66,618 research outputs found
Challenging the Computational Metaphor: Implications for How We Think
This paper explores the role of the traditional computational metaphor in our thinking as computer scientists, its influence on epistemological styles, and its implications for our understanding of cognition. It proposes to replace the conventional metaphor--a sequence of steps--with the notion of a community of interacting entities, and examines the ramifications of such a shift on these various ways in which we think
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Speeding-up the execution of credit risk simulations using desktop grid computing: A case study
This paper describes a case study that was
undertaken at a leading European Investment
bank in which desktop grid computing was used
to speed-up the execution of Monte Carlo credit risk simulations. The credit risk simulations were modelled using commercial-off-the-shelf simulation packages (CSPs). The CSPs did not incorporate built-in support for desktop grids, and therefore the authors implemented a middleware for desktop grid computing, called WinGrid, and interfaced it with the CSP. The performance results show that WinGrid can speed-up the execution of CSP-based Monte Carlo simulations. However, since WinGrid was installed on non-dedicated PCs, the speed-up
achieved varied according to usersâ PC usage.
Finally, the paper presents some lessons learnt from this case study. It is expected that this paper will encourage simulation practitioners and CSP vendors to experiment with desktop grid computing technologies with the objective of speeding-up simulation experimentation
Lustre, Hadoop, Accumulo
Data processing systems impose multiple views on data as it is processed by
the system. These views include spreadsheets, databases, matrices, and graphs.
There are a wide variety of technologies that can be used to store and process
data through these different steps. The Lustre parallel file system, the Hadoop
distributed file system, and the Accumulo database are all designed to address
the largest and the most challenging data storage problems. There have been
many ad-hoc comparisons of these technologies. This paper describes the
foundational principles of each technology, provides simple models for
assessing their capabilities, and compares the various technologies on a
hypothetical common cluster. These comparisons indicate that Lustre provides 2x
more storage capacity, is less likely to loose data during 3 simultaneous drive
failures, and provides higher bandwidth on general purpose workloads. Hadoop
can provide 4x greater read bandwidth on special purpose workloads. Accumulo
provides 10,000x lower latency on random lookups than either Lustre or Hadoop
but Accumulo's bulk bandwidth is 10x less. Significant recent work has been
done to enable mix-and-match solutions that allow Lustre, Hadoop, and Accumulo
to be combined in different ways.Comment: 6 pages; accepted to IEEE High Performance Extreme Computing
conference, Waltham, MA, 201
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