The 67th edition of the TOP500 list of the world’s most powerful supercomputers was announced today at the ISC 2026 conference in Hamburg, Germany. LineShine, a previously unlisted system installed in China, debuts at No. 1, displacing El Capitan as the world’s most powerful supercomputer as measured by the High Performance Linpack (HPL) benchmark. The new list also reflects continued depth in U.S. and European exascale capability, a new entrant in Italy’s HPC fleet, and unchanged leadership atop the Green500 energy-efficiency ranking.

The 66th edition of the TOP500 list of the world’s most powerful supercomputers was announced today at the SC25 Conference in St Louis, Missouri. The new list reflects continued U.S. leadership in high-performance computing (HPC), historic European milestones, and growing global diversity across architectures and energy-efficient design.

The 65th edition of the TOP500 showed that the El Capitan system retains the No. 1 position. With El Capitan, Frontier, and Aurora, there are now 3 Exascale systems leading the TOP500. All three are installed at Department of Energy (DOE) laboratories in the United States.

A look back to supercomputing at the turn of the century

When I first attended the Supercomputing (SC) conferences back in the early 2000s as an IBMer working in High Performance Computing (HPC), it was obvious this conference was intended for serious computer science researchers and industries singularly focused on pushing the boundaries of computing. Linux was still in its infancy. I vividly remember having to re-compile kernels with newly released drivers every time there was a new server that came to market just so I could get the system to PXE boot over the network. But there was one …

Years ago, when Artificial Intelligence (AI) began to emerge as a potential technology to be harnessed as a powerful tool to change the way the world works, organizations began to kick the AI tires by exploring it’s potential to enhance their research or business. However, to get started with AI, neural networks needed to be created, data sets trained, and microprocessors were needed that could perform matrix-multiplication calculations ideally suited to perform these computationally demanding tasks. Enter the accelerator.