Summit Specs
Peak Petaflops: 200 IBM Power9 Processors: 9,216 IBM Power9 Cores: 193,536 Compute Nodes: 4,608 NVIDIA Volta Accelerators: 27,648
Memory (TB): 2,801 Disk Space (PB): 250 Floor Space (feet2): 5,600 Leadership-class Job Limit (Minimum): 922 Nodes
Current JobsPITypeElapsedRequestedNodes
Nuclear femtography: parton distribution functions for the Electron-Ion ColliderPhiala ShanahanINCITE6:31:0724:00:001,536
Exascale Simulation and Deep Learning Model for Energetic Particles in Burning PlasmasZhihong LinINCITE5:06:506:26:001,024
Extreme-Scale Simulations for Advanced Seismic Ground Motion and Hazard ModelingPhilip MaechlingALCC4:13:327:00:00750
Simulating Neutron Star Binary Merger Remnant Disks and Tilted Thin DiskAlexander TchekhovskoyALCC0:08:272:00:00192
2.2.2.02 ADSE14-Combustion-Pele: Transforming Combustion Science & Technology with Exascale SimulationsJacqueline ChenECP5:05:346:00:0050
2.2.2.02 ADSE14-Combustion-Pele: Transforming Combustion Science & Technology with Exascale SimulationsJacqueline ChenECP5:05:346:00:0050
Million Atom Chemical Dynamics at Heterogeneous Aqueous InterfacesRoberto CarALCC0:54:072:00:0045
Extreme Scale Multiphysics Models To Predict Metastatic Tumor Cell FateAmanda RandlesINCITE3:55:5124:00:0043
Pending JobsPITypeRequestedNodes
Intermolecular energy and electron transfer by non-orthogonal configuration interactionCoen de GraafINCITE0:10:003,000
Intermolecular energy and electron transfer by non-orthogonal configuration interactionCoen de GraafINCITE0:10:003,000
Intermolecular energy and electron transfer by non-orthogonal configuration interactionCoen de GraafINCITE0:10:003,000
Intermolecular energy and electron transfer by non-orthogonal configuration interactionCoen de GraafINCITE0:10:003,000
Advances in Quark and Lepton Flavor Physics with Lattice QCDAdreas KronfeldINCITE6:00:001,296
Scalable accelerated training of physics informed deep learning models for material scienceMassimiliano Lupo PasiniDD12:00:001,240
Scalable accelerated training of physics informed deep learning models for material scienceMassimiliano Lupo PasiniDD12:00:001,240
Scalable accelerated training of physics informed deep learning models for material scienceMassimiliano Lupo PasiniDD4:00:001,240
Cores in Use by Scientific Domain
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