3,644 research outputs found
Dynamic cluster resizing
Processor resources required for an effective execution of an application vary across different sections. We propose to take advantage of clustering to turn-off resources that do not contribute to improve performance. First, we present a simple hardware scheme to dynamically compute the energy consumed by each processor block and the energy-delay2 product for a given interval of time. This scheme is used to compute the effectiveness of the current configuration in terms of energy-delay2 and evaluate the benefits of increasing/decreasing the number of active issue queues. Performance evaluation shows an average energy-delay2 product improvement of 18%, and up to 50% for some applications, in a quad-cluster architecture.Peer ReviewedPostprint (published version
Priority-enabled Scheduling for Resizable Parallel Applications
In this paper, we illustrate the impact of dynamic resizability on parallel scheduling.
Our ReSHAPE framework includes an application scheduler that supports dynamic resizing of parallel applications. We propose and evaluate new scheduling policies made possible by our ReSHAPE framework. The framework also provides a platform to experiment with more interesting and sophisticated scheduling policies and scenarios for resizable parallel applications. The proposed policies support scheduling of parallel applications with and without user assigned priorities. Experimental results show that these scheduling policies significantly improve individual application turn around time as well as overall cluster utilization
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
An Algorithm for Dynamic Load Balancing of Synchronous Monte Carlo Simulations on Multiprocessor Systems
We describe an algorithm for dynamic load balancing of geometrically
parallelized synchronous Monte Carlo simulations of physical models. This
algorithm is designed for a (heterogeneous) multiprocessor system of the MIMD
type with distributed memory. The algorithm is based on a dynamic partitioning
of the domain of the algorithm, taking into account the actual processor
resources of the various processors of the multiprocessor system.Comment: 12 pages, uuencoded figures included, 75.93.0
Providing Transaction Class-Based QoS in In-Memory Data Grids via Machine Learning
Elastic architectures and the ”pay-as-you-go” resource pricing model offered by many cloud infrastructure providers may seem the right choice for companies dealing with data centric applications characterized by high variable workload. In such a context, in-memory transactional data grids have demonstrated to be particularly suited for exploiting advantages provided by elastic computing platforms, mainly thanks to their ability to be dynamically (re-)sized and tuned. Anyway, when specific QoS requirements have to be met, this kind of architectures have revealed to be complex to be managed by humans. Particularly, their management is a very complex task without the stand of mechanisms supporting run-time automatic sizing/tuning of the data platform and the underlying (virtual) hardware resources provided by the cloud. In this paper, we present a neural network-based architecture where the system is constantly and automatically re-configured, particularly in terms of computing resources
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