2,097 research outputs found
HTC Scientific Computing in a Distributed Cloud Environment
This paper describes the use of a distributed cloud computing system for
high-throughput computing (HTC) scientific applications. The distributed cloud
computing system is composed of a number of separate
Infrastructure-as-a-Service (IaaS) clouds that are utilized in a unified
infrastructure. The distributed cloud has been in production-quality operation
for two years with approximately 500,000 completed jobs where a typical
workload has 500 simultaneous embarrassingly-parallel jobs that run for
approximately 12 hours. We review the design and implementation of the system
which is based on pre-existing components and a number of custom components. We
discuss the operation of the system, and describe our plans for the expansion
to more sites and increased computing capacity
Survey and Analysis of Production Distributed Computing Infrastructures
This report has two objectives. First, we describe a set of the production
distributed infrastructures currently available, so that the reader has a basic
understanding of them. This includes explaining why each infrastructure was
created and made available and how it has succeeded and failed. The set is not
complete, but we believe it is representative.
Second, we describe the infrastructures in terms of their use, which is a
combination of how they were designed to be used and how users have found ways
to use them. Applications are often designed and created with specific
infrastructures in mind, with both an appreciation of the existing capabilities
provided by those infrastructures and an anticipation of their future
capabilities. Here, the infrastructures we discuss were often designed and
created with specific applications in mind, or at least specific types of
applications. The reader should understand how the interplay between the
infrastructure providers and the users leads to such usages, which we call
usage modalities. These usage modalities are really abstractions that exist
between the infrastructures and the applications; they influence the
infrastructures by representing the applications, and they influence the ap-
plications by representing the infrastructures
The CHAIN-REDS Semantic Search Engine
e-Infrastructures, and in particular Data Repositories and Open Access Data Infrastructures, are essential platforms for e-Science and e-Research and are being built since several years both in Europe and the rest of the world to support diverse multi/inter-disciplinary Virtual Research Communities. So far, however, it is difficult for scientists to correlate papers to datasets used to produce them and to discover data and documents in an easy way. In this paper, the CHAINREDS project’s Knowledge Base and its Semantic Search Engine are presented, which attempt to address those drawbacks and contribute to the reproducibility of science
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
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