Visit additional Tabor Communication Publications
December 16, 2005
IBM has announced that Chevron Corporation selected an IBM cluster built with AMD Opteron processor-based IBM eServer 326 servers to power its compute-intensive depth imaging technology. Working with IBM, Chevron has been able to process data up to seven times faster, enabling the company to make quicker, more accurate choices before drilling.
Oil exploration and drilling are expensive undertakings, and both time and accuracy are critical. Chevron relies on sub-surface pictures of its land to choose where to drill and minimize the associated risks. By running its depth imaging technology on more than 700 nodes of dual-core AMD Opteron processor-based IBM cluster, Chevron is able to turn data around faster, improving processing time and productivity.
Chevron's cluster runs Linux and provides compatibility for both 32-bit and 64-bit applications, allowing Chevron to continue running some 32-bit applications while taking advantage of 64-bit computing.
"Chevron's top priority is growing its business and serving partners, not IT. By choosing an IBM e326-based cluster powered by AMD Opteron processors, Chevron created a platform that allows it to achieve its goals while operating more efficiently," said Leo Suarez, vice president, IBM eServer xSeries. "To IBM, that is the definition of on-demand. Customers like Chevron recognize the value of our on-demand strategy, and, as a result, IBM continues to win customers from competitors."
The IBM e326 is a high-performance 1U server that was upgraded in April to include support for dual-core AMD Opteron processors. In 2003, IBM was the first tier one OEM to introduce AMD Opteron support with their server and workstation line.
"IBM has continued to take a leadership role in delivering powerful systems to address the demands of enterprises running business-critical applications," said Kevin Knox, vice president, Commercial Business, AMD. "AMD is the undisputed leader in x86 dual-core processing performance and energy cost-savings for servers and workstations which helps companies like Chevron deliver essential resources quickly, efficiently and cost-effectively."
In quieter times, sounding the bell of funding big science with big systems tends to resonate further than when ears are already burning with sour economic and national security news. For exascale's future, however, the time could be ripe to instill some sense of urgency....
In a recent solicitation, the NSF laid out needs for furthering its scientific and engineering infrastructure with new tools to go beyond top performance, Having already delivered systems like Stampede and Blue Waters, they're turning an eye to solving data-intensive challenges. We spoke with the agency's Irene Qualters and Barry Schneider about..
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).
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 22, 2013 |
At some point in the not-too-distant future, building powerful, miniature computing systems will be considered a hobby for high schoolers, just as robotics or even Lego-building are today. That could be made possible through recent advancements made with the Raspberry Pi computers.
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.
May 15, 2013 |
Supercomputers at the Department of Energy’s National Energy Research Scientific Computing Center (NERSC) have worked on important computational problems such as collapse of the atomic state, the optimization of chemical catalysts, and now modeling popping bubbles.
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.
04/15/2013 | Bull | “50% of HPC users say their largest jobs scale to 120 cores or less.” How about yours? Are your codes ready to take advantage of today’s and tomorrow’s ultra-parallel HPC systems? Download this White Paper by Analysts Intersect360 Research to see what Bull and Intel’s Center for Excellence in Parallel Programming can do for your codes.
In this demonstration of SGI DMF ZeroWatt disk solution, Dr. Eng Lim Goh, SGI CTO, discusses a function of SGI DMF software to reduce costs and power consumption in an exascale (Big Data) storage datacenter.
The Cray CS300-AC cluster supercomputer offers energy efficient, air-cooled design based on modular, industry-standard platforms featuring the latest processor and network technologies and a wide range of datacenter cooling requirements.