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Computational capacity of the fastest supercomputers

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Technological Change

What you should know about this indicator

  • Computational capacity is measured with the Linpack benchmark, which tests how fast a computer can solve a large, dense set of linear equations.
  • This indicator shows the highest sustained Linpack speed achieved by the fastest supercomputer each year, as recorded by the TOP500 project.
  • One gigaflop per second (Gflop/s) equals one billion per second.
  • The Linpack benchmark tests only one type of computation, so it doesn't capture overall system performance. Real-world applications often perform differently than this benchmark suggests.

How is this data described by its producer?

The Top500 list the 500 fastest computer system being used today. In 1993 the collection was started and has been updated every 6 months since then. The report lists the sites that have the 500 most powerful computer systems installed. The best Linpack benchmark performance achieved is used as a performance measure in ranking the computers. The TOP500 list has been updated twice a year since June 1993.

"The Linpack Benchmark is a measure of a computer’s floating-point rate of execution. It is determined by running a computer program that solves a dense system of linear equations. The paper “The LINPACK Benchmark: Past, Present, and Future” by Jack Dongarra, Piotr Luszczek, and Antoine Petitet provides a look at the details of the benchmark and provides performance data in graphics form for a number of machines on basic operations. A copy of the paper is available at http://www.netlib.org/utk/people/JackDongarra/PAPERS/hpl.pdf"

Computational capacity of the fastest supercomputers
The number of carried out per second by the fastest supercomputer in any given year. This is expressed in gigaflops, equivalent to 10⁹ floating-point operations per second.
Source
Dongarra et al. (2025) – with major processing by Our World in Data
Last updated
February 1, 2026
Next expected update
February 2027
Date range
1993–2025
Unit
gigaflops per second

Related research and writing

What is Moore's Law?

Max Roser, Hannah Ritchie, and Edouard Mathieu

Sources and processing

This data is based on the following sources

Dongarra et al. – TOP500 Supercomputer Lists (1993-2025)

The TOP500 project ranks and details the 500 most powerful non-distributed computer systems in the world. The project was started in 1993 and publishes an updated list of the supercomputers twice a year (June and November). This dataset combines all TOP500 lists from 1993 to 2025, including computational performance benchmarks, system specifications, and rankings.

Retrieved on
February 1, 2026
Retrieved from
https://www.top500.org/
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
“Performance of Various Computers Using Standard Linear Equations Software”, Jack Dongarra, University of Tennessee, Knoxville TN, 37996, Computer Science Technical Report Number CS - 89 – 85, today’s date, url:http://www.netlib.org/benchmark/performance.ps."

The TOP500 project ranks and details the 500 most powerful non-distributed computer systems in the world. The project was started in 1993 and publishes an updated list of the supercomputers twice a year (June and November). This dataset combines all TOP500 lists from 1993 to 2025, including computational performance benchmarks, system specifications, and rankings.

Retrieved on
February 1, 2026
Retrieved from
https://www.top500.org/
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
“Performance of Various Computers Using Standard Linear Equations Software”, Jack Dongarra, University of Tennessee, Knoxville TN, 37996, Computer Science Technical Report Number CS - 89 – 85, today’s date, url:http://www.netlib.org/benchmark/performance.ps."

How we process data at Our World in Data

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Read about our data pipeline
Notes on our processing step for this indicator

Data is aggregated from all TOP500 lists published between 1993 and 2025. For each year, we select the maximum Rmax value across all lists published in that year. Performance values are converted from teraflops per second (TFlop/s) to gigaflops per second (GFlop/s) by multiplying by 1000.

Reuse this work

  • All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
  • All data, visualizations, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Computational capacity of the fastest supercomputers”, part of the following publication: Max Roser, Hannah Ritchie, and Edouard Mathieu (2023) - “Technological Change”. Data adapted from Dongarra et al.. Retrieved from https://archive.ourworldindata.org/20260304-094028/grapher/supercomputer-power-flops.html [online resource] (archived on March 4, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Dongarra et al. (2025) – with major processing by Our World in Data

Full citation

Dongarra et al. (2025) – with major processing by Our World in Data. “Computational capacity of the fastest supercomputers” [dataset]. Dongarra et al., “TOP500 Supercomputer Lists (1993-2025)” [original data]. Retrieved April 1, 2026 from https://archive.ourworldindata.org/20260304-094028/grapher/supercomputer-power-flops.html (archived on March 4, 2026).

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Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/supercomputer-power-flops.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://ourworldindata.org/grapher/supercomputer-power-flops.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

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metadata = requests.get("https://ourworldindata.org/grapher/supercomputer-power-flops.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/supercomputer-power-flops.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/supercomputer-power-flops.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/supercomputer-power-flops.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear