3D applications. To address the discovered performance limitations, we develop a novel strategy for significantly improved performance and interoperability between both APIs by leveraging GASPI shared windows and shared notifications. First results with a corresponding implementation in the MiniGhost proxy application demonstrate the viability of this approach.

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The EPiGRAM Project: Preparing Parallel Programming Models for Exascale

Cited by 10 publications

References 31 publications

The Landscape of Exascale Research

The Landscape of Exascale Research

A view of programming scalable data analysis: from clouds to exascale

Interoperability strategies for GASPI and MPI in large-scale scientific applications

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