Topic: Hybrid/Heterogeneous

LLNL has signed a memorandum of understanding with HPC facilities in Germany, the United Kingdom, and the U.S., jointly forming the International Association of Supercomputing Centers.

The Lab's upcoming exascale-capable supercomputer will see an implementation of a converged accelerated computing unit, or APU, hybrid CPU-GPU compute engine.

In a presentation delivered to the 79th HPC User Forum at Oak Ridge National Laboratory, LLNL's Terri Quinn revealed that AMD’s forthcoming MI300 APU would be the computational bedrock of El Capitan, which is slated for installation at LLNL in late 2023.

The utility-grade infrastructure project massively upgraded the power and water-cooling capacity of the adjacent Livermore Computing Center, preparing it to house next generation exascale-class supercomputers for NNSA.

As the U.S. welcomed the world’s first “true” exascale supercomputer, three predecessor machines for LLNL's future exascale system El Capitan managed to rank highly on the latest Top500 List of the world’s most powerful supercomputers.

The Exascale Computing Project (ECP) 2022 Community Birds-of-a-Feather Days will take place May 10–12 via Zoom. The event provides an opportunity for the HPC community to engage with ECP teams to discuss our latest development efforts.

El Capitan will have a peak performance of more than 2 exaflops—roughly 16 times faster on average than the Sierra system—and is projected to be several times more energy efficient than Sierra.

LC sited two different AI accelerators in 2020: the Cerebras wafer-scale AI engine attached to Lassen; and an AI accelerator from SambaNova Systems into the Corona cluster.

A Livermore-developed programming approach helps software to run on different platforms without major disruption to the source code.

FGPU provides code examples that port FORTRAN codes to run on IBM OpenPOWER platforms like LLNL's Sierra supercomputer.

Umpire is a resource management library that allows the discovery, provision, and management of memory on next-generation architectures.

Highlights include debris and shrapnel modeling at NIF, scalable algorithms for complex engineering systems, magnetic fusion simulation, and data placement optimization on GPUs.

Highlights include the latest work with RAJA, the Exascale Computing Project, algebraic multigrid preconditioners, and OpenMP.

Highlights include recent LDRD projects, Livermore Tomography Tools, our work with the open-source software community, fault recovery, and CEED.

Highlights include the HYPRE library, recent data science efforts, the IDEALS project, and the latest on the Exascale Computing Project.

Livermore computer scientists have helped create a flexible framework that aids programmers in creating source code that can be used effectively on multiple hardware architectures.