More details at top500.org.

AUSTIN, Texas, — Financial institutions are turning to graphics processing unit (GPU) computing for real economic and performance benefits. Fast and accurate derivatives pricing model development and accelerated execution speeds are crucial for today’s derivatives marketplace. SciComp Inc. has enhanced SciFinance®, its flagship derivatives pricing software, to help quantitative developers further shorten Monte Carlo derivatives pricing model development time and create models with faster execution speeds. SciFinance® now features support for NVIDIA® Tesla™ 20-series GPUs and CUDA™ 3.0.

“The mathematical problems of pricing derivatives are tailor-made for GPU computing, and Monte Carlo simulations enjoy some of the fastest speed-ups on GPUs: from 50 to over 300 times faster compared to serial code,” said Curt Randall, executive vice president of SciComp. “This execution speed increase makes it feasible to replace grid solutions (CPUs and interconnects) with a GPU system. GPU costs are a tiny percentage of the cost of a grid solution and offer radical reductions in both footprint and power consumption.”

SciFinance takes advantage of new GPU hardware and software from NVIDIA

“Our customers can quickly take advantage of the speed increases afforded by NVIDIA’s latest hardware and software enhancements,” added Randall. “SciFinance automatically takes care of the CUDA programming issues. Customers need not have any CUDA or parallel computing expertise, and no hand coding is needed. All it takes is one keyword “CUDA” added to a pricing model specification and SciFinance automatically produces optimized GPU-enabled pricing model source code.”

The new NVIDIA Tesla 20-series GPUs represent a speed and feature step up from the previous generation GPUs. CUDA, a parallel computing architecture developed by NVIDIA, gives users the ability to unlock the parallel computational power of GPUs. SciFinance now supports the updated CUDA 3.0 Toolkit. At a keystroke, users can recompile their existing pricing model source code and take advantage of CUDA 3.0 for instant speedups in their derivatives pricing model runtimes.

About SciComp Inc.

A recognized leader in derivatives pricing software, SciComp is the developer of SciFinance, a financial compiler for generating C, C++ or CUDA pricing source code from concise, high-level model specifications. SciComp’s global customer base includes investment banks, money center banks, asset managers, insurance companies, hedge funds and service providers. Derivatives instruments supported include equity derivatives, convertible bonds, cross currency/interest rate derivatives, commodity/energy derivatives, FX products, credit derivatives and cross asset structures. Visit http://www.scicomp.com or call 512-451-1050 for more information.

###

All rights reserved. SciFinance is a registered trademark of SciComp Inc. NVIDIA, Tesla and CUDA are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Features, pricing, availability, and specifications are subject to change without notice. Certain statements in this press release including, but not limited to, statements as to: the benefits, features, uses, impact, and capabilities of NVIDIA GPUs, NVIDIA CUDA technology and SciFinance are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations.

For more information, please contact:

Stacy Formby
SciComp Inc.
sformby@scicomp.com
+1 512 451 1050 x212

http://www.scicomp.com

New Software Solution Reduces Dependency on CPUs

PORTLAND, Ore.- SC09-Nov. 18, 2009- NVIDIA Corporation (Nasdaq: NVDA) and Mellanox Technologies Ltd. today introduced new software that will increase cluster application performance by as much as 30% by reducing the latency that occurs when communicating over Mellanox InfiniBand to servers equipped with NVIDIA Tesla™ GPUs.

The system architecture of a GPU-CPU server requires the CPU to initiate and manage memory transfers between the GPU and the InfiniBand network. The new software solution will enable Tesla GPUs to transfer data to pinned system memory that a Mellanox InfiniBand solution is able to read and transmit over the network. The result is increased overall system performance and efficiency.

“NVIDIA Tesla GPUs deliver large increases in performance across each node in a cluster, but in our production runs on TSUBAME 1 we have found that network communication becomes a bottleneck when using multiple GPUs,” said Prof. Satoshi Matsuoka from Tokyo Institute of Technology. “Reducing the dependency on the CPU by using InfiniBand will deliver a major boost in performance in high performance GPU clusters, thanks to the work of NVIDIA and Mellanox, and will further enhance the architectural advances we will make in TSUBAME2.0.”

“In GPU-based clusters, most of the compute intensive processing is running on the GPUs,” said Gilad Shainer, director of high performance computing and technical marketing at Mellanox Technologies. “It’s a natural evolution of the system architecture to enable GPUs to communicate more intelligently over InfiniBand. This helps create a computing platform that will enable future Exascale computing and dramatically increase performance for a broad spectrum of applications.”

“Anyone who cares about performance in their datacenter uses InfiniBand,” said Andy Keane, general manager, Tesla business at NVIDIA. “This new feature will further improve application performance on GPU-based clusters by reducing the dependency on the CPU for communicating over InfiniBand.”

This software capability will be available in the NVIDIA CUDAT architecture toolkit beginning in Q2 2010 and will work on existing Tesla S1070 1U computing systems and Tesla M1060 module-based clusters and also with the new Tesla 20-series S2050 and S2070 1U systems.

For more information please visit http://www.renderstream.com/HPC.html or email info@renderstream.com.

SAN FRANCISCO, Aug. 11 — Penguin Computing, experts in high performance computing solutions, today announced the immediate availability of “Penguin on Demand” — or POD — a new service that delivers, for the first time, a complete high performance computing (HPC) solution in the cloud. POD extends the concept of cloud computing by making optimized compute resources designed specifically for HPC available on demand. POD is targeted at researchers, scientists and engineers who require surge capacity for time-critical analyses or organizations that need HPC capabilities without the expense and effort required to acquire HPC clusters.

POD provides a computing infrastructure of highly optimized Linux clusters with specialized hardware interconnects and software configurations tuned specifically for HPC. Rather than utilizing machine virtualization, as is typical in traditional cloud computing, POD allows users to access a server’s full resources at one time for maximum performance and I/O for massive HPC workloads.

Comprising high-density Xeon-based compute nodes coupled with high-speed storage, POD provides a persistent compute environment that runs on a head node and executes directly on the compute nodes’ physical cores. Both GigE and DDR high-performance Infiniband network fabrics are available. POD customers also get access to state-of-the-art GPU supercomputing with NVIDIA Tesla processor technology. Jobs typically run over a localized network topology to maximize inter-process communication, to maximize bandwidth and minimize latency.

Mark Harris (NVIDIA) will present an introduction to the CUDA architecture, programming model, and the programming environment of C for CUDA, as well as an overview of the Tesla GPU architecture, a live programming demo, and strategies for optimizing CUDA applications for the GPU. To better enable the uptake of this technology, Dragan Dimitrovici from Xenon Systems will provide an overview of CUDA enabled hardware options. The workshop will also include brief presentations of some of the projects using CUDA within Western Australia, including a presentation from Professor Karen Haines (WASP@UWA) on parallel computing strategies required for optimizing applications for GPU and heterogeneous computing.

Please see the workshop flyer for full details.

More details from an NVIDIA press release:

NVIDIA Tesla Powers 29th Most Powerful Supercomputer in the World

SC08—AUSTIN, TX—NOVEMBER 17, 2008—The Tokyo Institute of Technology (Tokyo Tech) today announced a collaboration with NVIDIA to use NVIDIA® Tesla™ GPUs to boost the computational horsepower of its TSUBAME supercomputer. Through the addition of 170 Tesla S1070 1U systems, the TSUBAME supercomputer now delivers nearly 170 TFLOPS of theoretical peak performance, as well as 77.48 TFLOPS of measured Linpack performance, placing it, again, amongst the top ranks in the world’s Top 500 Supercomputers.

“Tokyo Tech is constantly investigating future computing platforms and it had become clear to us that to make the next major leap in performance, TSUBAME had to adopt GPU computing technologies,” said Satoshi Matsuoka, division director of the Global Scientific Information and Computing Center at Tokyo Tech. “In testing our key applications, the Tesla GPUs delivered speed-ups that we had never seen before, sometimes even orders of magnitude – a tremendous competitive boost for our scientists and engineers in reducing their time to solution.”

Speaking to the ease of implementation, Matsuoka continued,

“The entire upgrade was carried out in 1 week, and the TSUBAME supercomputer remained live throughout. This is an unprecedented feat in top-level supercomputing.”

“We are honored to partner with Tokyo Tech – world famous for their supercomputing expertise and success,” said Andy Keane, general manager of the GPU Computing business at NVIDIA. “NVIDIA Tesla breaking into the Top 500 marks a milestone in supercomputing history. The massively parallel GPU is now essential for supercomputing centers worldwide.”

The first to achieve Top 500 ranking with an NVIDIA Tesla based GPU cluster, Tokyo Tech is one of hundreds of distinguished universities and supercomputing centers that have adopted GPU based solutions for research. Other leading centers include the National Center of Supercomputing Applications (NCSA) at the University of Illinois, Rice University, University of Heidelberg, University of Maryland, Max Planck Institute and University of North Carolina.

The Tesla S1070 1U GPU system is based on the NVIDIA CUDA™ parallel architecture. This architecture is accessible through an industry standard C language programming environment that allows developers and researchers to tap into the parallel architecture of the GPU more quickly and easily than any other solution shipping today.

For more information on NVIDIA Tesla S1070, please visit: www.nvidia.com/object/tesla_s1070

For more details including contact information, see the full press release.

NVIDIA Tesla Makes Personal SuperComputing A Reality

Tesla GPUs Enable Cluster Class Performance On The Desktop at 1/10th The Power

SC08—AUSTIN, TX—NOVEMBER 18 2008— Today, scientific research is carried out on supercomputing clusters, a shared resource that consumes hundreds of kilowatts of power and costs millions of dollars to build and maintain. As a result, researchers must fight for time on these resources, slowing their work and delaying results. NVIDIA and its worldwide partners today announced the availability of the GPU-based Tesla™ Personal Supercomputer, which delivers the equivalent computing power of a cluster, at 1/100th of the price and in a form factor of a standard desktop workstation.

“We’ve all heard ‘desktop supercomputer’ claims in the past, but this time it’s for real,” said Burton Smith, Microsoft Technical Fellow. “NVIDIA and its partners will be delivering outstanding performance and broad applicability to the mainstream marketplace. Heterogeneous computing, where GPUs work in tandem with CPUs, is what makes such a breakthrough possible.”

Priced like a conventional PC workstation, yet delivering 250 times the processing power, researchers now have the horsepower to perform complex, data-intensive computations right at their desk, processing more data faster and cutting time to discovery.

“GPUs have evolved to the point where many real world applications are easily implemented on them and run significantly faster than on multi-core systems,” said Prof. Jack Dongarra, director of the Innovative Computing Laboratory at the University of Tennessee and author of LINPACK. “Future computing architectures will be hybrid systems with parallel-core GPUs working in tandem with multi-core CPUs.”

Leading institutions including MIT, the Max Planck Institute, University of Illinois at Urbana-Champaign, Cambridge University, and others are already advancing their research using GPU-based personal supercomputers.

“GPU based systems enable us to run life science codes in minutes rather than the hours it took earlier. This exceptional speedup has the ability to accelerate the discovery of potentially life-saving anti-cancer drugs,” said Jack Collins, manager of scientific computing and program development at the Advanced Biomedical Computing Center in Frederick Md., operated by SAIC-Frederick, Inc.

At the core of the GPU-based Tesla Personal Supercomputer is the Tesla C1060 GPU Computing Processor which is based on the NVIDIA® CUDA™ parallel computing architecture. CUDA enables developers and researchers to harness the massively parallel computational power of Tesla through industry standard C.

“Dell has led the workstation category for almost a decade and GPU computing represents a massive leap forward in performance that will bring supercomputer power to the masses,” said Antonio Julio, director, Dell Product Group. “The Dell Precision R5400 and T7400 will allow the scientific community to harness the capabilities of the NVIDIA Tesla C1060 GPU with up to two teraflops of computational power.”

As well as Dell, GPU-based Tesla Personal Supercomputers are available today from the following leading HPC OEMs, Systems Builders and Resellers: AMAX (US), Armari (UK), Asus (WW), Azken Muga (ES), Boxx (US), CAD2 (UK), CADnetwork (DE), Carri (FR), Colfax (US), Comptronic (DE), Concordia (IT), Connoisseur (IN), Dell (WW), Dospara (JP), E-Quattro (IT), JRTI (US), Lenovo (WW), Littlebit (CH), Meijin (RU), Microway (US), Sprinx (CZ), Sysgen (DE), Transtec (DE),Tycrid (US), Unitcom (JP), Ustar (UKR),Viglen (UK), Western Scientific (US)

To learn more about the industry-changing applications benefitting from NVIDIA GPU Computing technology, visit www.nvidia.com/cuda and for more information on the GPU-based NVIDIA Tesla Personal Supercomputer, please visit www.nvidia.com/personal_supercomputing.

See the complete press release for full details.