November 03, 2009
The groundswell of enthusiasm for GPU computing was underscored recently by the Institute of Processing Engineering (IPE), Chinese Academy of Sciences (CAS).
Since IPE’s founding in 1958, the institute has made extensive use of high performance computing (HPC) for its investigations into chemical engineering, metallurgy, biochemistry, material science, energy and green technology. IPE notes that, “The semiconductor technology for integrated circuits, which has powered the dramatic development of HPC, is now approaching its physical limit, for the foreseeable future at least. Fine-grained multi-thread and many-core parallel computation is, most probably, the only get-around to maintain this development, and NVIDIA’s massively parallel CUDA™ architecture has established a new paradigm at this critical moment.
IPE is not alone. Companies, laboratories and research institutions around the world are turning to GPU computing to augment or even replace their CPU clusters. The power of the GPU model has become available to developers through CUDA, a software and hardware architecture that enables GPUs to be programmed using high level languages such as C. And now NVIDIA has introduced the next generation of CUDA GPU architecture, codenamed “Fermi”, that delivers breakthroughs in both graphics and GPU computing, plus support for C++ in addition to C, Fortran, Java, Python, OpenCL and Direct Compute.
GPU computing is an international phenomenon. Below are brief descriptions of how three organizations, the Institute of Processing Engineering at the prestigious Chinese Academy of Sciences, Bloomberg in the United States and the U.K.’s BAE Systems, are using NVIDIA GPU-based solutions to meet their HPC requirements.
IPE at CAS, located in Beijing, has been developing the energy-minimization multi-scale (EMMS) method for more than 25 years. Though the method was originally aimed at multi-phase flow, it gave birth to a highly scalable and general-purpose simulation methodology for different systems that took into account the structural similarities between the HPC hardware, software and the problem to be solved. An algorithm framework applicable to a wide range of physical systems and computer architectures was thereby proposed during 2000~2003, but put on hold due to the fact that the institute’s CPU-based systems could not handle the computational complexity involved.
In 2007, with the release of CUDA 1.0 from NVIDIA, the situation changed dramatically. Within a year IPE had deployed its first GPU computing system that embodied their EMMS principle. Based on 126 HP workstations with 200 NVIDIA Tesla C870 GPU computing processors, the system, Mole-9.7, delivered over 100 teraflops (TF) of peak performance in single precision, enough horsepower to tackle some of their most pressing simulation problems.
That was just the beginning. The IPE system later received another shot in the arm with the addition of over 600TF worth of NVIDIA GT200 GPUs, and was interconnected with other IPE computational resources over a 20 Gbp/s InfiniBand fabric. The whole GPU-based system, known as Mole -8.7, broke the petaflops (PF) barrier in March 2009 and was by far the largest GPU cluster in China and one of the largest in the world.
With its ability to realize the structural similarity between hardware, software and the computed system, IPE is in an advantageous position to solve industrial problems efficiently using such an HPC system.
Here are just a few of the CUDA applications at IPE:
In recognition of its extensive development and advancement of science through the use of CUDA and GPU computing, NVIDIA recently awarded CAS-IPE the distinction of a CUDA Center of Excellence. NVIDIA and CAS-IPE will continue to work together to further the value of GPU computing with next generation technologies.
Despite the balky economy, transactions involving thousands of mortgages are still a routine occurrence in the financial markets. For example, collateralized debt obligations (CDOs) and collateralized mortgage obligations (CMO) make up baskets of thousands of loans that are publicly traded financial instruments.
Bloomberg, headquartered in New York with offices around the world, is a leading financial services organization. One of its services is modeling the risks and determining the price of CMO/CDO baskets for its customers. The company uses powerful algorithms to calculate massive amounts of data and deliver pricing information based on large scale simulation. Using CPUs, these calculations would have taken 16 hours, an unacceptable solution for an overnight computational run.
Bloomberg’s solution was to implement an NVIDIA Tesla GPU computing system in its data center and port the CMO/CDO application to run on the CUDA architecture. The results were immediate and dramatic. Large calculations that dragged on for hours using the CPU-based cluster were now completed in minutes; smaller runs that once took 20 minutes take only seconds.
The speed-ups delivered by NVIDIA CUDA and GPU computing allowed Bloomberg to process the CMO/CDO prices up to 50 times faster than on a CPU. The company is now investigating the possibility of offering real-time pricing - a huge step forward for the industry.
Bottom line, Bloomberg’s pricing calculations for securities backed by assets such as mortgages, home equity loans and auto loans, as well as mortgage-backed securities such as CMOs, have seen an 800% speed up in processing compared to its CPU solution. At the same time, the data center is now consuming three times less power and the NVIDIA hardware is taking up one quarter of the tile space.
Bloomberg is also researching other uses for its GPU computing cluster such as valuations of certain types of derivative products, risk management and portfolio valuations.
In the last analysis, Bloomberg’s customers are the biggest winners in this shift to GPU computing. They are now working with the most current pricing information using the most predictive models, a capability that provides them with a serious competitive advantage in a market where timing is everything.
As the premier global defense, security and aerospace company, BAE Systems designs, builds and supports a wide range of products, from unmanned aircraft to land vehicles, ships and submarines. Understanding the aerodynamic performance of its products, using the principles of computational fluid dynamics (CFD), is a vital part of the design process. However, as the requirements of their customers have become more complex and development timescales more challenging, the simulation tools currently available to BAE Systems’ engineers have begun to limit their ability to affordably explore design options with greater accuracy.
Physical model tests are extremely expensive, with a single wind tunnel test campaign costing more than $700,000, while virtual testing is limited by the power and cost of conventional computing systems. In its efforts to develop more affordable and comprehensive CFD simulations, BAE Systems has turned to NVIDIA Tesla GPUs.
One of the goals of the Mathematical Modeling team at the BAE Systems Advanced Technology Center is to make engineering design tools like CFD more affordable and capable so its simulations need no longer be limited to a small number of design conditions, such as cruise, but can be applied to the whole performance envelope that the products will experience in service. Assessing all the design points of an aircraft can require between ten thousand and twenty million individual simulations, which is an unfeasibly costly and time consuming process using conventional CFD techniques.
Jamil Appa’s team recognized that the highly parallel and computationally intensive nature of its CFD and visualization problems made them ideally suited to processing on the GPU. Using Tesla GPUs, BAE Systems has succeeded in creating a system which packs the power of 60 Nehalem cores into a workstation-sized unit capable of processing both visualization and CFD to generate high fidelity 3D simulations.
“Up until now, everyone thought real time visualization and CFD was too ambitious,” says Jamil Appa. “By running our visualization techniques and CFD codes on the GPU we’ve proved them wrong - we’re now very close to having a system which is not just faster but actually interactive. In tests with production test cases we used an NVIDIA Tesla S1070 solution and achieved 100x times to solution speed-ups, meaning tests which previously took hours now require a matter of minutes.
"GPU computing is extremely exciting because it can deliver both a cost saving, in terms of power, total cost of ownership and software licenses, and a significant increase in performance. We're working with NVIDIA to continue developing the potential of our GPU systems - this technology is hugely significant for our industry and for high performance computing as a whole.”
GPU - A Technology That's Time Has Come
The IPE at the Chinese Academy of Sciences, Bloomberg and BAE Systems are just three examples of the many organizations around the world that are benefitting from GPU computing. Now, with the recent introduction of the “Fermi” architecture in September, 2009, NVIDIA has taken a major step in ensuring that the GPU is a technology that's time has come.
To find out how other organizations around the world are using NVIDIA’s GPU computing solutions to solve their HPC problems, visit http://www.nvidia.com/page/pg_20040317553375.html
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