12,882 research outputs found
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
D4M 3.0: Extended Database and Language Capabilities
The D4M tool was developed to address many of today's data needs. This tool
is used by hundreds of researchers to perform complex analytics on unstructured
data. Over the past few years, the D4M toolbox has evolved to support
connectivity with a variety of new database engines, including SciDB.
D4M-Graphulo provides the ability to do graph analytics in the Apache Accumulo
database. Finally, an implementation using the Julia programming language is
also now available. In this article, we describe some of our latest additions
to the D4M toolbox and our upcoming D4M 3.0 release. We show through
benchmarking and scaling results that we can achieve fast SciDB ingest using
the D4M-SciDB connector, that using Graphulo can enable graph algorithms on
scales that can be memory limited, and that the Julia implementation of D4M
achieves comparable performance or exceeds that of the existing MATLAB(R)
implementation.Comment: IEEE HPEC 201
The financial clouds review
This paper demonstrates financial enterprise portability, which involves moving entire application services from desktops to clouds and between different clouds, and is transparent to users who can work as if on their familiar systems. To demonstrate portability, reviews for several financial models are studied, where Monte Carlo Methods (MCM) and Black Scholes Model (BSM) are chosen. A special technique in MCM, Least Square Methods, is used to reduce errors while performing accurate calculations. The coding algorithm for MCM written in MATLAB is explained. Simulations for MCM are performed on different types of Clouds. Benchmark and experimental results are presented for discussion. 3D Black Scholes are used to explain the impacts and added values for risk analysis, and three different scenarios with 3D risk analysis are explained. We also discuss implications for banking and ways to track risks in order to improve accuracy. We have used a conceptual Cloud platform to explain our contributions in Financial Software as a Service (FSaaS) and the IBM Fined Grained Security Framework. Our objective is to demonstrate portability, speed, accuracy and reliability of applications in the clouds, while demonstrating portability for FSaaS and the Cloud Computing Business Framework (CCBF), which is proposed to deal with cloud portability
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