9,625 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
A batch scheduler with high level components
In this article we present the design choices and the evaluation of a batch
scheduler for large clusters, named OAR. This batch scheduler is based upon an
original design that emphasizes on low software complexity by using high level
tools. The global architecture is built upon the scripting language Perl and
the relational database engine Mysql. The goal of the project OAR is to prove
that it is possible today to build a complex system for ressource management
using such tools without sacrificing efficiency and scalability. Currently, our
system offers most of the important features implemented by other batch
schedulers such as priority scheduling (by queues), reservations, backfilling
and some global computing support. Despite the use of high level tools, our
experiments show that our system has performances close to other systems.
Furthermore, OAR is currently exploited for the management of 700 nodes (a
metropolitan GRID) and has shown good efficiency and robustness
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