Japan’s Fugaku Tops Global Supercomputing Rankings

By Tiffany Trader

June 22, 2020

A new Top500 champ was unveiled today. Supercomputer Fugaku, the pride of Japan and the namesake of Mount Fuji, vaulted to the top of the 55th edition of the Top500 list with 415.5 Linpack petaflops, marking a win for system builder Fujitsu, for Arm-based supercomputing and for the fight against the COVID-19 pandemic in which Fugaku is already engaged. In reduced precision, measured via the new HPL-AI benchmark, Fugaku achieved a record 1.4 exaflops. The Fujitsu Arm system is installed at RIKEN Center for Computational Science (R-CCS) in Kobe, Japan.

A decade in the making, Fugaku was developed by RIKEN in close collaboration with Fujitsu and the application community with funding from MEXT. At the centerpiece is a new processor, Fujitsu’s 48-core Arm A64FX SoC. Riken’s Top500 run was performed with 396 racks, comprising 152,064 A64FX nodes, which is approximately 95.6 percent of the entire (158,976-node) system. With nearly 7.3 million Arm cores running at 2.2GHz, Fugaku achieved a double-precision Linpack performance of 415.53 petaflops out of 513.98 theoretical petaflops, delivering a computing efficiency of 80.87 percent.

An in-depth report from Top500 co-author Jack Dongarra provides these technical details:

The Fugaku system is built on the A64FX ARM v8.2-A, which uses Scalable Vector Extension (SVE) instructions and a 512-bit implementation. The Fugaku system adds the following Fujitsu extensions: hardware barrier, sector cache, prefetch, and the 48/52 core CPU. It is optimized for high-performance computing (HPC) with an extremely high bandwidth 3D stacked memory, 4x 8 GB HBM with 1024 GB/s, on-die Tofu-D network BW (~400 Gbps), high SVE FLOP/s (3.072 TFLOP/s), and various AI support (FP16, INT8, etc.). The A64FX processor provides for general purpose Linux, Windows, and other cloud systems. 

Fugaku provides 4.85 petabytes of total memory with an aggregate 163 petabytes-per-second of memory bandwidth. The Tofu-D 6D Torus network delivers 6.49 petabytes-per-second injection bandwidth. The storage system consists of three layers: 15.9 petabytes of NVMe, a Lustre-based global file system, and cloud storage services that are in preparation. The installation occupies 1,920 square meters of floor space (equivalent to four basketball courts) and operates within a 30MW power envelope.

“We have a brand new processor,” said Fugaku project lead Satoshi Matsuoka, director of R-CCS, in today’s live-streamed Top500 briefing, hosted as part of the ISC 2020 Digital proceedings. “It’s an Arm instruction set, but is a brand new design by Fujitsu and RIKEN. [As a general-purpose CPU], it runs the same Arm code as a smartphone, it will run Red Hat Linux out of the box, [and] it will run Windows. It will run PowerPoint, even, but it’s also built to accommodate very large bandwidth, which is very important to sustain the speed up of the applications.”

Fugaku versus second-place finishers on Top500, HPCG, HPL-AI and Graph500 benchmarking (right-hand column shows speedup)

“You can think of Fugaku as putting 20 million smartphones in a single room, or equivalently 300,000 standard servers in a single room,” said Matsuoka, highlighting the scale of the system. “And these by coincidence, are about the same number as the annual shipment of respective units in Japan. So if you have two Fugakus basically, you can pretty much fill the so called edge-to-cloud compute requirements for the entire country of Japan.”

The cost to build Fugaku was about one billion dollars, on par with what is projected for the U.S. exascale machines. The total includes “significant R&D cost & the DC upgrade cost,” Matsuoka indicated in a Tweet, adding “it would have cost 3 times as much if we had used off-the-shelf CPUs.”

Fugaku demonstrated more than 2.8 times the performance of the previous list leader Summit (ORNL), benchmarked at 148.6 petaflops (and now in second place). The last time Japan clinched the top spot of the list was in November 2011, with the launch of the K computer, which held its position for six months before being supplanted by Sequoia, an IBM BlueGene/Q system installed at the National Nuclear Security Administration.

June 2020 top 100 research systems by chip architecture – aggregate performance share (source: Top500)

Fugaku contributes 18.7 percent of aggregate list flops, setting a new record. The machine’s magnitude shakes up the list dynamics, boosting Fujitsu into first place by performance share, and raising Japan into third place by performance share (behind the U.S., which still leads, and China). Segmenting the list by top 100 research systems, Japan zooms into first place (with 36 percent), and the Arm architecture, which only entered the list a year-and-a-half ago, now dominates with a 31 percent performance share.

There are just three other Arm systems on the Top500: the A64FX Fugaku prototype at Fujitsu (#205); the new Fujitsu PRIMEHPC FX1000 A64FX system, Flow, at Japan’s Nagoya University (#37); and Astra, the Marvell/Cavium ThunderX2 installation at Sandia (#245), recognized as the world’s first petascale Arm system in November 2018.

Performance fraction of Top500 systems (source: Top500)

Fugaku also broke records on the HPCG (13.4 petaflops), the Graph500 (70,980 gigaTEPS) and the HPL-AI (1.42 exaflops), coming in first in all three. Remarking on the system’s placement on the new AI-geared HPL-AI benchmark, Top500 co-author Erich Strohmaier observed, “That’s non trivial, because to satisfy the requirements of the benchmark, you cannot just compute only in 16-bit, you actually have to make up for the lost precision at the end of the benchmark to get back to the full 64-bit precision in the results. But that penalty was easily overcome by the more than two exaflops of peak performance Fugaku has in 16-bit operations.”

Fugaku is also one of the most energy-efficient machines on the Top500, joining its “mini-me” A64FX prototype, in the top ten of the Green500. With its 28.33 MW Linpack run, Fugaku delivered 14.7 gigaflops-per-watt, earning it a ninth-place finish on the Green500 list. The smaller A64FX prototype (#205 on the Top500), installed at Fujitsu’s Numazu plant, holds the fourth spot on the Green500 with 16.87 gigaflops-per-watt. Green500 glory goes to newcomer Preferred Networks, which achieved 21.1 gigaflops-per-watt, with its MN-3 system (#394 on the Top500) that combines Intel Xeon and specialized AI processors.

The two Arm systems — Fugaku and its prototype — are notable as the only systems in the top 20 of the Green500 that do not make use of GPUs or specialized accelerators. “Our power efficiency is pretty much in the range of GPUs or the latest specialized accelerators while being a general purpose CPU,” said Matsuoka, adding that the Fugaku processor is three times more powerful and also three times more power efficient [for Riken’s target workloads] compared to traditional CPUs, on account of extensive tuning.

June 2020 top 100 research systems by country – aggregate performance share (source: Top500)

Matsuoka reported that Fugaku was put into production almost a year ahead of schedule to combat COVID-19 (see additional HPCwire coverage here). For medical pharma applications that assess the effectiveness of drug targets, Fugaku is showing 100X speedups over K, according to Matsuoka. Efforts are also being directed to  societal and epidemiological applications to simulate how infections spread and the effectiveness of contact tracing. “The latter has tremendous potential and is already helping to mitigate the virus infections at macroscale,” Matsuoka added.

Asked about potential plans to grow Fugaku across the 64-bit precision exascale threshold, Matsuoka responded wryly, “If we have the money, obviously, anything is possible.” But he emphasized the goal of the project was never about peak performance.

“Our design metric was basically to accelerate existing applications by two orders of magnitude,” he said. “In some sense, the excellence is in the variety of the benchmarks, not just the Top500, but across the board, HPCG, HPL-AI, Top500, and so forth — showing basically the result of our efforts to accelerate the applications. So the outcome is applications describing the benchmarks and not the other way around. So, we’re very satisfied with the result. If we make progress it’ll only be because we will have made progress in the application speedup by which we could be achieving exaflop.”

Matsuoka added that the software ecosystem was the priority in the development of Fugaku. “That’s why we went to the Arm ecosystem from Spark, which was the K’s ecosystem and was not very, let’s say, proliferating,” he said. “The decision to go with Arm has led to a variety of collaborations with various institutions worldwide, with the DOE, with the European institutions, and so forth. Software is the key. That’s the heart of the computing system, and we’re making every effort to enrich the Arm ecosystem so that it’ll be one of the dominant systems in the HPC community.”

Feature image courtesy Riken.

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!

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

March 18, 2024

Nvidia's latest and fastest GPU, code-named Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from its predecessors, including the red-hot H100 and A100 GPUs. 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. While Nvidia may not spring to mind when thinking of the quant Read more…

2024 Winter Classic: Meet the HPE Mentors

March 18, 2024

The latest installment of the 2024 Winter Classic Studio Update Show features our interview with the HPE mentor team who introduced our student teams to the joys (and potential sorrows) of the HPL (LINPACK) and accompany 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 field was normalized for boys in 1969 when the Apollo 11 missi Read more…

Apple Buys DarwinAI Deepening its AI Push According to Report

March 14, 2024

Apple has purchased Canadian AI startup DarwinAI according to a Bloomberg report today. Apparently the deal was done early this year but still hasn’t been publicly announced according to the report. Apple is preparing Read more…

Survey of Rapid Training Methods for Neural Networks

March 14, 2024

Artificial neural networks are computing systems with interconnected layers that process and learn from data. During training, neural networks utilize optimization algorithms to iteratively refine their parameters until Read more…

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

March 18, 2024

Nvidia's latest and fastest GPU, code-named 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…

Survey of Rapid Training Methods for Neural Networks

March 14, 2024

Artificial neural networks are computing systems with interconnected layers that process and learn from data. During training, neural networks utilize optimizat Read more…

PASQAL Issues Roadmap to 10,000 Qubits in 2026 and Fault Tolerance in 2028

March 13, 2024

Paris-based PASQAL, a developer of neutral atom-based quantum computers, yesterday issued a roadmap for delivering systems with 10,000 physical qubits in 2026 a Read more…

India Is an AI Powerhouse Waiting to Happen, but Challenges Await

March 12, 2024

The Indian government is pushing full speed ahead to make the country an attractive technology base, especially in the hot fields of AI and semiconductors, but Read more…

Charles Tahan Exits National Quantum Coordination Office

March 12, 2024

(March 1, 2024) My first official day at the White House Office of Science and Technology Policy (OSTP) was June 15, 2020, during the depths of the COVID-19 loc Read more…

AI Bias In the Spotlight On International Women’s Day

March 11, 2024

What impact does AI bias have on women and girls? What can people do to increase female participation in the AI field? These are some of the questions the tech 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…

Analyst Panel Says Take the Quantum Computing Plunge Now…

November 27, 2023

Should you start exploring quantum computing? Yes, said a panel of analysts convened at Tabor Communications HPC and AI on Wall Street conference earlier this y 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…

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…

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…

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

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…

Training of 1-Trillion Parameter Scientific AI Begins

November 13, 2023

A US national lab has started training a massive AI brain that could ultimately become the must-have computing resource for scientific researchers. Argonne N 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…

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…

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…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire