Journal•ISSN: 1094-3420
International Journal of High Performance Computing Applications
SAGE Publishing
About: International Journal of High Performance Computing Applications is an academic journal published by SAGE Publishing. The journal publishes majorly in the area(s): Computer science & Solver. It has an ISSN identifier of 1094-3420. Over the lifetime, 332 publications have been published receiving 3792 citations. The journal is also known as: High performance computing applications.
Papers published on a yearly basis
Papers
More filters
University of Southern California1, Argonne National Laboratory2, University of California, San Diego3, Rensselaer Polytechnic Institute4, Brookhaven National Laboratory5, Sandia National Laboratories6, Rutgers University7, Lawrence Berkeley National Laboratory8, University of Delaware9, Oak Ridge National Laboratory10
TL;DR: This work highlights use cases, computing systems, workflow needs, and concludes by summarizing the remaining challenges this community sees that inhibit large-scale scientific workflows from becoming a mainstream tool for extreme-scale science.
Abstract: Today’s computational, experimental, and observational sciences rely on computations that involve many related tasks. The success of a scientific mission often hinges on the computer automation of these workflows. In April 2015, the US Department of Energy (DOE) invited a diverse group of domain and computer scientists from national laboratories supported by the Office of Science, the National Nuclear Security Administration, from industry, and from academia to review the workflow requirements of DOE’s science and national security missions, to assess the current state of the art in science workflows, to understand the impact of emerging extreme-scale computing systems on those workflows, and to develop requirements for automated workflow management in future and existing environments. This article is a summary of the opinions of over 50 leading researchers attending this workshop. We highlight use cases, computing systems, workflow needs and conclude by summarizing the remaining challenges this community...
144 citations
TL;DR: This paper presents PyCOMPSs, a framework that facilitates the development of parallel computational workflows in Python and shows how this programming model can be built on top of a Big Data storage architecture, where the data stored in the backend is abstracted and accessed from the application in the form of persistent objects.
Abstract: The use of the Python programming language for scientific computing has been gaining momentum in the last years. The fact that it is compact and readable and its complete set of scientific libraries are two important characteristics that favour its adoption. Nevertheless, Python still lacks a solution for easily parallelizing generic scripts on distributed infrastructures, since the current alternatives mostly require the use of APIs for message passing or are restricted to embarrassingly parallel computations. In that sense, this paper presents PyCOMPSs, a framework that facilitates the development of parallel computational workflows in Python. In this approach, the user programs her script in a sequential fashion and decorates the functions to be run as asynchronous parallel tasks. A runtime system is in charge of exploiting the inherent concurrency of the script, detecting the data dependencies between tasks and spawning them to the available resources. Furthermore, we show how this programming model c...
109 citations
TL;DR: It is argued that the rapid proliferation of digital data generators, the unprecedented growth in the volume and diversity of the data they generate, and the intense evolution of the methods for analyzing and using that data are radically reshaping the landscape of scientific computing.
Abstract: Over the past four years, the Big Data and Exascale Computing (BDEC) project organized a series of five international workshops that aimed to explore the ways in which the new forms of data-centric...
98 citations
TL;DR: In this article, a generalizable AI-driven workflow that leverages heterogeneous HPC resources was developed to explore the time-dependent dynamics of molecular systems. But their work focused on the SARS-CoV-2 spike protein, the main viral infection machinery.
Abstract: We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike’s full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.
71 citations
TL;DR: The architectural and technological trends of systems used for scientific computing call for a significant reduction of scientific data sets that are composed mainly of floating-point data as mentioned in this paper, and this articl...
Abstract: Architectural and technological trends of systems used for scientific computing call for a significant reduction of scientific data sets that are composed mainly of floating-point data. This articl...
69 citations