1,858 research outputs found
Parallel Astronomical Data Processing with Python: Recipes for multicore machines
High performance computing has been used in various fields of astrophysical
research. But most of it is implemented on massively parallel systems
(supercomputers) or graphical processing unit clusters. With the advent of
multicore processors in the last decade, many serial software codes have been
re-implemented in parallel mode to utilize the full potential of these
processors. In this paper, we propose parallel processing recipes for multicore
machines for astronomical data processing. The target audience are astronomers
who are using Python as their preferred scripting language and who may be using
PyRAF/IRAF for data processing. Three problems of varied complexity were
benchmarked on three different types of multicore processors to demonstrate the
benefits, in terms of execution time, of parallelizing data processing tasks.
The native multiprocessing module available in Python makes it a relatively
trivial task to implement the parallel code. We have also compared the three
multiprocessing approaches - Pool/Map, Process/Queue, and Parallel Python. Our
test codes are freely available and can be downloaded from our website.Comment: 15 pages, 7 figures, 1 table, "for associated test code, see
http://astro.nuigalway.ie/staff/navtejs", Accepted for publication in
Astronomy and Computin
PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications
Energy efficiency is a major concern in modern high-performance computing system design. In the past few years, there has been mounting evidence that power usage limits system scale and computing density, and thus, ultimately system performance. However, despite the impact of power and energy on the computer systems community, few studies provide insight to where and how power is consumed on high-performance systems and applications. In previous work, we designed a framework called PowerPack that was the first tool to isolate the power consumption of devices including disks, memory, NICs, and processors in a high-performance cluster and correlate these measurements to application functions. In this work, we extend our framework to support systems with multicore, multiprocessor-based nodes, and then provide in-depth analyses of the energy consumption of parallel applications on clusters of these systems. These analyses include the impacts of chip multiprocessing on power and energy efficiency, and its interaction with application executions. In addition, we use PowerPack to study the power dynamics and energy efficiencies of dynamic voltage and frequency scaling (DVFS) techniques on clusters. Our experiments reveal conclusively how intelligent DVFS scheduling can enhance system energy efficiency while maintaining performance
Algorithms for Extracting Frequent Episodes in the Process of Temporal Data Mining
An important aspect in the data mining process is the discovery of patterns having a great influence on the studied problem. The purpose of this paper is to study the frequent episodes data mining through the use of parallel pattern discovery algorithms. Parallel pattern discovery algorithms offer better performance and scalability, so they are of a great interest for the data mining research community. In the following, there will be highlighted some parallel and distributed frequent pattern mining algorithms on various platforms and it will also be presented a comparative study of their main features. The study takes into account the new possibilities that arise along with the emerging novel Compute Unified Device Architecture from the latest generation of graphics processing units. Based on their high performance, low cost and the increasing number of features offered, GPU processors are viable solutions for an optimal implementation of frequent pattern mining algorithmsFrequent Pattern Mining, Parallel Computing, Dynamic Load Balancing, Temporal Data Mining, CUDA, GPU, Fermi, Thread
SKIRT: hybrid parallelization of radiative transfer simulations
We describe the design, implementation and performance of the new hybrid
parallelization scheme in our Monte Carlo radiative transfer code SKIRT, which
has been used extensively for modeling the continuum radiation of dusty
astrophysical systems including late-type galaxies and dusty tori. The hybrid
scheme combines distributed memory parallelization, using the standard Message
Passing Interface (MPI) to communicate between processes, and shared memory
parallelization, providing multiple execution threads within each process to
avoid duplication of data structures. The synchronization between multiple
threads is accomplished through atomic operations without high-level locking
(also called lock-free programming). This improves the scaling behavior of the
code and substantially simplifies the implementation of the hybrid scheme. The
result is an extremely flexible solution that adjusts to the number of
available nodes, processors and memory, and consequently performs well on a
wide variety of computing architectures.Comment: 21 pages, 20 figure
Functional requirements document for the Earth Observing System Data and Information System (EOSDIS) Scientific Computing Facilities (SCF) of the NASA/MSFC Earth Science and Applications Division, 1992
Five scientists at MSFC/ESAD have EOS SCF investigator status. Each SCF has unique tasks which require the establishment of a computing facility dedicated to accomplishing those tasks. A SCF Working Group was established at ESAD with the charter of defining the computing requirements of the individual SCFs and recommending options for meeting these requirements. The primary goal of the working group was to determine which computing needs can be satisfied using either shared resources or separate but compatible resources, and which needs require unique individual resources. The requirements investigated included CPU-intensive vector and scalar processing, visualization, data storage, connectivity, and I/O peripherals. A review of computer industry directions and a market survey of computing hardware provided information regarding important industry standards and candidate computing platforms. It was determined that the total SCF computing requirements might be most effectively met using a hierarchy consisting of shared and individual resources. This hierarchy is composed of five major system types: (1) a supercomputer class vector processor; (2) a high-end scalar multiprocessor workstation; (3) a file server; (4) a few medium- to high-end visualization workstations; and (5) several low- to medium-range personal graphics workstations. Specific recommendations for meeting the needs of each of these types are presented
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