{"id":2885,"date":"2022-09-02T10:28:57","date_gmt":"2022-09-02T03:28:57","guid":{"rendered":"http:\/\/international.binus.ac.id\/computer-science\/?p=2885"},"modified":"2022-09-02T10:28:57","modified_gmt":"2022-09-02T03:28:57","slug":"13-fastest-supercomputers-in-the-world-in-2022","status":"publish","type":"post","link":"https:\/\/international.binus.ac.id\/computer-science\/2022\/09\/02\/13-fastest-supercomputers-in-the-world-in-2022\/","title":{"rendered":"13 Fastest Supercomputers In The World | In 2022"},"content":{"rendered":"

For most of us, a computer probably seems fast enough if it can run 8K videos or the latest version of Far Cry in 60 fps without slowing down. However, there are many complicated tasks that require billions of calculations per second \u2013 something a desktop with i9 processor can\u2019t do.<\/p>\n

That\u2019s where supercomputers come in handy. They offer a high level of performance that allows governments and organizations to solve problems that wouldn\u2019t be possible with conventional computers.<\/p>\n

Today\u2019s supercomputers are built with AI (artificial intelligence) workloads in mind. In addition to weather forecasting, climate research, physical simulations, and oil and gas exploration, supercomputers help scientists discover more resilient building materials and study humans proteins and cellular systems at an extreme level of detail.<\/p>\n

Usually, the supercomputer\u2019s performance is measured in floating-point operations per second (FLOPS). In the field of scientific computations, FLOPS is a more accurate figure than measuring\u00a0instructions per second.<\/p>\n

Did you know the first supercomputer \u2014 Livermore Atomic Research Computer \u2014 was built for the US Navy Research and Development Centre in 1960.<\/p>\n

To show you how far we have come since then, we have curated a detailed list of fastest supercomputers in the world. They all are non-distributed computer systems running on Linux.<\/p>\n

13. Sequoia<\/h3>\n

\"\"<\/p>\n

Speed:<\/strong>\u00a017.1 petaFLOPS
\nCores:<\/strong>\u00a01,572,864<\/p>\n

Vendor:<\/strong>\u00a0IBM
\nLocation:<\/strong>\u00a0Lawrence Livermore National Laboratory, United States<\/p>\n

Sequoia uses IBM\u2019s BlueGene\/Q servers to deliver a theoretical peak performance of 20 petaFLOPS. It has 123% more cores and is 37% more energy efficient than its predecessor K computer.<\/p>\n

Although the machine is mostly used for nuclear weapons simulation, it is also available for many scientific purposes such as climate change and human genome analysis. It has also\u00a0demonstrated<\/a>\u00a0its great scalability with a 3D simulation of the human heart\u2019s electrophysiology.<\/p>\n

12. PANGEA III<\/h3>\n

\"\"Credit: Total S.A.<\/p>\n

Speed:<\/strong>\u00a017.8 petaFLOPS
\nCores:<\/strong>\u00a0291,024<\/p>\n

Vendor:<\/strong>\u00a0IBM
\nLocation:<\/strong>\u00a0CSTJF technical and scientific research center in Pau, France<\/p>\n

Pangea III relies on IBM\u2019s AI-optimized, high-performance architecture. IBM and NVIDIA worked together to build the industry\u2019s only CPU-to-GPU NVLink connection, which enables over 5 times faster memory bandwidth between the IBM POWER9 CPU and NVIDIA Tesla V100 Tensor Core GPUs than the conventional x86-based systems.<\/p>\n

The architecture not only improves computing performance but also enhances energy efficiency. The new system uses less than 10% of the energy consumption per petaFLOP as its predecessor, Pangea I and II.<\/p>\n

Pangea III has various applications, especially in three different fields \u2013 exploration and development seismic imaging, development and production models, and asset valuation and selectivity.<\/p>\n

11. Lassen<\/h3>\n

\"\"<\/p>\n

Speed:<\/strong>\u00a018.2 petaFLOPS
\nCores:<\/strong>\u00a0288,288<\/p>\n

Vendor:<\/strong>\u00a0IBM
\nLocation:<\/strong>\u00a0Lawrence Livermore National Laboratory, United States<\/p>\n

Lassen is designated for unclassified simulation and analysis. It is installed in the same lab and using the same building components as Sierra (#2 fastest supercomputer).<\/p>\n

Although Sierra is a big system, Lassen is a decent size in its own right: it is exactly 1\/6th of the size of its larger brother.\u00a0Lassen system is contained in 40 racks, while Sierra hogs up 240 racks.<\/p>\n

IBM Power9 processors and 253 terabytes of main memory help Lassen to achieve to a perk performance of 23 petaFLOPS.<\/p>\n

10. SuperMUC-NG<\/h3>\n

\"\"<\/p>\n

Speed:<\/strong>\u00a019.4 petaFLOPS
\nCores:<\/strong>\u00a0305,856<\/p>\n

Vendor:<\/strong>\u00a0Lenovo
\nLocation:<\/strong>\u00a0Leibniz Supercomputing Centre, Germany<\/p>\n

SuperMUC-NG features 6,400 Lenovo ThinkSystem SD650 direct-water-cooled computing nodes with over 700 terabytes of main memory and 70 petabytes of disk storage.<\/p>\n

It is connected to powerful visualization systems that contain a large 4K stereoscopic powerwall and a 5-sided CAVE artificial virtual reality environment.<\/p>\n

The supercomputer servers European scientists of many fields, including genome analysis, fluid dynamics, quantum chromodynamics, life sciences, medicine, and astrophysics.<\/p>\n

9. AI Bridging Cloud Infrastructure<\/h3>\n

\"\"Credit: ABCI<\/p>\n

Speed:<\/strong>\u00a019.8 petaFLOPS
\nCores:<\/strong>\u00a0391,680<\/p>\n

Vendor:<\/strong>\u00a0Fujitsu
\nLocation:<\/strong>\u00a0National Institute of Advanced Industrial Science and Technology, Japan<\/p>\n

This is the world\u2019s first large-scale Open AI Computing Infrastructure that delivers 32.577 petaFLOPS of peak performance. It has a total of 1,088 nodes, each containing 2 Intel Xenon Gold Scalable processors, 4 NVIDIA Tesla V100 GPU, 2 InfiniBand EDR HCAs, and 1 NVMe SSD.<\/p>\n

Fujitsu Limited claims that the supercomputer can achieve 20 times the thermal density of conventional data centers, and a cooling capacity of 70 kW Rack by using hot water and air cooling.<\/p>\n

8. Trinity<\/h3>\n

\"\"<\/p>\n

Speed:<\/strong>\u00a021.2 petaFLOPS
\nCores:<\/strong>\u00a0979,072<\/p>\n

Vendor:<\/strong>\u00a0Cray
\nLocation:<\/strong>\u00a0Los Alamos National Laboratory, United States<\/p>\n

Trinity is built to provide an extraordinary computational capability for the NNSA Nuclear Security Enterprise. It aims to improve geometric and physics fidelities in nuclear weapons simulation code, while ensuring that the\u00a0nuclear stockpile<\/a>\u00a0is safe, secure, and effective.<\/p>\n

The supercomputer was developed in two stages: the first stage incorporated the Intel Xeon Haswell processor, and the second stage included a substantial performance increase using the Intel Xeon Phi Knights Landing Processor. It can deliver a total peak performance of over 41 petaFLOPS.<\/p>\n

7. Piz Daint<\/h3>\n

\"\"<\/p>\n

Speed:<\/strong>\u00a021.2 petaFLOPS
\nCores:<\/strong>\u00a0387,872<\/p>\n

Vendor:<\/strong>\u00a0Cray
\nLocation:<\/strong>\u00a0Swiss National Supercomputing Centre, Switzerland<\/p>\n

This supercomputer, named after the mountain Piz Daint in the Swiss Alps, runs on Intel Xeon E5-26xx microprocessor and NVIDIA Tesla P100.<\/p>\n

Piz Daint utilizes\u00a0DataWarp\u2019s<\/a>\u00a0\u2018burst buffer mode\u2019 to increase effective bandwidth to and from storage devices. This accelerates the data input\/output rates, facilitating the analysis of millions of small, unstructured files.<\/p>\n

In addition to its daily tasks, it can handle the data analysis of some of the world\u2019s most data-intensive projects, such as data collected from experiments at the Large Hadron Collider.<\/p>\n

Read:\u00a0What Is A Particle Accelerator?<\/a><\/p>\n

6. Frontera<\/h3>\n

\"\"A view between two rows of Frontera servers | Credit: TACC<\/p>\n

Speed:<\/strong>\u00a023.5 petaFLOPS
\nCores:<\/strong>\u00a0448,448<\/p>\n

Vendor:<\/strong>\u00a0Dell EMC
\nLocation:<\/strong>\u00a0Texas Advanced Computing Center, United States<\/p>\n

Frontera<\/a>\u00a0opens up new possibilities in engineering and research by providing extensive computational resources that make it easier for scientists to tackle many complex challenges across a wide range of domains.<\/p>\n

Frontera features two computing subsystems: the first one focuses on double-precision performance while the second one focuses on single-precision stream-memory computing. It also has cloud interfaces and multiple application nodes for hosting virtual servers.<\/p>\n

5. Tianhe-2A<\/h3>\n

\"\"Tianhe-2 in National Supercomputer Center in Guangzhou<\/p>\n

Speed:<\/strong>\u00a061.4 petaFLOPS
\nCores:<\/strong>\u00a04,981,760<\/p>\n

Vendor:<\/strong>\u00a0NUDT
\nLocation:<\/strong>\u00a0National Supercomputing Center in Guangzhou, China<\/p>\n

With more than 16,000 computer nodes, Tianhe-2A represents the world\u2019s largest installation of Intel Ivy Bridge and Xeon Phi processors. While each node has 88 gigabytes of memory, the total memory (CPU+coprocessor) is 1,375 tebibyte.<\/p>\n

China spent 2.4 billion yuan (US$390 million) on building this supercomputer. It is now mostly used in simulations, analysis, and government security applications.<\/p>\n

4. Sunway TaihuLight<\/h3>\n

\"\"<\/p>\n

Speed:<\/strong>\u00a093 petaFLOPS
\nCores:<\/strong>\u00a010,649,600<\/p>\n

Vendor:<\/strong>\u00a0NRCPC
\nLocation:<\/strong>\u00a0National Supercomputing Center in Wuxi, China<\/p>\n

The computing power of TaihuLight comes from a homegrown several-core SW26010 CPU that includes both computing processing elements and management processing elements.<\/p>\n

A single SW26010 provides a peak performance of more than 3 teraFLOPS, thanks to its 260 processing elements (integrated into one CPU). Each computing processing element has a scratchpad memory that serves as a user-controlled cache, significantly reducing the memory bottleneck in most applications.<\/p>\n

In addition to life sciences and pharmaceutical research, TaihuLight has been used to simulate the universe with 10 trillion digital particles. However, China is trying to achieve a lot more: the country has already\u00a0stated its goal<\/a>\u00a0to be the leader in AI by 2030.<\/p>\n

3. Sierra<\/h3>\n

\"\"Image credit: Wikimedia<\/p>\n

Speed:<\/strong>\u00a094.6 petaFLOPS
\nCores:<\/strong>\u00a01,572,480<\/p>\n

Vendor:<\/strong>\u00a0IBM
\nLocation:<\/strong>\u00a0Lawrence Livermore National Laboratory, United States<\/p>\n

Sierra<\/a>\u00a0offers up to 6 times the sustained performance and 7 times the workload performance of its predecessor Sequoia. It combines two types of processor chips: IBM\u2019s Power 9 processors and NVIDIA\u2019s Volta GPUs.<\/p>\n

Sierra is specifically designed for assessing the performance of nuclear weapon systems. It is used for predictive applications in stockpile stewardship, the US program of reliability testing and maintenance of nuclear weapons without any nuclear testing.<\/p>\n

Read:\u00a0What Is Tensor Processing Unit (TPU)? How Is It Different From GPU?<\/a><\/p>\n

2. Summit<\/h3>\n

\"\"Image credit: ORNL<\/p>\n

Speed:<\/strong>\u00a0148.6 petaFLOPS
\nCores:<\/strong>\u00a02,414,592<\/p>\n

Vendor:<\/strong>\u00a0IBM
\nLocation:<\/strong>\u00a0Oak Ridge National Laboratory, United States<\/p>\n

Summit can deliver 200 petaFLOPS at peak. This is equivalent to 200 quadrillion floating-point operations per second.<\/p>\n

It is also the world\u2019s third most energy-efficient supercomputer, with a recorded power efficiency of 14.66 gigaFLOPS per watt.<\/p>\n

Summit\u2019s 4,600+ servers, which take up the size of two basketball courts, house more than 9,200 IBM Power9 processors and over 27,600 NVIDIA Tesla V100 GPUs. The system is connected by 185 miles of fiber optic cable, and it consumes enough power to run 8,100 homes<\/p>\n

In 2018, Summit became the first supercomputer to break the exascale barrier. While analyzing genomic data, it achieved a peak throughput of\u00a01.88 exaops<\/a>, which is nearly 2 billion billion calculations per second.<\/p>\n

The United States aims to develop a fully capable exascale computing ecosystem for scientific studies by the next year, and Summit is a step towards that.<\/p>\n

1. Fugaku<\/h3>\n

\"Fugaku\"<\/p>\n

Speed:<\/strong>\u00a0442 petaFLOPS
\nCores:<\/strong>\u00a07,630,848<\/p>\n

Vendor:<\/strong>\u00a0Fujitsu
\nLocation:<\/strong>\u00a0RIKEN Center for Computational Science, Japan<\/p>\n

With a theoretical peak performance of 537 petaFLOPs, Fugaku is the world\u2019s fastest supercomputer. It is also the first top-ranked supercomputer to be powered by ARM processors.<\/p>\n

As per the HPCG benchmark, Fugaku\u2019s performance surpasses the combined performance of the next top four supercomputers in the world.<\/p>\n

It\u2019s a great achievement for the Japanese government, but designing such a powerful system didn\u2019t come cheap. Since 2014, the government has spent about $1 billion on the project\u2019s R&D, acquisitions, and application development.<\/p>\n

 <\/p>\n

Original Article: https:\/\/www.rankred.com\/fastest-supercomputers-in-the-world\/<\/p>\n","protected":false},"excerpt":{"rendered":"

For most of us, a computer probably seems fast enough if it can run 8K videos or the latest version of Far Cry in 60 fps without slowing down. However, there are many complicated tasks that require billions of calculations per second \u2013 something a desktop with i9 processor can\u2019t do. That\u2019s where supercomputers come […]<\/p>\n","protected":false},"author":7,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[112],"tags":[],"class_list":["post-2885","post","type-post","status-publish","format-standard","hentry","category-article"],"_links":{"self":[{"href":"https:\/\/international.binus.ac.id\/computer-science\/wp-json\/wp\/v2\/posts\/2885"}],"collection":[{"href":"https:\/\/international.binus.ac.id\/computer-science\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/international.binus.ac.id\/computer-science\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/international.binus.ac.id\/computer-science\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/international.binus.ac.id\/computer-science\/wp-json\/wp\/v2\/comments?post=2885"}],"version-history":[{"count":1,"href":"https:\/\/international.binus.ac.id\/computer-science\/wp-json\/wp\/v2\/posts\/2885\/revisions"}],"predecessor-version":[{"id":2886,"href":"https:\/\/international.binus.ac.id\/computer-science\/wp-json\/wp\/v2\/posts\/2885\/revisions\/2886"}],"wp:attachment":[{"href":"https:\/\/international.binus.ac.id\/computer-science\/wp-json\/wp\/v2\/media?parent=2885"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/international.binus.ac.id\/computer-science\/wp-json\/wp\/v2\/categories?post=2885"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/international.binus.ac.id\/computer-science\/wp-json\/wp\/v2\/tags?post=2885"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}