18,774 research outputs found
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FABRIC: A National-Scale Programmable Experimental Network Infrastructure
FABRIC is a unique national research infrastructure to enable cutting-edge and exploratory research at-scale in networking, cybersecurity, distributed computing and storage systems, machine learning, and science applications. It is an everywhere-programmable nationwide instrument comprised of novel extensible network elements equipped with large amounts of compute and storage, interconnected by high speed, dedicated optical links. It will connect a number of specialized testbeds for cloud research (NSF Cloud testbeds CloudLab and Chameleon), for research beyond 5G technologies (Platforms for Advanced Wireless Research or PAWR), as well as production high-performance computing facilities and science instruments to create a rich fabric for a wide variety of experimental activities
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Exploring adaptation & self-adaptation in autonomic computing systems
This panel paper sets out to discuss what self-adaptation
means, and to explore the extent to which current
autonomic systems exhibit truly self-adaptive behaviour.
Many of the currently cited examples are clearly
adaptive, but debate remains as to what extent they are
simply following prescribed adaptation rules within preset
bounds, and to what extent they have the ability to
truly learn new behaviour. Is there a standard test that
can be applied to differentiate? Is adaptive behaviour
sufficient anyway? Other autonomic computing issues are
also discussed
Lessons Learned from a Decade of Providing Interactive, On-Demand High Performance Computing to Scientists and Engineers
For decades, the use of HPC systems was limited to those in the physical
sciences who had mastered their domain in conjunction with a deep understanding
of HPC architectures and algorithms. During these same decades, consumer
computing device advances produced tablets and smartphones that allow millions
of children to interactively develop and share code projects across the globe.
As the HPC community faces the challenges associated with guiding researchers
from disciplines using high productivity interactive tools to effective use of
HPC systems, it seems appropriate to revisit the assumptions surrounding the
necessary skills required for access to large computational systems. For over a
decade, MIT Lincoln Laboratory has been supporting interactive, on-demand high
performance computing by seamlessly integrating familiar high productivity
tools to provide users with an increased number of design turns, rapid
prototyping capability, and faster time to insight. In this paper, we discuss
the lessons learned while supporting interactive, on-demand high performance
computing from the perspectives of the users and the team supporting the users
and the system. Building on these lessons, we present an overview of current
needs and the technical solutions we are building to lower the barrier to entry
for new users from the humanities, social, and biological sciences.Comment: 15 pages, 3 figures, First Workshop on Interactive High Performance
Computing (WIHPC) 2018 held in conjunction with ISC High Performance 2018 in
Frankfurt, German
Exploring Application Performance on Emerging Hybrid-Memory Supercomputers
Next-generation supercomputers will feature more hierarchical and
heterogeneous memory systems with different memory technologies working
side-by-side. A critical question is whether at large scale existing HPC
applications and emerging data-analytics workloads will have performance
improvement or degradation on these systems. We propose a systematic and fair
methodology to identify the trend of application performance on emerging
hybrid-memory systems. We model the memory system of next-generation
supercomputers as a combination of "fast" and "slow" memories. We then analyze
performance and dynamic execution characteristics of a variety of workloads,
from traditional scientific applications to emerging data analytics to compare
traditional and hybrid-memory systems. Our results show that data analytics
applications can clearly benefit from the new system design, especially at
large scale. Moreover, hybrid-memory systems do not penalize traditional
scientific applications, which may also show performance improvement.Comment: 18th International Conference on High Performance Computing and
Communications, IEEE, 201
Social Intelligence Design in Ambient Intelligence
This Special Issue of AI and Society contains a selection of papers presented at the 6th Social Intelligence Design Workshop held at ITC-irst, Povo (Trento, Italy) in July 2007. Being the 6th in a series means that there now is a well-established and also a growing research area. The interest in this research area is growing because, among other things, current computing technology allows other than the traditional efficiency-oriented applications associated with computer science and interface technology. For example, in Ambient Intelligence (AmI) applications we look at sensor-equipped environments and devices (robots, smart furniture, virtual humans and pets) that support their human inhabitants during their everyday activities. These everyday activities also include computer-mediated communication, collaboration and community activities
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