NVIDIA Unveils Teraflop GPU Computing

By Michael Feldman

June 16, 2008

NVIDIA has announced two new Tesla-branded GPU computing products at ISC’08, continuing the company’s efforts to move into the HPC market. The new products are based on NVIDIA’s next generation 10-series GPU processor architecture. The T10P processor unveiled today offers double precision float point support, more local memory, plus much higher overall performance. NVIDIA is touting the new 10-series chip as the second generation processor for CUDA, the company’s GPU computing development platform.

The T10P, which is built on 55nm process technology, doubles the capability of the previous generation Tesla offerings, which were based the 8-series NVIDIA architecture. The new GPU has twice the FP precision (32-bit to 64-bit) and the raw compute performance (500 gigaflops to 1 teraflop). It’s important to note that the teraflop figure is single precision performance; double precision performance is delivered at a much more modest 100 gigaflops.


The T10P also nearly doubles the number of cores from 128 to 240. The new processor is an evolution of the 8- and 9-series GPUs, and like those older processors, allows NVIDIA to share the same componentry across the Quadro and GeForce product lines. Because of the common architecture, CUDA is able to maintain backward and cross compatibility for applications, and also allows the user software to be independent of the number of cores on the chip. The CUDA driver queues up the application threads and the hardware does the fine-grained mapping of the threads to the processing cores at runtime. So the same CUDA app can run on a cluster, a workstation or a notebook, as long as they contain recent vintage NVIDIA hardware.

Each of the 240 cores in the T10P is implemented as a “thread processor” with an integer unit, floating point unit, and a register file. Eight thread processors are arranged in a thread processor array, which shares a special functions unit (transcendental and other functions) a double precision (DP) floating point unit, and 16KB of shared memory that works at cache speed. Except for the DP unit, the design is the same as the NVIDIA’s 8-series GPU architecture.

In addition to the performance and memory bumps, the T10P will also benefit from a wider memory interface (512 bits), faster memory I/O (102 GB/sec), and upgraded I/O interface (PCIe x16 Gen2). But it’s the DP capability that will make HPC users take notice, especially now that the latest IBM Cell processor (PowerXCell 8i) and AMD FireStream GPU now boast DP capability. The absence of double precision FP support has limited Tesla’s potential market, especially in certain financial and scientific realms where applications need 64-bit floating point math.

The disparity between single and double floating point performance on the T10P reflects a trade-off that NVIDIA made between cost and capability. It also reflects the fact that a lot of HPC users can use 32-bit floating point to eke out more performance, jumping into the slower double precision calculations only when necessary. Nonetheless, the T10P’s 100 DP gigaflops is in the same ballpark as IBM’s PowerXCell 8i, which achieves nearly 109 DP gigaflops, and the brand new ClearSpeed CSX700 processor at 96 gigaflops. However, the new AMD FireStream 9250 GPU breaks out of the pack at 200 DP gigaflops.

The T10P will end up in two new Tesla products: the S1070, a 1U box to be hooked up to HPC servers; and the C1060, an accelerator card for high performance desktop systems. They are being priced aggressively: MSRP for the S1070 is $7,995, a couple of thousand less than the first generation Tesla S870; while MSRP for the C1060 is $1699, $400 less that the previous desktop offering.

The S1070 puts four 1.5 GHz T10P devices in a standard 1U chassis, yielding 4 teraflops of single precision performance plus 16 GB of on-board memory. If the host has a couple of free PCIe 2.0 slots, two S1070 boxes can be attached, producing an 8 teraflop computer node in a 3U space. The large on-board 16 GB of memory (4 GB per T10P) will help minimize the number of host memory transfers, which slow down application performance when data sets are large.

A single S1070 draws 700 watts when heavily loaded, compared to about 550 watts for the previous generation S870 offering. But since NVIDIA has doubled the FLOPS, that represents much better performance per watt. At 700 watts, the company is pushing the upper end of the power envelope for a 1U box — most Xeon or Opteron servers are in the 400W-500W range. But NVIDIA believes most users they’re going after are more concerned with compute density and FLOPS/watt than they are their electric bill.

The C1060 card is for technical workstations and packs a single T10P GPU. With a slightly slower clock (1.33 GHz) on the GPU than the server offering, peak performance tops out at around 887 single precision gigaflops, with double precision proportionately less. The slower clock was necessary to keep the device inside of 160 watts, a more reasonable thermal envelope on a desktop.

NVIDIA hopes to parlay the new products into an expanded footprint in the HPC market. Although the company isn’t sharing unit sales of the first generation Tesla boxes, Geoff Ballew, product manager for the Tesla Server group, did say they have around 250 HPC customers on CUDA platforms spread across the usual suspects of HPC verticals: oil & gas, finance, medical, digital content, and research.

“Oil and gas is an area where we’ve had tremendous success,” says Ballew, “one, because the price of a barrel of oil keeps going up, so they’re very motivated to use new tools to find more oil. But it’s also been one where their problem is nicely aligned with our [solution], and they’ve been scratching their heads on how to get the performance they want out of traditional clusters.”

Examples of some of the larger Tesla installations include Hess, NCSA, JFCOM, SAIC, University of Illinois, University of North Carolina, Max Plank Institute, Rice University, University of Maryland, GusGus, Eotvas University, University of Wuppertal, IPE/Chinese Academy of Sciences, and a number of unnamed Cell phone manufacturers. Ballew assured me that he had a lot more customers that he couldn’t talk about yet.

NVIDIA has an even broader base of users that could drive future Tesla sales. The company estimates they have 70 million CUDA-capable GPUs — Tesla, GeForce, and Quadro — deployed and more than 60 thousand CUDA downloads. If the company can move some percentage of these grassroots customers onto Tesla platforms, they’ll have a steady supply of new customers.

The Tesla products announced today won’t go into production until August, so we’ll see only demo systems at ISC this week. But NVIDIA is hinting that Tesla-equipped supercomputers could appear on the November TOP500 list, with perhaps even a system that breaks into the top 20.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

DARPA, NSF Seek Real-Time ML Processor

March 18, 2019

A new U.S. research initiative seeks to develop a processor capable of real-time learning while operating with the “efficiency of the human brain.” The National Science Foundation (NSF) and the Defense Advanced Re Read more…

By George Leopold

It’s Official: Aurora on Track to Be First U.S. Exascale Computer in 2021

March 18, 2019

The U.S. Department of Energy along with Intel and Cray confirmed today that an Intel/Cray supercomputer, "Aurora," capable of sustained performance of one exaflops, will be delivered by the end of 2021 to Argonne Nation Read more…

By Tiffany Trader

NASA’s Pleiades Simulates Launch Abort Scenarios

March 15, 2019

NASA is using flow simulations running on its Pleiades supercomputer to help design the agency’s next manned spacecraft, Orion. Crew safety is paramount, so NASA engineers are using the HPC cluster to simulate and v Read more…

By George Leopold

HPE Extreme Performance Solutions

HPE and Intel® Omni-Path Architecture: How to Power a Cloud

Learn how HPE and Intel® Omni-Path Architecture provide critical infrastructure for leading Nordic HPC provider’s HPCFLOW cloud service.

powercloud_blog.jpgFor decades, HPE has been at the forefront of high-performance computing, and we’ve powered some of the fastest and most robust supercomputers in the world. Read more…

IBM Accelerated Insights

The Spark That Ignited A New World of Real-Time Analytics

High Performance Computing has always been about Big Data. It’s not uncommon for research datasets to contain millions of files and many terabytes, even petabytes of data, or more. Read more…

Why Nvidia Bought Mellanox: ‘Future Datacenters Will Be…Like High Performance Computers’

March 14, 2019

“Future datacenters of all kinds will be built like high performance computers,” said Nvidia CEO Jensen Huang during a phone briefing on Monday after Nvidia revealed scooping up the high performance networking company Mellanox for $6.9 billion. Read more…

By Tiffany Trader

It’s Official: Aurora on Track to Be First U.S. Exascale Computer in 2021

March 18, 2019

The U.S. Department of Energy along with Intel and Cray confirmed today that an Intel/Cray supercomputer, "Aurora," capable of sustained performance of one exaf Read more…

By Tiffany Trader

Why Nvidia Bought Mellanox: ‘Future Datacenters Will Be…Like High Performance Computers’

March 14, 2019

“Future datacenters of all kinds will be built like high performance computers,” said Nvidia CEO Jensen Huang during a phone briefing on Monday after Nvidia revealed scooping up the high performance networking company Mellanox for $6.9 billion. Read more…

By Tiffany Trader

Oil and Gas Supercloud Clears Out Remaining Knights Landing Inventory: All 38,000 Wafers

March 13, 2019

The McCloud HPC service being built by Australia’s DownUnder GeoSolutions (DUG) outside Houston is set to become the largest oil and gas cloud in the world th Read more…

By Tiffany Trader

Quick Take: Trump’s 2020 Budget Spares DoE-funded HPC but Slams NSF and NIH

March 12, 2019

U.S. President Donald Trump’s 2020 budget request, released yesterday, proposes deep cuts in many science programs but seems to spare HPC funding by the Depar Read more…

By John Russell

Nvidia Wins Mellanox Stakes for $6.9 Billion

March 11, 2019

The long-rumored acquisition of Mellanox came to fruition this morning with GPU chipmaker Nvidia’s announcement that it has purchased the high-performance net Read more…

By Doug Black

Optalysys Rolls Commercial Optical Processor

March 7, 2019

Optalysys, Ltd., a U.K. company seeking to advance it optical co-processor technology, moved a step closer this week with the unveiling of what it claims is th Read more…

By George Leopold

Intel Responds to White House AI Initiative

March 6, 2019

The Trump Administration’s release last month of the “American AI Initiative,” aimed at prioritizing federal R&D investments in machine intelligence, Read more…

By Doug Black

IBM Pitches Quantum Volume as Benchmarking Tool for Gate-based Quantum Computers

March 6, 2019

IBM this week announced it had achieved its highest Quantum Volume number to date at the American Physical Society (APS) March meeting being held in Boston. Wha Read more…

By John Russell

Quantum Computing Will Never Work

November 27, 2018

Amid the gush of money and enthusiastic predictions being thrown at quantum computing comes a proposed cold shower in the form of an essay by physicist Mikhail Read more…

By John Russell

The Case Against ‘The Case Against Quantum Computing’

January 9, 2019

It’s not easy to be a physicist. Richard Feynman (basically the Jimi Hendrix of physicists) once said: “The first principle is that you must not fool yourse Read more…

By Ben Criger

ClusterVision in Bankruptcy, Fate Uncertain

February 13, 2019

ClusterVision, European HPC specialists that have built and installed over 20 Top500-ranked systems in their nearly 17-year history, appear to be in the midst o Read more…

By Tiffany Trader

Intel Reportedly in $6B Bid for Mellanox

January 30, 2019

The latest rumors and reports around an acquisition of Mellanox focus on Intel, which has reportedly offered a $6 billion bid for the high performance interconn Read more…

By Doug Black

Looking for Light Reading? NSF-backed ‘Comic Books’ Tackle Quantum Computing

January 28, 2019

Still baffled by quantum computing? How about turning to comic books (graphic novels for the well-read among you) for some clarity and a little humor on QC. The Read more…

By John Russell

Why Nvidia Bought Mellanox: ‘Future Datacenters Will Be…Like High Performance Computers’

March 14, 2019

“Future datacenters of all kinds will be built like high performance computers,” said Nvidia CEO Jensen Huang during a phone briefing on Monday after Nvidia revealed scooping up the high performance networking company Mellanox for $6.9 billion. Read more…

By Tiffany Trader

Contract Signed for New Finnish Supercomputer

December 13, 2018

After the official contract signing yesterday, configuration details were made public for the new BullSequana system that the Finnish IT Center for Science (CSC Read more…

By Tiffany Trader

Deep500: ETH Researchers Introduce New Deep Learning Benchmark for HPC

February 5, 2019

ETH researchers have developed a new deep learning benchmarking environment – Deep500 – they say is “the first distributed and reproducible benchmarking s Read more…

By John Russell

Leading Solution Providers

SC 18 Virtual Booth Video Tour

Advania @ SC18 AMD @ SC18
ASRock Rack @ SC18
DDN Storage @ SC18
HPE @ SC18
IBM @ SC18
Lenovo @ SC18 Mellanox Technologies @ SC18
One Stop Systems @ SC18
Oracle @ SC18 Panasas @ SC18
Supermicro @ SC18 SUSE @ SC18 TYAN @ SC18
Verne Global @ SC18

IBM Quantum Update: Q System One Launch, New Collaborators, and QC Center Plans

January 10, 2019

IBM made three significant quantum computing announcements at CES this week. One was introduction of IBM Q System One; it’s really the integration of IBM’s Read more…

By John Russell

IBM Bets $2B Seeking 1000X AI Hardware Performance Boost

February 7, 2019

For now, AI systems are mostly machine learning-based and “narrow” – powerful as they are by today's standards, they're limited to performing a few, narro Read more…

By Doug Black

The Deep500 – Researchers Tackle an HPC Benchmark for Deep Learning

January 7, 2019

How do you know if an HPC system, particularly a larger-scale system, is well-suited for deep learning workloads? Today, that’s not an easy question to answer Read more…

By John Russell

HPC Reflections and (Mostly Hopeful) Predictions

December 19, 2018

So much ‘spaghetti’ gets tossed on walls by the technology community (vendors and researchers) to see what sticks that it is often difficult to peer through Read more…

By John Russell

Arm Unveils Neoverse N1 Platform with up to 128-Cores

February 20, 2019

Following on its Neoverse roadmap announcement last October, Arm today revealed its next-gen Neoverse microarchitecture with compute and throughput-optimized si Read more…

By Tiffany Trader

Move Over Lustre & Spectrum Scale – Here Comes BeeGFS?

November 26, 2018

Is BeeGFS – the parallel file system with European roots – on a path to compete with Lustre and Spectrum Scale worldwide in HPC environments? Frank Herold Read more…

By John Russell

France to Deploy AI-Focused Supercomputer: Jean Zay

January 22, 2019

HPE announced today that it won the contract to build a supercomputer that will drive France’s AI and HPC efforts. The computer will be part of GENCI, the Fre Read more…

By Tiffany Trader

Microsoft to Buy Mellanox?

December 20, 2018

Networking equipment powerhouse Mellanox could be an acquisition target by Microsoft, according to a published report in an Israeli financial publication. Microsoft has reportedly gone so far as to engage Goldman Sachs to handle negotiations with Mellanox. Read more…

By Doug Black

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This