1,500 research outputs found
ReSHAPE: A Framework for Dynamic Resizing and Scheduling of Homogeneous Applications in a Parallel Environment
Applications in science and engineering often require huge computational
resources for solving problems within a reasonable time frame. Parallel
supercomputers provide the computational infrastructure for solving such
problems. A traditional application scheduler running on a parallel cluster
only supports static scheduling where the number of processors allocated to an
application remains fixed throughout the lifetime of execution of the job. Due
to the unpredictability in job arrival times and varying resource requirements,
static scheduling can result in idle system resources thereby decreasing the
overall system throughput. In this paper we present a prototype framework
called ReSHAPE, which supports dynamic resizing of parallel MPI applications
executed on distributed memory platforms. The framework includes a scheduler
that supports resizing of applications, an API to enable applications to
interact with the scheduler, and a library that makes resizing viable.
Applications executed using the ReSHAPE scheduler framework can expand to take
advantage of additional free processors or can shrink to accommodate a high
priority application, without getting suspended. In our research, we have
mainly focused on structured applications that have two-dimensional data arrays
distributed across a two-dimensional processor grid. The resize library
includes algorithms for processor selection and processor mapping. Experimental
results show that the ReSHAPE framework can improve individual job turn-around
time and overall system throughput.Comment: 15 pages, 10 figures, 5 tables Submitted to International Conference
on Parallel Processing (ICPP'07
Many-Task Computing and Blue Waters
This report discusses many-task computing (MTC) generically and in the
context of the proposed Blue Waters systems, which is planned to be the largest
NSF-funded supercomputer when it begins production use in 2012. The aim of this
report is to inform the BW project about MTC, including understanding aspects
of MTC applications that can be used to characterize the domain and
understanding the implications of these aspects to middleware and policies.
Many MTC applications do not neatly fit the stereotypes of high-performance
computing (HPC) or high-throughput computing (HTC) applications. Like HTC
applications, by definition MTC applications are structured as graphs of
discrete tasks, with explicit input and output dependencies forming the graph
edges. However, MTC applications have significant features that distinguish
them from typical HTC applications. In particular, different engineering
constraints for hardware and software must be met in order to support these
applications. HTC applications have traditionally run on platforms such as
grids and clusters, through either workflow systems or parallel programming
systems. MTC applications, in contrast, will often demand a short time to
solution, may be communication intensive or data intensive, and may comprise
very short tasks. Therefore, hardware and software for MTC must be engineered
to support the additional communication and I/O and must minimize task dispatch
overheads. The hardware of large-scale HPC systems, with its high degree of
parallelism and support for intensive communication, is well suited for MTC
applications. However, HPC systems often lack a dynamic resource-provisioning
feature, are not ideal for task communication via the file system, and have an
I/O system that is not optimized for MTC-style applications. Hence, additional
software support is likely to be required to gain full benefit from the HPC
hardware
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
Maiter: An Asynchronous Graph Processing Framework for Delta-based Accumulative Iterative Computation
Myriad of graph-based algorithms in machine learning and data mining require
parsing relational data iteratively. These algorithms are implemented in a
large-scale distributed environment in order to scale to massive data sets. To
accelerate these large-scale graph-based iterative computations, we propose
delta-based accumulative iterative computation (DAIC). Different from
traditional iterative computations, which iteratively update the result based
on the result from the previous iteration, DAIC updates the result by
accumulating the "changes" between iterations. By DAIC, we can process only the
"changes" to avoid the negligible updates. Furthermore, we can perform DAIC
asynchronously to bypass the high-cost synchronous barriers in heterogeneous
distributed environments. Based on the DAIC model, we design and implement an
asynchronous graph processing framework, Maiter. We evaluate Maiter on local
cluster as well as on Amazon EC2 Cloud. The results show that Maiter achieves
as much as 60x speedup over Hadoop and outperforms other state-of-the-art
frameworks.Comment: ScienceCloud 2012, TKDE 201
- …