Welcome to the Post-Petaflop Era

By Michael Feldman

June 10, 2008

This week’s achievement of the Linpack petaflop milestone by the IBM Roadrunner was widely predicted, but nonetheless, impressive. Last year at this time, the number one system was Lawrence Livemore’s Blue Gene/L at 280 teraflops, and only two other systems — the Cray XT4/XT3 supercomputer at Oak Ridge and the Cray Red Storm system at Sandia — made it past 100 teraflops. In fact, the raw computation power of the Roadrunner exceeds the aggregate performance of the top 10 system in June 2007.

The nearly insatiable demand for supercomputing power has driven a remarkable increase in HPC capability over the last decade and a half. During this time the computational performance of the top systems have increased at a rate of 1000x for every 10 years. As I mentioned in Monday’s Roadrunner coverage, that pace of increase is an order of magnitude greater than that reflected by Moore’s Law. Today, Moore’s Law is contributing relatively little to processor speed increases; it’s being used to add more cores. But even if the chip real estate dedicated to cores scales proportionally as transistors shrink, (which is probably not the case since the memory bandwidth bottleneck encourages larger on-chip caches), that would only yield about a 100x increase in raw performance every 10 years.

Which explains why clusters and supercomputers are scaling both up (more processors and cores) and out (more nodes). But, even ignoring the software challenges of distributing applications over more and more CPUs, just jamming additional commodity processors into a system runs up against physical constraints like power and space, not to mention system cost. It is significant that the first petaflop system was not an x86 cluster.

All of this explains the HPC community’s current obsession with hardware accelerators — FPGA, GPU, Cell, ClearSpeed and vector processors. While not general-purpose in nature, these accelerators offer a lot of computational power in a small, cheap, and energy-efficient package.

In the Roadrunner, each AMD Opteron core is paired with a PowerXCell 8i (Cell) processor, which acts as a high-performance floating point accelerator. But the 12,240 Cell processors can barely be characterized as accelerators since they account for the vast majority of the system’s performance. The 6,120 dual-core Opterons contribute only around 3 percent to the total performance. The PowerXCell 8i offers over 100 double precision gigaflops for a modest 92 watts, which is about an order of magnitude better performance and performance/watt than the dual-core Opterons in Roadrunner. So minimizing the Opteron parts was the key to maximizing FLOPS.

But there are other ways to get to a petaflop. In fact, it’s not immediately apparent to me why the DOE, who bought the Roadrunner system for Los Alamos and the NNSA, didn’t go the Blue Gene/P route. The latter machine represents IBM’s other petaflop-capable system, which was introduced a year ago. A handful are in the field, but no one has purchased a petaflop-sized system to date.

The price tag for a petaflop Blue Gene/P would probably be just north of $100 million, in the same general vicinity as the $120 million that the DOE paid for Roadrunner. And the DOE certainly has plenty of experience with Blue Gene technology, so no red flags there. Finally, compared to Roadrunner, Blue Gene comes with a simpler and more mature software environment.

From the application point of view, the biggest difference between the two architectures is that Blue Gene needs more than twice as many processing cores to get to a petaflop than Roadrunner — about 300K cores for Blue Gene/P versus 120K for Roadrunner (each Cell processor has 9 cores). That means your application needs to be divided into more pieces to run on the Blue Gene than on the more computationally dense Roadrunner. More parallelism might be fine for some apps, but not for others.

Energy efficiencies of the two architectures are comparable. At 376 megawatts/watt, Roadrunner is tops in this regard. But Blue Gene/P comes in at a very respectable 350 megaflops/watt. The energy efficiency of Blue Gene is the result of using low-power ASICs, based on the PowerPC, a type of processor that is more at home in embedded systems.

In general, processors for embedded application are designed for low power rather than speed, but they offer HPC vendors an alternative way to build large-scale energy-efficient systems. SiCortex, for example, is using MIPS processors to create a low-power line HPC clusters.

But as systems get into the tens of petaflops range, even commodity embedded chips won’t be practical. Researchers at LBNL estimate that a Blue Gene-like system capable of running an application at 10 petaflops of sustained performance will cost over a billion dollars and require tens of megawatts to operate, even taking into account future price/performance advances. The Berkeley researchers are looking at using ultra-low-power custom processors to make these kinds of systems practical.

As energy costs and hardware costs really start to limit the kind of machines vendors can offer in a post-petaflop world, commodity processors may yield to either accelerators or low-power, homogeneous processors. Over the next ten years, a battle between these two approaches may take place on the path from petaflops to exaflops. But this week, the accelerators won the first round.

Subscribe to HPCwire's Weekly Update!

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

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

Nvidia Appoints Andy Grant as EMEA Director of Supercomputing, Higher Education, and AI

March 22, 2024

Nvidia recently appointed Andy Grant as Director, Supercomputing, Higher Education, and AI for Europe, the Middle East, and Africa (EMEA). With over 25 years of high-performance computing (HPC) experience, Grant brings a Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the fi Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire