222,084 research outputs found
A survey of general-purpose experiment management tools for distributed systems
International audienceIn the field of large-scale distributed systems, experimentation is particularly difficult. The studied systems are complex, often nondeterministic and unreliable, software is plagued with bugs, whereas the experiment workflows are unclear and hard to reproduce. These obstacles led many independent researchers to design tools to control their experiments, boost productivity and improve quality of scientific results. Despite much research in the domain of distributed systems experiment management, the current fragmentation of efforts asks for a general analysis. We therefore propose to build a framework to uncover missing functionality of these tools, enable meaningful comparisons be-tween them and find recommendations for future improvements and research. The contribution in this paper is twofold. First, we provide an extensive list of features offered by general-purpose experiment management tools dedicated to distributed systems research on real platforms. We then use it to assess existing solutions and compare them, outlining possible future paths for improvements
High Energy Physics Forum for Computational Excellence: Working Group Reports (I. Applications Software II. Software Libraries and Tools III. Systems)
Computing plays an essential role in all aspects of high energy physics. As
computational technology evolves rapidly in new directions, and data throughput
and volume continue to follow a steep trend-line, it is important for the HEP
community to develop an effective response to a series of expected challenges.
In order to help shape the desired response, the HEP Forum for Computational
Excellence (HEP-FCE) initiated a roadmap planning activity with two key
overlapping drivers -- 1) software effectiveness, and 2) infrastructure and
expertise advancement. The HEP-FCE formed three working groups, 1) Applications
Software, 2) Software Libraries and Tools, and 3) Systems (including systems
software), to provide an overview of the current status of HEP computing and to
present findings and opportunities for the desired HEP computational roadmap.
The final versions of the reports are combined in this document, and are
presented along with introductory material.Comment: 72 page
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
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
Scientific Computing Meets Big Data Technology: An Astronomy Use Case
Scientific analyses commonly compose multiple single-process programs into a
dataflow. An end-to-end dataflow of single-process programs is known as a
many-task application. Typically, tools from the HPC software stack are used to
parallelize these analyses. In this work, we investigate an alternate approach
that uses Apache Spark -- a modern big data platform -- to parallelize
many-task applications. We present Kira, a flexible and distributed astronomy
image processing toolkit using Apache Spark. We then use the Kira toolkit to
implement a Source Extractor application for astronomy images, called Kira SE.
With Kira SE as the use case, we study the programming flexibility, dataflow
richness, scheduling capacity and performance of Apache Spark running on the
EC2 cloud. By exploiting data locality, Kira SE achieves a 2.5x speedup over an
equivalent C program when analyzing a 1TB dataset using 512 cores on the Amazon
EC2 cloud. Furthermore, we show that by leveraging software originally designed
for big data infrastructure, Kira SE achieves competitive performance to the C
implementation running on the NERSC Edison supercomputer. Our experience with
Kira indicates that emerging Big Data platforms such as Apache Spark are a
performant alternative for many-task scientific applications
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