Fujitsu and RIKEN Take First Place Worldwide in TOP500, HPCG, and HPL-AI with Supercomputer Fugaku

June 22, 2020

TOKYO, June 22, 2020 — Fujitsu announced that Fugaku(1), a supercomputer jointly developed by RIKEN and Fujitsu, was ranked No. 1 in the 58th TOP500 list of the world’s supercomputers. Fugaku also took the No.1 position in the international ranking HPCG (High Performance Conjugate Gradient), which measure the processing speed of the conjugate gradient method(2) often used in practical applications including in the field of industry, and in the ranking of HPL-AI, which measures the performance of low-precision computing often used in AI such as deep learning.

These rankings were announced on June 22 at the ongoing virtual event ISC (International Supercomputing Conference) High Performance 2020 Digital.

Supercomputer Fugaku (in development and preparation) credit: RIKEN

The achievement of No. 1 in these three rankings indicates the overall high performance of Fugaku and demonstrates that it can sufficiently respond to the needs of Society 5.0(3) which aims to build a smart society that creates new value. Fugaku can contribute in such society as an information infrastructure technology accelerating the solution of social problems with simulation while advancing the development of AI technologies as well as technologies related to information distribution and processing.

Measurement Results of Fugaku

1. TOP500 

The Fugaku system ranked first in the TOP500 list consisted of 396 racks (152,064 nodes(4), approximately 95.6% of the entire system), and the LINPACK performance was 415.53 PFLOPS (petaflops) with the computing efficiency ratio of 80.87%. It is the first time for a Japanese supercomputer to take the first place in TOP500 since the K computer claimed No.1 in November 2011 (the 38th TOP 500 list). Fugaku’s performance is approximately 2.8 times that of the supercomputer ranked second in the TOP500 list with148.6 PFLOPS.

2. HPCG 

For this benchmark, 360 racks (138,240 nodes, approximately 87% of the entire system) of Fugaku were used to achieve the high score of 13,400 TFLOPS (teraflops). This proves that the supercomputer can efficiently handle such real-world applications in the field of industry and perform well. Moreover, Fugaku exceeds the performance of the No.2 supercomputer (2,925.75 TFLOPS) by approximately 4.6 times.

3.  HPL-AI 

Unlike the conventional listings of TOP500 and HPCG which measure the performance of double-precision arithmetic logic unit, HPL-AI is a new benchmark established in November 2019 as an index for evaluating calculation performance that takes into account the capabilities of single-precision and half-precision arithmetic logic units used in artificial intelligence. For this measurement, a high score of 1.421 EFLOPS (Exa FLOPS) was recorded using 330 racks (126,720 nodes, approximately 79.7% of the entire system) of Fugaku.

This is also a historical record, as Fugaku achieved 1 exa (10 raised to the power of 18) in one of HPL benchmarks for the first time in the world. This proves Fugaku’s capability to contribute to the advancement of Society 5.0, as a research platform for machine learning and big data analysis.

About the supercomputer benchmarks

1. TOP500 

The TOP500 list is a project that regularly ranks and evaluates the top 500 fastest supercomputer systems in the world based on LINPACK performance. Developed by Dr. Jack Dongarra of the University of Tennessee, US, to solve a system of linear equations by matrix calculation, the LINPACK program was launched in 1993 to announce the supercomputer ranking two times a year (June and November).

LINPACK measures the computing power of double-precision floating-point numbers used in many scientific and industrial applications and to get a high score on this benchmark, it is necessary to run a large-scale benchmark for a long time. In general, a high LINPACK score is said to be a comprehensive measure of computing power and reliability.

2. HPCG 

The TOP500 has long been a popular benchmark for evaluating computing power, which was an important performance indicator for solving a system of linear equations composed of a dense coefficient matrix. More than 20 years have passed since the project was launched in 1993, and recently it has been pointed out that the performance requirements of actual applications are not met, and the time required for benchmark testing is prolonged.

Accordingly, Dr. Dongarra et al. proposed a new benchmark program, HPCG, that uses the conjugate gradient method to solve a system of linear equations composed of a sparse coefficient matrix, which are often used in industrial applications. Following the announcement of measurements on the world’s leading 15 supercomputer systems at ISC 2014 in June, the official ranking was announced at the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC14) held in New Orleans, US, in November.

3.  HPL-AI 

The TOP500 and HPCG have ranked supercomputers in terms of computational performance for solving a system of linear equations. In both cases, it was stipulated in the rules that only double precision arithmetic (16-digit floating point number in 10), which has been widely used in scientific and technological calculations as well as industrial applications, should be used for calculations.

In recent years, more computers, equipped with GPUs or AI dedicated chips, are adding a large number of low-precision arithmetic logic units (5 or 10 digits in 10) to increase their performance. Since these high-performance computing capabilities are not reflected in the TOP500 list, Dr. Dongarra et al. improved the LINPACK benchmark by allowing the use of low precision calculations and proposed a new benchmark, HPL-AI, in November 2019.

HPL-AI allows LINPACK to perform low-precision computations when solving a system of linear equations using LU decomposition(5). However, since the calculation accuracy is inferior to that of double precision calculation, it is required to obtain the same accuracy as double precision calculation by a technique called iterative refinement(6). In other words, it’s a two-step benchmark. As the HPL-AI rules were issued in November 2019, this is the first announcement of the benchmark ranking.

Satoshi Matsuoka, Director, Riken-Center for Computational Science (R-CCS), said “Ten years after the initial concept was proposed, and six years after the official start of the project, Fugaku is now near completion. Fugaku was developed based on the idea of achieving high performance on a variety of applications of great public interest, such as the achievement of Society 5.0, and we are very happy that it has shown itself to be outstanding on all the major supercomputer benchmarks. In addition to its use as a supercomputer, I hope that the leading-edge IT technology developed for it will contribute to major advances on difficult social challenges such as COVID-19.”

Naoki Shinjo, Corporate Executive Officer, Fujitsu Limited, said, “I believe that our decision to use a co-design process for Fugaku, which involved working with RIKEN and other parties to create the system, was a key to our winning the top position on a number of rankings. I am particularly proud that we were able to do this just one month after the delivery of the system was finished, even during the COVID-19 crisis. I would like to express our sincere gratitude to RIKEN and all the other parties for their generous cooperation and support. I very much hope that Fugaku will show itself to be highly effective in real-world applications and will help to make Society 5.0 a reality.”

Rene Haas, President, IPG, Arm, said “The Fugaku supercomputer illustrates a dramatic shift in the type of compute that has been traditionally used in these powerful machines, and it is proof of the innovation that can happen with flexible computing solutions driven by a strong ecosystem. For Arm, this achievement showcases the power efficiency, performance and scalability of our compute platform, which spans from smartphones to the world’s fastest supercomputer. We congratulate RIKEN and Fujitsu for challenging the status quo and showing the world what is possible in Arm-based high-performance computing.”

  • [1] Supercomputer Fugaku

Succeeding supercomputer K, Fugaku aims to contribute to Japan’s growth and produce world-leading results by solving social and scientific issues in the 2020s. Under the flagship 2020 project of the Ministry of Education, Culture, Sports, Science and Technology (the development of post-K) initiated in fiscal 2014, RIKEN Computational Science and Research Center has developed Fugaku which is planned to start public service in fiscal 2021 (starting in April 2021).

  • [2] Conjugate gradient method

When a physical phenomenon is simulated by a computer, it is often solved as a large-scale system of linear equations. There are two methods for solving a system of linear equations: a direct method for directly obtaining a solution, and an iterative method for converging to a correct solution through iterative calculation. The conjugate gradient method is one of the iterative methods, and by combining the preprocessing, the correct solution can be converged quickly. It is often used in the world of computer simulation.

  • [3] Society 5.0

As first introduced in the Fifth Science and Technology Basic Plan, this is a proposal for “a human-centered society that achieves both economic development and the resolution of social issues through a highly integrated system of cyberspace (virtual space) and physical space (real space),” indicating the future society that Japan should aim for.

  • [4] Nodes

 The smallest unit of supercomputer’s computing resource that an operating system can run. One node of Fugaku consists of 1 CPU (central processing unit) and 32 GiB of memory.

  • [5] LU decomposition

A method of solving a system of linear equations. This method is called the LU decomposition method because the matrix is decomposed into the product of the lower triangular matrix (Lower-triangular matrix) and the upper triangular matrix (Upper Triangular Matrix) in the middle of the solution.

  • [6] Iterative improvement

An approximate solution of a system of linear equations by methods such as LU decomposition contains errors with the true solution. This is a method of solving a system of linear equations (using the errors) again and obtaining a solution closer to the true solution by correcting the approximate solution.

About Fujitsu

Fujitsu is the leading Japanese information and communication technology (ICT) company offering a full range of technology products, solutions and services. Approximately 130,000 Fujitsu people support customers in more than 100 countries. We use our experience and the power of ICT to shape the future of society with our customers. Fujitsu Limited (TSE:6702) reported consolidated revenues of 3.9 trillion yen (US$35 billion) for the fiscal year ended March 31, 2020. For more information, please see www.fujitsu.com.


Source: Fujitsu

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!

Intel’s Silicon Brain System a Blueprint for Future AI Computing Architectures

April 24, 2024

Intel is releasing a whole arsenal of AI chips and systems hoping something will stick in the market. Its latest entry is a neuromorphic system called Hala Point. The system includes Intel's research chip called Loihi 2, Read more…

Anders Dam Jensen on HPC Sovereignty, Sustainability, and JU Progress

April 23, 2024

The recent 2024 EuroHPC Summit meeting took place in Antwerp, with attendance substantially up since 2023 to 750 participants. HPCwire asked Intersect360 Research senior analyst Steve Conway, who closely tracks HPC, AI, Read more…

AI Saves the Planet this Earth Day

April 22, 2024

Earth Day was originally conceived as a day of reflection. Our planet’s life-sustaining properties are unlike any other celestial body that we’ve observed, and this day of contemplation is meant to provide all of us Read more…

Intel Announces Hala Point – World’s Largest Neuromorphic System for Sustainable AI

April 22, 2024

As we find ourselves on the brink of a technological revolution, the need for efficient and sustainable computing solutions has never been more critical.  A computer system that can mimic the way humans process and s Read more…

Empowering High-Performance Computing for Artificial Intelligence

April 19, 2024

Artificial intelligence (AI) presents some of the most challenging demands in information technology, especially concerning computing power and data movement. As a result of these challenges, high-performance computing Read more…

Kathy Yelick on Post-Exascale Challenges

April 18, 2024

With the exascale era underway, the HPC community is already turning its attention to zettascale computing, the next of the 1,000-fold performance leaps that have occurred about once a decade. With this in mind, the ISC Read more…

Intel’s Silicon Brain System a Blueprint for Future AI Computing Architectures

April 24, 2024

Intel is releasing a whole arsenal of AI chips and systems hoping something will stick in the market. Its latest entry is a neuromorphic system called Hala Poin Read more…

Anders Dam Jensen on HPC Sovereignty, Sustainability, and JU Progress

April 23, 2024

The recent 2024 EuroHPC Summit meeting took place in Antwerp, with attendance substantially up since 2023 to 750 participants. HPCwire asked Intersect360 Resear Read more…

AI Saves the Planet this Earth Day

April 22, 2024

Earth Day was originally conceived as a day of reflection. Our planet’s life-sustaining properties are unlike any other celestial body that we’ve observed, Read more…

Kathy Yelick on Post-Exascale Challenges

April 18, 2024

With the exascale era underway, the HPC community is already turning its attention to zettascale computing, the next of the 1,000-fold performance leaps that ha Read more…

Software Specialist Horizon Quantum to Build First-of-a-Kind Hardware Testbed

April 18, 2024

Horizon Quantum Computing, a Singapore-based quantum software start-up, announced today it would build its own testbed of quantum computers, starting with use o 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…

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…

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…

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…

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…

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…

The GenAI Datacenter Squeeze Is Here

February 1, 2024

The immediate effect of the GenAI GPU Squeeze was to reduce availability, either direct purchase or cloud access, increase cost, and push demand through the roof. A secondary issue has been developing over the last several years. Even though your organization secured several racks... 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