November 21, 2005
Ohio Savings Bank's mortgage system -- which
processes hundreds of millions of dollars of transactions daily --
relies on an integrated Oracle Database 10g and Microsoft .NET
Founded in 1889, Ohio Savings Bank has grown from a savings and loan
with a single branch office in Cleveland and $20,000 in assets to one
of the nation's largest mortgage lenders. In 2004, the bank adopted an
Oracle Database 10g Grid computing infrastructure to improve its
information technology (IT) utilization and deliver a higher quality of
service. Ohio Savings Bank's Grid supports a number of mission-critical
systems including the heart and soul of its business -- a mortgage
wholesale and retail application transaction processing system.
Comprised of an Oracle Database and Microsoft middle-tier applications,
the bank's transaction heavy mortgage system manages the lifecycle of
loans from origination to funding. It is used by internal branches,
loan processing offices, and brokers that originate loans across
country and sell the loans to Ohio Savings Bank.
A three-node cluster using Oracle Real Application Clusters on
two-way HP ProLiant DL 380 servers with Intel Xeon processors running
Red Hat Enterprise Linux v. 3 powers Ohio Savings Bank's mortgage
system. This clustered Oracle Database is fully integrated with
Microsoft Visual Studio .NET 2003 applications running on Windows.
A key component in the bank's integration of Oracle and Microsoft
environments is their use of Oracle Data Provider for .NET (ODP.NET), a
driver that delivers optimized data access to Oracle Database
functionality from a .NET environment. ODP.NET enables developers to
take advantage of advanced Oracle Database features, including Oracle
Real Application Clusters, Oracle XML DB, and advanced security
functionality, among many others. It enables the bank to easily and
flexibly use XML throughout its applications and interface and with
Oracle Database. ODP.NET can be used from any .NET language, including
C# and Visual Basic .NET.
The ever-growing complexity of scientific and engineering problems continues to pose new computational challenges. Thus, we present a novel federation model that enables end-users with the ability to aggregate heterogeneous resource scale problems. The feasibility of this federation model has been proven, in the context of the UberCloud HPC Experiment, by gathering the most comprehensive information to date on the effects of pillars on microfluid channel flow.
Large-scale, worldwide scientific initiatives rely on some cloud-based system to both coordinate efforts and manage computational efforts at peak times that cannot be contained within the combined in-house HPC resources. Last week at Google I/O, Brookhaven National Lab’s Sergey Panitkin discussed the role of the Google Compute Engine in providing computational support to ATLAS, a detector of high-energy particles at the Large Hadron Collider (LHC).
Frank Ding, engineering analysis & technical computing manager at Simpson Strong-Tie, discussed the advantages of utilizing the cloud for occasional scientific computing, identified the obstacles to doing so, and proposed workarounds to some of those obstacles.
May 23, 2013 |
The study of climate change is one of those scientific problems where it is almost essential to model the entire Earth to attain accurate results and make worthwhile predictions. In an attempt to make climate science more accessible to smaller research facilities, NASA introduced what they call ‘Climate in a Box,’ a system they note acts as a desktop supercomputer.
May 16, 2013 |
When it comes to cloud, long distances mean unacceptably high latencies. Researchers from the University of Bonn in Germany examined those latency issues of doing CFD modeling in the cloud by utilizing a common CFD and its utilization in HPC instance types including both CPU and GPU cores of Amazon EC2.
05/10/2013 | Cleversafe, Cray, DDN, NetApp, & Panasas | From Wall Street to Hollywood, drug discovery to homeland security, companies and organizations of all sizes and stripes are coming face to face with the challenges – and opportunities – afforded by Big Data. Before anyone can utilize these extraordinary data repositories, however, they must first harness and manage their data stores, and do so utilizing technologies that underscore affordability, security, and scalability.
04/02/2012 | AMD | Developers today are just beginning to explore the potential of heterogeneous computing, but the potential for this new paradigm is huge. This brief article reviews how the technology might impact a range of application development areas, including client experiences and cloud-based data management. As platforms like OpenCL continue to evolve, the benefits of heterogeneous computing will become even more accessible. Use this quick article to jump-start your own thinking on heterogeneous computing.