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What was inside Summit?
Summit’s defining choice was to put several powerful GPUs beside general-purpose CPUs in every node. That made it an accelerator-heavy system: CPUs coordinated work and handled tasks suited to them, while GPUs performed many of the parallel calculations used in simulation and machine learning.
| Component | Per node | Across 4,608 nodes |
|---|---|---|
| IBM POWER9 CPUs | 2 | 9,216 |
| NVIDIA Tesla V100 GPUs | 6 | 27,648 |
| DDR4 memory | 512 GB | More than 2.3 PB, based on the per-node specification |
| GPU HBM2 memory | 96 GB | About 0.44 PB, based on the per-node specification |
| Non-volatile memory | 1,600 GB | About 7.4 PB, based on the per-node specification |
| Peak performance | About 42 TFLOPS in the official node table | About 200 PFLOPS theoretical peak in ORNL’s system description |
The memory figures describe different parts of the system, not one interchangeable pool. DDR4 was system memory, HBM2 was high-bandwidth memory attached to the GPUs, and the official node specification separately listed non-volatile memory. ORNL described the system as having more than 10 PB of aggregate memory. Summit also used a 250 PB IBM GPFS/Spectrum Scale file system, a separate shared-storage tier rather than memory inside each node.
How did Summit’s CPUs, GPUs and network work together?
Inside a node: POWER9, V100 and NVLink
Within each AC922 node, NVIDIA NVLink provided high-bandwidth communication between POWER9 CPUs and V100 GPUs. This mattered because adding processors alone does not make a workload faster if data cannot reach them quickly enough. Summit’s architecture aimed to reduce that bottleneck by giving the accelerators a fast route to data and to the CPUs coordinating a computation.
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The design could support both traditional simulations and machine-learning work, but it did not make every program automatically faster. Scientists had to adapt software to use the GPUs and manage data movement efficiently.
Between nodes: InfiniBand
Summit connected nodes using dual-rail Mellanox EDR 100G InfiniBand in a non-blocking fat-tree. In practical terms, multiple network links and the fat-tree design helped thousands of nodes exchange data as large jobs were distributed across the machine. EE Times’ June 9, 2018 feature described Summit as the first public high-performance cluster at this scale to support PCI Express 4.0 and reported cross-sectional network bandwidth approaching one petabit per second.
That network was crucial for workloads that repeatedly exchanged results among nodes. A GPU-rich node could calculate quickly, but a large simulation could still be limited if its pieces could not communicate at scale.
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How powerful was Summit, and how much power did it use?
ORNL’s 2018-era system description gave Summit a theoretical peak of 200 petaflops—about 200 quadrillion floating-point operations per second under idealized conditions. That peak is a design specification, not a promise that every application would sustain that speed. ORNL’s official specification table listed approximately 42 teraflops per node; multiplied across 4,608 nodes, that is roughly 193.5 petaflops, consistent with the rounded system-level figure.
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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →The official specification table listed peak power at about 13 MW. A separate historical planning passage on ORNL’s system page cited about 15 MW, so the figures refer to different contexts rather than one exact, universal reading. Both convey the scale of the electricity required to operate a machine of this size.
What did scientists use Summit for?
Summit was dedicated to open science, with work spanning energy, climate, materials, biology, health and artificial intelligence. Its mix of CPUs, GPUs, memory and high-speed networking suited projects that combined large datasets with complex computation.
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Biology and health
ORNL documented a biology project that combined neutron-scattering experiments, cryo-electron microscopy images and Summit computation to study intrinsically disordered proteins. Other ORNL reporting described molecular-dynamics work investigating DNA-repair mechanisms.
Simulation and AI
Summit was also used for large-scale simulations, including work relevant to energy and climate science, and for AI-supported processing of scientific data. In a 2024 ORNL decommissioning feature, infrastructure operations group leader Paul Abston said: “Summit was designed to run huge simulations on supernovae and fusion reactors.” The V100 GPUs were useful not only for simulations but also for machine-learning methods applied to scientific problems.
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Demand continued late in the system’s life. Under the SummitPLUS extension, 108 projects received more than 19 million compute hours from January through October 2024. OLCF director of science Bronson Messer described the extension this way: “Summit has been a remarkably successful supercomputer, and there was no reason to limit that success to just five years.”
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Is Summit still running?
No. Summit debuted in June 2018 and was decommissioned on November 15, 2024. ORNL’s lifecycle notice set that date as the last day for batch jobs, and the archived user guide now warns that the system is no longer online. Summit is therefore a retired system, not a machine on which researchers can currently run jobs.
Frontier had taken over as the Oak Ridge Leadership Computing Facility’s flagship before Summit’s retirement. During its service, Summit remained among the world’s fastest systems and delivered more than 200 million node-hours, according to OLCF’s 2024 lifecycle notice.
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