IBM Enters Petascale Era with Blue Gene/P

By Michael Feldman

June 29, 2007

This week at the International Supercomputing Conference (ISC) in Dresden, Germany, IBM unveiled its next-generation Blue Gene architecture — Blue Gene/P. The new model is intended for users looking for petaflop-level computing and beyond. Like its Blue Gene/L predecessor, Blue Gene/P is targeted for big science applications and the very highest end of commercial HPC. According to IBM, Blue Gene/P is two and a half times more powerful than the Blue Gene/L generation and requires only slightly more power. A relatively modest two-rack Blue Gene/P configuration that IBM deployed in-house ended up as number 31 on the new Top500 list announced this week.

The previous-generation Blue Gene/L machines represent some of the fastest systems in the world. The Lawrence Livermore system currently holds the top spot on the Top500 list and a number of other Blue Gene/L installations are scattered throughout the list. But this is the end of the line for Blue Gene/L. IBM manufacturing will now switch over to the P line. Blue Gene/L purchases currently in the pipeline represent the last machines of the first generation.

The Blue Gene/P architecture is based on a quad-core PowerPC 450 ASIC chip, where each chip is capable of 13.6 gigaflops. A compute node includes the ASIC chip along with 2 GB of SDRAM DDR2 memory. Thirty-two of these nodes are aggregated onto a board and 32 of the boards are placed in a 6-foot high rack. The result is a 4096-core rack, which provides 13.9 teraflops (peak) of processing power. This represents the smallest Blue Gene/P system you can buy.

Although the original Blue Gene/L was dual-core, it did not implement cache coherency in the hardware. By contrast, Blue Gene/P is designed around cache coherent quad-core chips, so they can be treated as SMP nodes in the same manner as any multicore-based commodity cluster. This makes the new Blue Gene more suitable for multithreaded workloads based on standard software technologies like OpenMP.

Compared to Blue Gene/L, the new generation uses slightly faster PowerPC processors (850 MHz versus 700 MHz) and twice as many cores per chip (4 versus 2). L3 cache has been doubled from 4 MB to 8 MB and main memory per compute node has been quadrupled from 512 MB to 2 GB. Main memory bandwidth has also increased — from 5.6 to 13.6 GB/sec. In addition, the 3-D Torus and Tree networks have been upgraded, essentially more than doubling the bandwidth and cutting latencies in half. The increased capabilities provide a 2.4x increase in performance over Blue Gene/L, using roughly the same floor space and slightly more power.

A single 13.9-teraflop Blue Gene/P rack draws just 40 kilowatts, yielding 0.35 gigaflops/watt — possibly the best performance/watt metric of any general-purpose computing system on the planet. SiCortex’s MIPS-based cluster systems come close at around 0.32 gigaflops/watt. For comparison, Blue Gene/L offers a lower, but very respectable, 0.23 gigaflops/watt. Most x86-based high performance computing systems are an order of magnitude lower than that. As users build Blue Gene/P systems that scale to hundreds of teraflops and beyond, power efficiencies become even more critical.

And while not every customer will use Blue Gene/P to build petaflop systems, IBM anticipates at least one customer will put enough Blue Gene/P racks together to reach a sustained (Linpack) petaflop as early as next year. Apparently IBM has a few prospects that are considering purchasing the 80 or so Blue Gene/P racks required to build a such a machine. The architecture is actually designed to scale up 256 racks, which would come close to three Linpack petaflops. However, there are few customers who would know what to do with such power, and the cost would probably be prohibitive even for that select group. IBM realizes that, although there are many HPC customers with computational problems bigger than their machines, there are only so many organizations that have the right combination of money, workload, and software experience that’s required to take advantage of machines like Blue Gene/P.

In any case, the Blue Gene/P sales pipeline is already filling up. The U.S. Dept. of Energy’s Argonne National Laboratory, Argonne, Ill., will deploy the first Blue Gene/P system this fall. Argonne’s initial Blue Gene/P system will be a 114-teraflop machine, and the lab is on track to eventually install about half a petaflop. Argonne currently has a Blue Gene/L system and will continue to operate that machine through at least 2008.

Explaining the lab’s motivation to increase their Blue Gene investment, Ray Bair, Division Director for the Argonne Leadership Class Facility said: “Blue Gene has been a resounding success for scientific computing since its inception, both for DOE’s INCITE program at Argonne National Laboratory and in diverse science programs at institutions around the world. The breadth and scale of science problems that can be addressed with Blue Gene was another important factor. IBM designed Blue Gene/P with petascale scientific computing in mind, making performance and functionality improvements from top to bottom while preserving Blue Gene’s extraordinary balance.”

Other installations are being planned as well. In Germany, the Max Planck Society and Forschungszentrum Jülich are scheduled to begin installing Blue Gene/P systems in late 2007. Other Blue Gene/P deployments are being planned by Stony Brook University and Brookhaven National Laboratory in Upton, N.Y., and the Science and Technology Facilities Council, Daresbury Laboratory in Cheshire, England.

Since the public sector is the principal source of the money for capability-class supercomputers, Blue Gene systems tend to live almost exclusively in government labs and facilities. IBM has courted Wall Street, but has not closed any Blue Gene accounts there. With the increased emphasis on power and cooling costs, IBM is hoping that a tipping point will occur at some point and commercial entities will consider Blue Gene supercomputers as cost-effective alternatives to large HPC cluster systems.

What IBM is really counting on is that a good proportion of the installed Blue Gene/L base will upgrade to Blue Gene/P. Applications should port rather easily, requiring only a recompilation. Unfortunately, the two architectures won’t interoperate; a Blue Gene/P system won’t be able to bolt onto a Blue Gene/L rack to accelerate the original system. IBM does, however, offer a trade-in program for customers looking to retire their older models and get some credit against a Blue Gene/P purchase.

Because of the scale of the architecture and the extended lifetimes of these types of supercomputers, IBM put a lot of thought into reliability and system robustness. Efficient cooling design, soldered memory, and a low number of moving parts supports a low mean time between failure (MTBF) rate. From the feedback they received from Lawrence Livermore National Laboratory, it turned out that the lab’s Blue Gene/L system had an order of magnitude better MTBF than commodity-based systems installed there. When added to the cost savings realized from the system’s power efficiency, IBM offers a fairly compelling TCO story.

This message is tougher to sell in the commercial HPC space, where customers are still very sensitive to initial acquisition costs. According to Herb Schultz, Deep Computing Marketing Manager, IBM is aiming for around ten cents per megaflop for the new Blue Gene systems, which he feels is price competitive with other non-discounted HPC systems in the industry. But with the smallest installation being a 14-teraflop system, customers are looking at $1.4 million to join the Blue Gene/P club.

“If we can get customers to look more broadly at the overall system costs — the power and cooling bill over three or four years and the costs associated with system downtime — we think Blue Gene/P looks really good,” said Schultz. “So we’re trying to get people to look beyond the initial acquisition cost and focus on the total cost of operating the machine over its lifetime.”

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!

2024 Winter Classic: Meet Team Morehouse

April 17, 2024

Morehouse College? The university is well-known for their long list of illustrious graduates, the rigor of their academics, and the quality of the instruction. They were one of the first schools to sign up for the Winter Read more…

MLCommons Launches New AI Safety Benchmark Initiative

April 16, 2024

MLCommons, organizer of the popular MLPerf benchmarking exercises (training and inference), is starting a new effort to benchmark AI Safety, one of the most pressing needs and hurdles to widespread AI adoption. The sudde Read more…

Quantinuum Reports 99.9% 2-Qubit Gate Fidelity, Caps Eventful 2 Months

April 16, 2024

March and April have been good months for Quantinuum, which today released a blog announcing the ion trap quantum computer specialist has achieved a 99.9% (three nines) two-qubit gate fidelity on its H1 system. The lates Read more…

Mystery Solved: Intel’s Former HPC Chief Now Running Software Engineering Group 

April 15, 2024

Last year, Jeff McVeigh, Intel's readily available leader of the high-performance computing group, suddenly went silent, with no interviews granted or appearances at press conferences.  It led to questions -- what's Read more…

Exciting Updates From Stanford HAI’s Seventh Annual AI Index Report

April 15, 2024

As the AI revolution marches on, it is vital to continually reassess how this technology is reshaping our world. To that end, researchers at Stanford’s Institute for Human-Centered AI (HAI) put out a yearly report to t Read more…

Crossing the Quantum Threshold: The Path to 10,000 Qubits

April 15, 2024

Editor’s Note: Why do qubit count and quality matter? What’s the difference between physical qubits and logical qubits? Quantum computer vendors toss these terms and numbers around as indicators of the strengths of t Read more…

MLCommons Launches New AI Safety Benchmark Initiative

April 16, 2024

MLCommons, organizer of the popular MLPerf benchmarking exercises (training and inference), is starting a new effort to benchmark AI Safety, one of the most pre Read more…

Exciting Updates From Stanford HAI’s Seventh Annual AI Index Report

April 15, 2024

As the AI revolution marches on, it is vital to continually reassess how this technology is reshaping our world. To that end, researchers at Stanford’s Instit Read more…

Intel’s Vision Advantage: Chips Are Available Off-the-Shelf

April 11, 2024

The chip market is facing a crisis: chip development is now concentrated in the hands of the few. A confluence of events this week reminded us how few chips Read more…

The VC View: Quantonation’s Deep Dive into Funding Quantum Start-ups

April 11, 2024

Yesterday Quantonation — which promotes itself as a one-of-a-kind venture capital (VC) company specializing in quantum science and deep physics  — announce Read more…

Nvidia’s GTC Is the New Intel IDF

April 9, 2024

After many years, Nvidia's GPU Technology Conference (GTC) was back in person and has become the conference for those who care about semiconductors and AI. I Read more…

Google Announces Homegrown ARM-based CPUs 

April 9, 2024

Google sprang a surprise at the ongoing Google Next Cloud conference by introducing its own ARM-based CPU called Axion, which will be offered to customers in it Read more…

Computational Chemistry Needs To Be Sustainable, Too

April 8, 2024

A diverse group of computational chemists is encouraging the research community to embrace a sustainable software ecosystem. That's the message behind a recent Read more…

Hyperion Research: Eleven HPC Predictions for 2024

April 4, 2024

HPCwire is happy to announce a new series with Hyperion Research  - a fact-based market research firm focusing on the HPC market. In addition to providing mark 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…

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…

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…

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…

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…

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…

Leading Solution Providers

Contributors

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…

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…

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…

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…

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…

Eyes on the Quantum Prize – D-Wave Says its Time is Now

January 30, 2024

Early quantum computing pioneer D-Wave again asserted – that at least for D-Wave – the commercial quantum era has begun. Speaking at its first in-person Ana Read more…

GenAI Having Major Impact on Data Culture, Survey Says

February 21, 2024

While 2023 was the year of GenAI, the adoption rates for GenAI did not match expectations. Most organizations are continuing to invest in GenAI but are yet to Read more…

Intel’s Xeon General Manager Talks about Server Chips 

January 2, 2024

Intel is talking data-center growth and is done digging graves for its dead enterprise products, including GPUs, storage, and networking products, which fell to Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire