461 research outputs found
ASCR/HEP Exascale Requirements Review Report
This draft report summarizes and details the findings, results, and
recommendations derived from the ASCR/HEP Exascale Requirements Review meeting
held in June, 2015. The main conclusions are as follows. 1) Larger, more
capable computing and data facilities are needed to support HEP science goals
in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of
the demand at the 2025 timescale is at least two orders of magnitude -- and in
some cases greater -- than that available currently. 2) The growth rate of data
produced by simulations is overwhelming the current ability, of both facilities
and researchers, to store and analyze it. Additional resources and new
techniques for data analysis are urgently needed. 3) Data rates and volumes
from HEP experimental facilities are also straining the ability to store and
analyze large and complex data volumes. Appropriately configured
leadership-class facilities can play a transformational role in enabling
scientific discovery from these datasets. 4) A close integration of HPC
simulation and data analysis will aid greatly in interpreting results from HEP
experiments. Such an integration will minimize data movement and facilitate
interdependent workflows. 5) Long-range planning between HEP and ASCR will be
required to meet HEP's research needs. To best use ASCR HPC resources the
experimental HEP program needs a) an established long-term plan for access to
ASCR computational and data resources, b) an ability to map workflows onto HPC
resources, c) the ability for ASCR facilities to accommodate workflows run by
collaborations that can have thousands of individual members, d) to transition
codes to the next-generation HPC platforms that will be available at ASCR
facilities, e) to build up and train a workforce capable of developing and
using simulations and analysis to support HEP scientific research on
next-generation systems.Comment: 77 pages, 13 Figures; draft report, subject to further revisio
CERN openlab Whitepaper on Future IT Challenges in Scientific Research
This whitepaper describes the major IT challenges in scientific research at CERN and several other European and international research laboratories and projects. Each challenge is exemplified through a set of concrete use cases drawn from the requirements of large-scale scientific programs. The paper is based on contributions from many researchers and IT experts of the participating laboratories and also input from the existing CERN openlab industrial sponsors. The views expressed in this document are those of the individual contributors and do not necessarily reflect the view of their organisations and/or affiliates
The Future of High Energy Physics Software and Computing
Software and Computing (S&C) are essential to all High Energy Physics (HEP)
experiments and many theoretical studies. The size and complexity of S&C are
now commensurate with that of experimental instruments, playing a critical role
in experimental design, data acquisition/instrumental control, reconstruction,
and analysis. Furthermore, S&C often plays a leading role in driving the
precision of theoretical calculations and simulations. Within this central role
in HEP, S&C has been immensely successful over the last decade. This report
looks forward to the next decade and beyond, in the context of the 2021
Particle Physics Community Planning Exercise ("Snowmass") organized by the
Division of Particles and Fields (DPF) of the American Physical Society.Comment: Computational Frontier Report Contribution to Snowmass 2021; 41
pages, 1 figure. v2: missing ref and added missing topical group conveners.
v3: fixed typo
Design considerations for workflow management systems use in production genomics research and the clinic
Abstract The changing landscape of genomics research and clinical practice has created a need for computational pipelines capable of efficiently orchestrating complex analysis stages while handling large volumes of data across heterogeneous computational environments. Workflow Management Systems (WfMSs) are the software components employed to fill this gap. This work provides an approach and systematic evaluation of key features of popular bioinformatics WfMSs in use today: Nextflow, CWL, and WDL and some of their executors, along with Swift/T, a workflow manager commonly used in high-scale physics applications. We employed two use cases: a variant-calling genomic pipeline and a scalability-testing framework, where both were run locally, on an HPC cluster, and in the cloud. This allowed for evaluation of those four WfMSs in terms of language expressiveness, modularity, scalability, robustness, reproducibility, interoperability, ease of development, along with adoption and usage in research labs and healthcare settings. This article is trying to answer, which WfMS should be chosen for a given bioinformatics application regardless of analysis type?. The choice of a given WfMS is a function of both its intrinsic language and engine features. Within bioinformatics, where analysts are a mix of dry and wet lab scientists, the choice is also governed by collaborations and adoption within large consortia and technical support provided by the WfMS team/community. As the community and its needs continue to evolve along with computational infrastructure, WfMSs will also evolve, especially those with permissive licenses that allow commercial use. In much the same way as the dataflow paradigm and containerization are now well understood to be very useful in bioinformatics applications, we will continue to see innovations of tools and utilities for other purposes, like big data technologies, interoperability, and provenance
Distributed Computing in a Pandemic
The current COVID-19 global pandemic caused by the SARS-CoV-2 betacoronavirus has resulted in over a million deaths and is having a grave socio-economic impact, hence there is an urgency to find solutions to key research challenges. Much of this COVID-19 research depends on distributed computing. In this article, I review distributed architectures -- various types of clusters, grids and clouds -- that can be leveraged to perform these tasks at scale, at high-throughput, with a high degree of parallelism, and which can also be used to work collaboratively. High-performance computing (HPC) clusters will be used to carry out much of this work. Several bigdata processing tasks used in reducing the spread of SARS-CoV-2 require high-throughput approaches, and a variety of tools, which Hadoop and Spark offer, even using commodity hardware. Extremely large-scale COVID-19 research has also utilised some of the world's fastest supercomputers, such as IBM's SUMMIT -- for ensemble docking high-throughput screening against SARS-CoV-2 targets for drug-repurposing, and high-throughput gene analysis -- and Sentinel, an XPE-Cray based system used to explore natural products. Grid computing has facilitated the formation of the world's first Exascale grid computer. This has accelerated COVID-19 research in molecular dynamics simulations of SARS-CoV-2 spike protein interactions through massively-parallel computation and was performed with over 1 million volunteer computing devices using the Folding@home platform. Grids and clouds both can also be used for international collaboration by enabling access to important datasets and providing services that allow researchers to focus on research rather than on time-consuming data-management tasks
Parallel computing 2011, ParCo 2011: book of abstracts
This book contains the abstracts of the presentations at the conference Parallel Computing 2011, 30 August - 2 September 2011, Ghent, Belgiu
Diva: A Declarative and Reactive Language for In-Situ Visualization
The use of adaptive workflow management for in situ visualization and
analysis has been a growing trend in large-scale scientific simulations.
However, coordinating adaptive workflows with traditional procedural
programming languages can be difficult because system flow is determined by
unpredictable scientific phenomena, which often appear in an unknown order and
can evade event handling. This makes the implementation of adaptive workflows
tedious and error-prone. Recently, reactive and declarative programming
paradigms have been recognized as well-suited solutions to similar problems in
other domains. However, there is a dearth of research on adapting these
approaches to in situ visualization and analysis. With this paper, we present a
language design and runtime system for developing adaptive systems through a
declarative and reactive programming paradigm. We illustrate how an adaptive
workflow programming system is implemented using our approach and demonstrate
it with a use case from a combustion simulation.Comment: 11 pages, 5 figures, 6 listings, 1 table, to be published in LDAV
2020. The article has gone through 2 major revisions: Emphasized
contributions, features and examples. Addressed connections between DIVA and
FRP. In sec. 3, we fixed a design flaw and addressed it in sec. 3.3-3.4.
Re-designed sec. 5 with a more concrete example and benchmark results.
Simplified the syntax of DIV
VisIVO - Integrated Tools and Services for Large-Scale Astrophysical Visualization
VisIVO is an integrated suite of tools and services specifically designed for
the Virtual Observatory. This suite constitutes a software framework for
effective visual discovery in currently available (and next-generation) very
large-scale astrophysical datasets. VisIVO consists of VisiVO Desktop - a stand
alone application for interactive visualization on standard PCs, VisIVO Server
- a grid-enabled platform for high performance visualization and VisIVO Web - a
custom designed web portal supporting services based on the VisIVO Server
functionality. The main characteristic of VisIVO is support for
high-performance, multidimensional visualization of very large-scale
astrophysical datasets. Users can obtain meaningful visualizations rapidly
while preserving full and intuitive control of the relevant visualization
parameters. This paper focuses on newly developed integrated tools in VisIVO
Server allowing intuitive visual discovery with 3D views being created from
data tables. VisIVO Server can be installed easily on any web server with a
database repository. We discuss briefly aspects of our implementation of VisiVO
Server on a computational grid and also outline the functionality of the
services offered by VisIVO Web. Finally we conclude with a summary of our work
and pointers to future developments
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