42 research outputs found
Heterogeneity, High Performance Computing, Self-Organization and the Cloud
application; blueprints; self-management; self-organisation; resource management; supply chain; big data; PaaS; Saas; HPCaa
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie
Heterogeneity, High Performance Computing, Self-Organization and the Cloud
application; blueprints; self-management; self-organisation; resource management; supply chain; big data; PaaS; Saas; HPCaa
Metrics, clustering and simulations to evaluate seismic signals
This thesis presents an overview on seismic signals analysis and its related activities
to clustering. The real applications require the use of metrics, algorithms and data to
test hypothesis or to infer them. Hypocenter and focal mechanism of an earthquake
can be determined by the analysis of signals, named waveforms, related to the wave
field produced by earthquakes and recorded by a seismic network. Assuming that
waveform similarity implies the similarity of focal parameters, the analysis of those
signals characterized by very similar shapes can be used to give important details
about the physical phenomena which have generated an earthquake. Recent works
have shown the effectiveness of cross-correlation and/or cross-spectral dissimilarities
to identify clusters of seismic events. In this thesis we propose a new dissimilarity
measure between seismic signals whose reliability has been tested on real seismic data
by computing external and internal validation indices on the obtained clustering.
Results show its superior quality in terms of cluster homogeneity and computational
time with respect to the largely adopted cross correlation dissimilarity
Heterogeneity, high performance computing, self-organization and the Cloud
This open access book addresses the most recent developments in cloud computing such as HPC in the Cloud, heterogeneous cloud, self-organising and self-management, and discusses the business implications of cloud computing adoption. Establishing the need for a new architecture for cloud computing, it discusses a novel cloud management and delivery architecture based on the principles of self-organisation and self-management. This focus shifts the deployment and optimisation effort from the consumer to the software stack running on the cloud infrastructure. It also outlines validation challenges and introduces a novel generalised extensible simulation framework to illustrate the effectiveness, performance and scalability of self-organising and self-managing delivery models on hyperscale cloud infrastructures. It concludes with a number of potential use cases for self-organising, self-managing clouds and the impact on those businesses