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Data-centric Parallel Programming

Total Cost €


EC-Contrib. €






 DAPP project word cloud

Explore the words cloud of the DAPP project. It provides you a very rough idea of what is the project "DAPP" about.

relies    fundamental    million    supercomputers    mapping    computationally    express    memlets    scheduling    abstractions    layout    optimizations    operation    substantially    society    memory    readily    combining    guide    machine    arithmetic    ranging    inherently    science    quad    parallel    demands    world    model    largely    wall    severely    parallelism    class    hard    believe    blocks    failing    create    computer    mapped    data    fetching    complexity    operands    magnitude    computing    notoriously    processors    programming    scheduled    architectures    prediction    runtime    expensive    architectural    graph    centric    scientific    first    weather    satisfy    heterogeneous    analytics    remote    big    technological    scaling    drug    inefficiency    formulation    core    computers    building    static    collections    computational    depart    limit    platforms    compiler    amount    compiled    programs    laptops    programmers    orders    demanding    ignore    threads    holistic    dynamic    objects    prevalent   

Project "DAPP" data sheet

The following table provides information about the project.


Organization address
address: Raemistrasse 101
postcode: 8092

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Switzerland [CH]
 Project website
 Total cost 1˙499˙672 €
 EC max contribution 1˙499˙672 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2015-STG
 Funding Scheme ERC-STG
 Starting year 2016
 Duration (year-month-day) from 2016-06-01   to  2021-05-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 Project objective

We address a fundamental and increasingly important challenge in computer science: how to program large-scale heterogeneous parallel computers. Society relies on these computers to satisfy the growing demands of important applications such as drug design, weather prediction, and big data analytics. Architectural trends make heterogeneous parallel processors the fundamental building blocks of computing platforms ranging from quad-core laptops to million-core supercomputers; failing to exploit these architectures efficiently will severely limit the technological advance of our society. Computationally demanding problems are often inherently parallel and can readily be compiled for various target architectures. Yet, efficiently mapping data to the target memory system is notoriously hard, and the cost of fetching two operands from remote memory is already orders of magnitude more expensive than any arithmetic operation. Data access cost is growing with the amount of parallelism which makes data layout optimizations crucial. Prevalent parallel programming abstractions largely ignore data access and guide programmers to design threads of execution that are scheduled to the machine. We depart from this control-centric model to a data-centric program formulation where we express programs as collections of values, called memlets, that are mapped as first-class objects by the compiler and runtime system. Our holistic compiler and runtime system aims to substantially advance the state of the art in parallel computing by combining static and dynamic scheduling of memlets to complex heterogeneous target architectures. We will demonstrate our methods on three challenging real-world applications in scientific computing, data analytics, and graph processing. We strongly believe that, without holistic data-centric programming, the growing complexity and inefficiency of parallel programming will create a scaling wall that will limit our future computational capabilities.


year authors and title journal last update
List of publications.
2019 T. De Matteis, J. de Fine Licht, J. Beránek, T. Hoefler
Streaming Message Interface: High-Performance DistributedMemory Programming on Reconfigurable Hardware
published pages: , ISSN: , DOI:
arXiv 2019-12-17
2019 P. Grönquist, T. Ben-Nun, N. Dryden, P. Dueben, L. Lavarini, S. Li, T. Hoefler
Predicting Weather Uncertainty with Deep Convnets
published pages: , ISSN: , DOI:
arXiv 2019-12-17
2019 Ben-Nun, Tal; Licht, Johannes de Fine; Ziogas, Alexandros Nikolaos; Schneider, Timo; Hoefler, Torsten
Stateful Dataflow Multigraphs: A Data-Centric Model for Performance Portability on Heterogeneous Architectures
published pages: , ISSN: , DOI:
arXiv 4 2019-12-16
2019 T. Ben-Nun, M. Besta, S. Huber, A. Nikolaos Ziogas, D. Peter, T. Hoefler
A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning
published pages: , ISSN: , DOI:
arXiv 2019-12-17
2019 De Matteis, Tiziano; Licht, Johannes de Fine; Hoefler, Torsten
FBLAS: Streaming Linear Algebra on FPGA
published pages: , ISSN: , DOI:
arXiv 5 2019-12-17
2017 Didem Unat, Anshu Dubey, Torsten Hoefler, John Shalf, Mark Abraham, Mauro Bianco, Bradford L. Chamberlain, Romain Cledat, H. Carter Edwards, Hal Finkel, Karl Fuerlinger, Frank Hannig, Emmanuel Jeannot, Amir Kamil, Jeff Keasler, Paul H J Kelly, Vitus Leung, Hatem Ltaief, Naoya Maruyama, Chris J. Newburn, and Miquel Pericas:
Trends in Data Locality Abstractions for HPC Systems
published pages: , ISSN: 1045-9219, DOI:
IEEE Transactions on Parallel and Distributed Systems (TPDS) 2019-04-19
2018 J. de Fine Licht, M. Blott, T. Hoefler
Designing scalable FPGA architectures using high-level synthesis
published pages: , ISSN: , DOI:
2018 Tal Ben-Nun, Alice Shoshana Jakobovits, Torsten Hoefler
Neural Code Comprehension: A Learnable Representation of Code Semantics
published pages: , ISSN: , DOI:
Advances in Neural Information Processing Systems 31 2019-04-19
2017 T. Hoefler, S. Di Girolamo, K. Taranov, R. E. Grant, R. Brightwell
sPIN: High-performance streaming Processing in the Network
published pages: , ISSN: , DOI:

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