55 research outputs found
Multiprocessor Out-of-Core FFTs with Distributed Memory and Parallel Disks
This paper extends an earlier out-of-core Fast Fourier Transform (FFT) method for a uniprocessor with the Parallel Disk Model (PDM) to use multiple processors. Four out-of-core multiprocessor methods are examined. Operationally, these methods differ in the size of mini-butterfly computed in memory and how the data are organized on the disks and in the distributed memory of the multiprocessor. The methods also perform differing amounts of I/O and communication. Two of them have the remarkable property that even though they are computing the FFT on a multiprocessor, all interprocessor communication occurs outside the mini-butterfly computations. Performance results on a small workstation cluster indicate that except for unusual combinations of problem size and memory size, the methods that do not perform interprocessor communication during the mini-butterfly computations require approximately 86% of the time of those that do. Moreover, the faster methods are much easier to implement
DALiuGE: A Graph Execution Framework for Harnessing the Astronomical Data Deluge
The Data Activated Liu Graph Engine - DALiuGE - is an execution framework for
processing large astronomical datasets at a scale required by the Square
Kilometre Array Phase 1 (SKA1). It includes an interface for expressing complex
data reduction pipelines consisting of both data sets and algorithmic
components and an implementation run-time to execute such pipelines on
distributed resources. By mapping the logical view of a pipeline to its
physical realisation, DALiuGE separates the concerns of multiple stakeholders,
allowing them to collectively optimise large-scale data processing solutions in
a coherent manner. The execution in DALiuGE is data-activated, where each
individual data item autonomously triggers the processing on itself. Such
decentralisation also makes the execution framework very scalable and flexible,
supporting pipeline sizes ranging from less than ten tasks running on a laptop
to tens of millions of concurrent tasks on the second fastest supercomputer in
the world. DALiuGE has been used in production for reducing interferometry data
sets from the Karl E. Jansky Very Large Array and the Mingantu Ultrawide
Spectral Radioheliograph; and is being developed as the execution framework
prototype for the Science Data Processor (SDP) consortium of the Square
Kilometre Array (SKA) telescope. This paper presents a technical overview of
DALiuGE and discusses case studies from the CHILES and MUSER projects that use
DALiuGE to execute production pipelines. In a companion paper, we provide
in-depth analysis of DALiuGE's scalability to very large numbers of tasks on
two supercomputing facilities.Comment: 31 pages, 12 figures, currently under review by Astronomy and
Computin
Department of Computer Science Activity 1998-2004
This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period
Design and implementation of wave scope storage manager and access scheduler
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 115-116).In this thesis, I designed, implemented, and analyzed the performance of an optimized storage manager for the Wavescope project. In doing this, I implemented an importation system that converts CENSAM data into a format specific to the processing system and cleans that data from measurement errors and irregularities; designed and implemented a highly efficient bulk-data processing system that is further optimized with a parallel-processor and disk access reorderer; carefully analyzed various methods for accessing the disk and our processing system, resulting in an accurate and predictive system model; and carefully ran a set of different applications to analyze the performance of our processing system. The project involves low-level optimization of Linux disk I/O and high-level optimizations such as parallel-processing. In the end, I created a system that is highly optimized and actually usable by CENSAM and other researchers.by Jeremy Elliot Smith.M.Eng
DSPSR: Digital Signal Processing Software for Pulsar Astronomy
DSPSR is a high-performance, open-source, object-oriented, digital signal
processing software library and application suite for use in radio pulsar
astronomy. Written primarily in C++, the library implements an extensive range
of modular algorithms that can optionally exploit both multiple-core processors
and general-purpose graphics processing units. After over a decade of research
and development, DSPSR is now stable and in widespread use in the community.
This paper presents a detailed description of its functionality, justification
of major design decisions, analysis of phase-coherent dispersion removal
algorithms, and demonstration of performance on some contemporary
microprocessor architectures.Comment: 15 pages, 10 figures, to be published in PAS
Efficient I/O for Computational Grid Applications
High-performance computing increasingly occurs on computational grids composed of heterogeneous and geographically distributed systems of computers, networks, and storage devices that collectively act as a single virtual computer. A key challenge in this environment is to provide efficient access to data distributed across remote data servers. This dissertation explores some of the issues associated with I/O for wide-area distributed computing and describes an I/O system, called Armada, with the following features: a framework to allow application and dataset providers to flexibly compose graphs of processing modules that describe the distribution, application interfaces, and processing required of the dataset before or after computation; an algorithm to restructure application graphs to increase parallelism and to improve network performance in a wide-area network; and a hierarchical graph-partitioning scheme that deploys components of the application graph in a way that is both beneficial to the application and sensitive to the administrative policies of the different administrative domains. Experiments show that applications using Armada perform well in both low- and high-bandwidth environments, and that our approach does an exceptional job of hiding the network latency inherent in grid computing
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