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06/30/09 Penguin Computing Delivers University Of Delaware’s Fastest Supercomputer to Global Computing Laboratory
Penguin Computing announced that the University of Delaware Global Computing Laboratory has deployed the university’s largest supercomputer, code-named “Geronimo”, based on a custom GPGPU design utilizing NVIDIA Tesla GPU computing technology coupled with Intel 5400 series processors.
06/23/09 PGI and NVIDIA Team To Deliver CUDA Fortran Compiler
The Portland Group announced an agreement with NVIDIA under which the two companies plan to develop new Fortran language support for CUDA GPUs.
06/22/09 Supermicro and NVIDIA Smash 1U Server Performance Records at International SuperComputing (ISC) 2009
Super Micro Computer, Inc. is showcasing the fastest 1U server on the planet, its new, 2-Teraflop SuperServer 6016GT-TF-TM2 at ISC'09. This massively parallel processing dual-GPU server is the first 1U multi-GPU (graphics processing unit) system with a fully non-blocking architecture.
06/19/09 AMAX Launches Tesla GPU Testing Lab
AMAX has established a GPU parallel computing lab for inquiring HPC customers to experience Tesla's revolutionary performance. The GPU systems available for remote testing are ready equipped with the CUDA programming environment. AMAX's knowledgeable CUDA engineers are available for immediate consultation.
06/16/2009 Bull Makes Big Push Into HPC with New Supercomputer Blades
French-owned computer maker Bull has unveiled a new family of HPC servers based on a novel blade architecture. Branded as "bullx," the blades come in two flavors: CPU-only and GPU-accelerated. Both versions are based on dual-socket Nehalem EP (Xeon 5500) nodes, but the accelerator blades include up to two NVIDIA Tesla M1060 GPUs on board.
06/15/09 CAPS to Launch CAPS Compute Lab with BULL and NVIDIA
CAPS Entreprise announces the launch of its CAPS Compute Lab, a first and exclusive EMEA solution center for hybrid computing with both BULL and NVIDIA partners.
06/11/2009 Standard GPU Cluster Provides High Performance In The Mid-Range (page 25)
Supercomputing continues to get faster, cheaper, and more available. Costs are dropping rapidly partially because of graphics processing units (GPUs) and their highly parallel architecture.
06/08/09 Allinea to Enhance DDT Debugging Tool for GPGPU Hybrid through collaboration with CEA
Allinea Software has signed a collaboration agreement with CEA to develop enhancements to Allinea's Distributed Debugging Tool for next generation hybrid and "many-core" computer systems. Once developed, this technology will be made available to Allinea's customers.
06/01/2009 NVIDIA, Supermicro Give Birth to CPU-GPU Server
Until now, the only practical way for customers to get GPU-accelerated clusters was to combine NVIDIA's own S1070 Tesla servers with x86 CPU servers from a traditional system vendor. Before May, the onus was on the users to configure the Tesla and x86 boxes themselves. But on May 4, NVIDIA launched its pre-configured cluster program, which brought in OEM partners to construct these mixed-processor clusters, allowing customers to purchase pre-built GPU-accelerated systems
06/01/2009 NVIDIA and Supermicro Announce Server with Integrated Tesla Hardware
Supermicro and NVIDIA have announced a new line of server-based machines with integrated Tesla GPUs. The Supermicro SuperServer 6016T-GF-TM2 is a single, 1U chassis with an integrated NVIDIA Tesla GPU. The new server line is marketed towards those looking to make use of NVIDIA’s CUDA programming paradigm designed for massively parallel computing on their GPUs.
06/01/2009 Nvidia, Supermicro Tout 'Highest-Perfomance 1U Server'
Nvidia and SuperMicro will team up on a 1U server that combines two CPUs and two GPUs, all to be used for computational-intensive algorithms. The two will claim that the SuperServer 6016, due in June, is the world's fastest 1U server, according to Andy Walsh, the director of product marke ting for Nvidia.
06/01/2009 New GPU-based SuperServer delivers 12X more computing power
NVIDIA and Supermicro today announced the immediate availability of a new class of server that combines massively parallel NVIDIA Tesla GPUs with multi-core CPUs in a single 1U rack-mount server. This unique configuration delivers 12 times the performance of a traditional quad-core CPU-based 1U server. Supermicro will be demonstrating the NVIDIA Tesla-based SuperServer 6016T-GF-TM2 at Computex 2009 in Taiwan this week.
05/31/2009 Supermicro launches Nvidia Tesla fueled server
Supermicro and Nvidia took the wraps off a class of server that’s turbo charged by graphics processors. At the Computex trade show in Taiwan, Supermicro is demonstrating a server that features Nvidia’s Tesla GPUs with multi-core processors in a single 1U rack server.
05/07/2009 Dell "Personal Supercomputers" Now Available With NVIDIA Tesla GPUs
If you’re worried that just one of these GPUs isn’t enough to handle your hardcore needs, worry not – just one C1060 has enough power to control the main system of the European Extremely Large Telescope project (reportedly the world’s largest).
05/06/2009 Tesla-Based Clusters, Workstations Shipping
Needless to say, there's a lot of power going on whether it's a Tesla-charged Dell Precision workstation, or a Tesla Preconfigured Cluster from NVIDIA.
05/06/2009 French Bank Takes On GPU Computing
Using just two of the four GPUs on an NVIDIA S1070 board, they were able to achieve a 15-fold performance increase and a 100-fold power improvement in performance per watt in this one procedure.
05/04/2009 NVIDIA Shifts GPU Clusters Into Second Gear
The good news is that in the GPU computing realm, NVIDIA is the clear market leader.
05/05/2009 Introducing the personal supercomputer’s big brother: NVIDIA’s Tesla preconfigured clusters
NVIDIA’s new Tesla project is the Preconfigured Cluster, which the company calls “Accessible Supercomputing,” and it follows the model of the Personal Supercomputer project.
05/05/2009 Incremental Twiddling
As GPU Clusters hit the market, users are finding small code changes can result in big rewards.
04/30/2009 The Supercomputer Goes Personal
Today, graphics titan NVIDIA advertises its new workstation, the Tesla, as a “personal supercomputer.” It clusters four NVIDIA C1060 processing boards, each of which unites 240 graphics cores to process instructions at nearly teraflops speeds. We calculate it as about 17 percent more cost-effective than Khanna’s PS3 solution, and a lot more elegant.
04/27/2009 NVIDIA's graphic chips moving into high-end computing arena
Tesla is NVIDIA's bold move to stretch its business well beyond graphics. With considerable software development and some hardware tweaking, NVIDIA can turn advanced graphics chips into powerful number-crunching engines that can attack some of the same parallel-processing problems that cluster computers and even low-end supercomputers go after.
04/22/2009 BNP Paribas will use NVIDIA for its GPU solution but competition in the market is set to open up
NVIDIA actually implemented an architecture for GPU computing in CUDA, while the programming environment that developers can then use to access that capability is called C with CUDA extensions.
04/13/2009 BNP Speeds Risk Calculations With Hardware Acceleration
BNP Paribas, moving to bolster its computational power, has implemented a new technology platform designed to not only accelerate calculation times for complex equity derivatives but also slash energy consumption.
04/11/2009 Collaboration Leads to Success: Most Powerful Computer of its Kind in WNY Available World-Wide
Professor Jack Dongarra, one of the foremost authorities on high-end computing and director of the Innovative Computing Laboratory at the University of Tennessee said, “GPUs have evolved to the point where real-world applications are easily implemented on them and run faster than on multi-core systems. Future computing architectures will be hybrid systems with parallel-core GPUs working in tandem with multi-core CPUs.”
04/09/2009 Programming The CUDA Architecture: A Look At GPU Computing
Graphics processing units (GPUs) were originally designed to perform the highly parallel computations required for graphics rendering. But over the last couple of years, they’ve proven to be powerful computing workhorses across more than just graphics applications.
04/01/2009 GPUs: Here to Stay
The fact that GPU chipmaker NVIDIA has made porting code for GPUs easier for the average bench biologist with its CUDA software technology helps the argument for considering this breed of acceleration technology.
03/30/2009 STFC Daresbury Laboratory to Use Streamline Computing Cluster
Daresbury Laboratories, one of the UK’s most prestigious publically funded research institutes, has announced its new computing cluster will incorporate NVIDIA Tesla and CUDA technology. The mission of Daresbury Labs is to make it possible for a broad range of scientists to do the highest quality research tackling some of the most fundamental scientific questions.
03/25/2009 NVIDIA's Lock on GPU Computing Can Be Picked
In the HPC space, GPU computing is the most compelling technology to come on the scene in recent memory, and NVIDIA has jumped out to an early lead. Because of CUDA and some hardware innovations, NVIDIA is probably at least a year ahead of AMD and two years ahead of Intel (Larrabee) on this front.
03/24/2009 Lenovo Puts Supercomputing on the Desktop
Lenovo's new ThinkStation S20 and D20 can be configured to function as desktop supercomputers. Due later this month starting at $1,070, an optional Nvidia Tesla C1060 GPU adds more than $2,000 but boosts power for complex calculations.
03/24/2009 Lenovo Refreshes Workstations With Desktop Supercomputer
Lenovo on Tuesday refreshed its workstation line with two computers available with the Nvidia Tesla C1060 platform for companies looking to turn the systems into desktop supercomputers.
03/24/2009 Lenovo Announces New Workstations with NVIDIA Tesla Option
Lenovo has announced new business class workstation computers called the S20 and the D20 that are both aimed at professionals in the CAD, digital content creation and oil/gas fields.
3/18/2009 CUDA, Supercomputing for the Masses: Part 11
Optimizing the performance of CUDA applications most often involves optimizing data accesses which includes the appropriate use of the various CUDA memory spaces. Texture memory provides a surprising aggregation of capabilities including the ability to cache global memory (separate from register, global, and shared memory) and dedicated interpolation hardware separate from the thread processors.
3/12/2009 Penguin Computing Unveils Pre-Configured Clusters
Hoping to make graphical processing unit- (GPU) based computing appealing to a wider audience, Penguin Computing has unveiled two pre-configured Nvidia Tesla and AMD-based clusters.
3/6/2009 The Coming of the Megacomputer
Here's an incredible, and telling, data point. In a talk yesterday, reports the Financial Times' Richard Waters, the head of Microsoft Research, Rick Rashid, said that about 20 percent of all the server computers being sold in the world "are now being bought by a small handful of internet companies," including Microsoft, Google, Yahoo and Amazon.
3/5/2009 BNP Paribas Uses Graphics Cards to Price Derivatives
In its efforts to promote green computing, BNP Paribas Corporate and Investment Banking has the implemented a new GPU-based architecture that the firm says simultaneously reduces electricity consumption and accelerates calculation times.
3/2/2009 Analytical Visualization Uncovers More Detail
You might have noticed that high-end visualization is getting better looking. You’ll see translucency, transparency—even texture mapping—to place an image of the actual design (e.g., a circuit board) onto the simulation model.
3/1/2009 A supercomputer chip for every man
While GPUs (graphics processing units) were initially designed to accelerate video and gaming, vendors of science and engineering software are using them to accelerate their code. Paul Schreier looks at the benefits for today and tomorrow.
2/28/2009 NVIDIA's GPU Enters High-Performance Computing
The Cell microprocessor, jointly developed by IBM Corp of the US, Sony Corp of Japan and Toshiba Corp of Japan, was positioned as a new processor capable of covering a wide range of applications with one architecture, including high-performance computing, game systems and household appliances. Today, though, the future sketched out for the Cell is instead being realized by graphics processing units (GPU).
2/19/2009 Revisiting the Memory Wall
Today, you can get an NVIDIA Tesla GPU with 4 GB of (GDDR3) memory at 102 GB/second of bandwidth. Granted this is graphics memory, so you have to deal with the lack of error correction, but at roughly three times the memory performance available to a Nehalem processor, GPUs can offer some respite from the memory wall.
2/19/2009 Nvidia Tesla C1060 GPGPU - Double precision
The Nvidia Tesla board is a 240 core GPGPU which supports massively parallel programming through a threading model. Each of the cores has a speed of a 1.5GHz , and can carry out multiple operations per clock cycle giving a theoretical peak performance of 1 Terra Flop.
2/18/2009 High-end clusters a bright spot in downturn
The high performance computing market will decline 5.4 percent in 2009, but remain one of the most resilient sectors in information technology through the recession, according to the latest forecast released Wednesday (Feb 18) from International Data Corp. (Framingham, Mass.).
1/29/2009 CUDA, Supercomputing for the Masses: Part 10
In this installment, I examine CUDPP, the "CUDA Data Parallel Primitives Library." CUDPP is a quickly maturing package that implements some not-so-obvious algorithms to efficiently use the GPU for basic data-parallel operations such as sorting, stream compaction, and even building data structures like trees and summed-area tables. I discuss CUDPP here because it might provide some of the functionality needed to quickly speed the development of one of your projects.
1/28/2009 CUDA and OpenCL
Martin answers some questions about his recent post on GPU computing and CUDA.
1/27/2009 Supercomputing and the Cloud
High-performance hardware has a completely different meaning today than it did just a couple of years ago. There was a significant performance gap between clustered industry-standard hardware and the top-end limited edition and often proprietary systems.
1/26/2009 AccelerEyes Launches GPU Engine for MATLAB
The nascent GPGPU computing world received another boost today with the commercial release of Jacket 1.0, a GPU engine for MATLAB. Jacket was developed by AccelerEyes, a two-year-old Atlanta-based startup that was founded by Georgia Tech grad John Melonakos, who also runs the company.
1/26/2009 Computing in the Cloud: Could Subsidies Jumpstart PC Sales?
Volunteer distributed computing projects have been around for a while. You've probably heard of for example, SETI@home, a Berkeley project launched in 1999 to listen for radio signals from ET. Hey, maybe you even run the work manager as your screensaver. Another example would be the Stanford protein folding study project, Folding@home. I call this whole class, HPC@home (High Performance Computing).
1/25/2009 Personal Supercomputing
Well, with NVidia's new “Tesla personal supercomputer” you can now have the serious digital horsepowerof a supercomputer at home. The beast delivers cluster computing performance and up to 250 times faster computing then a standard top of the range PC.
1/23/2009 CUDA from NVIDIA - Turbo-Charging High Performance Computing
As you can see from the few examples provided here, the performance gain from GPGPU is undeniable. Processes that used to take days now take only hours; and those that used to take minutes can now be accomplished in real time. And we have CUDA to thank for all of this, as it has truly unlocked the potential of GPGPU on NVIDIA graphics cards.
1/23/2009 2009 Preview: Tech to Watch For in the Year Ahead
For years, computers have worked perfectly well with a central processing unit (CPU) that did the thinking and a graphics processor (GPU) to make everything on the screen look pretty. GPUs on separate graphics cards were easily replaced—all the better for gamers, who were most likely to benefit from the rapid advances in GPU technology.
1/23/2009 Supercomputing Hits the Desktop
Much like how the advent of the PC forever altered the work habits and productivity level of everyday corporate employees, the emerging personal supercomputer platform promises to have a similar impact on how engineers, scientists and researchers tackle complex design and simulation work.
1/22/2009 GPU computing is about massive data parallelism
For embarrassingly parallel problems, for example digital tomography, an under-$10,000 Tesla personal supercomputer can beat a $5 million Sun CalcUA. CUDA makes the parallel programming tractable.
1/21/2009 NVIDIA Tesla Personal Supercomputer by Daniel Metz
NVIDIA has harnessed the power of GPUs (graphics processing units) to work in tandem with the CPU (central processing unit) fitting 240 individual cores upon a single platter. With four platters as a base, delivering 250 times the normal processing power at 1/100th of the price has become a reality.
1/20/2009 Converting video with GPU acceleration tested
During the past couple of years the possibilities of video cards have increased to help in more than just 3D modelling and video games. Nowadays video cards can be used in for example breaking password protections, medical research and calculations, as well as video processing.
1/15/2009 Parallel Processing Zooms While Debugging Zags
Parallel processing is everywhere, with almost as many software choices as hardware. Symmetrical-multiprocessing (SMP) designs and clustering dominate large-core, multicore solutions on PCs and servers. Graphics processing units (GPUs) have their own architecture optimized for data flow, while specialized multicore solutions abound.
1/15/2009 Power-Sipping Micros, Multicore Monsters Dot The Landscape
Anyone waiting for a consolidation to occur in the micro or DSP arenas should settle in for the long haul. The choices just keep growing, even as vendors attempt to use software and peripheral consistency to simplify what developers have to deal with. Yet the array of options isn’t the sole purveyor of multiplicity. Multicore also falls into this category, and now it’s moving into embedded.
1/14/2009 NVIDIA Delivers the Personal Supercomputer
NVIDIA Corp. (Santa Clara, CA) announced its GPU-based Tesla Personal Supercomputer at SuperComputing '08. Said to deliver the equivalent computing power of a cluster at 1/100th of the price and 1/20th the power, the NVIDIA Tesla Personal Supercomputer features up to four Tesla C1060 GPU (graphics processing unit) cards, which is based on the company's CUDA parallel computing architecture, and powered by up to 960 parallel processing cores.
