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. The first of these updates always coincides with the International Supercomputing Conference in June, and the second is presented at the ACM/IEEE Supercomputing Conference in November. The project aims to provide a reliable basis for tracking and detecting trends in high-performance computing and bases rankings on HPL benchmarks, a portable implementation of the high-performance LINPACK benchm
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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. The first of these updates always coincides with the International Supercomputing Conference in June, and the second is presented at the ACM/IEEE Supercomputing Conference in November. The project aims to provide a reliable basis for tracking and detecting trends in high-performance computing and bases rankings on HPL benchmarks, a portable implementation of the high-performance LINPACK benchmark written in Fortran for distributed-memory computers.
The most recent edition of TOP500 was published in June 2025 as the 65th edition of TOP500, while the next edition of TOP500 will be published in November 2025 as the 66th edition of TOP500. As of June 2025, the United States' El Capitan is the most powerful supercomputer in the TOP500, reaching 1742 petaFlops (1.742 exaFlops) on the LINPACK benchmarks. As of submitted data until June 2025, the United States has the highest number of systems with 175 supercomputers; China is in second place with 47, and Germany is third at 41; the United States has by far the highest share of total computing power on the list (48.4%). Due to secrecy of the latest Chinese programs, publicly known supercomputer performance share in China represents only 2% that of global as of June 2025.
Discovered by embedding cosine similarity (sentence-transformers MiniLM, 384-dim).