Topic: HPC Systems and Software

Highlights include scalable deep learning, high-order finite elements, data race detection, and reduced order models.

The hypre team's latest work gives scientists the ability to efficiently utilize modern GPU-based extreme scale parallel supercomputers to address many scientific problems.

BUILD tackles the complexities of HPC software integration with dependency compatibility models, binary analysis tools, efficient logic solvers, and configuration optimization techniques.

The HPC industry publication HPCwire named Bronis R. de Supinski, LLNL’s chief technology officer for Livermore Computing, as one of its People to Watch for 2021.

COVID-19 HPC Consortium scientists and stakeholders met virtually to mark the consortium’s one-year anniversary, discussing the progress of research projects and the need to pursue a broader organization to mobilize supercomputing access for future crises.

In his opening keynote address at the AI Systems Summit, LLNL CTO Bronis de Supinski described integration of two AI-specific systems to achieve system level heterogeneity.

In recognition of March as International Women’s History Month, SC21 profiled six women doing trailblazing work, including LLNL's Hiranmayi Ranganathan.

CTO Bronis de Supinski discusses the integrated storage strategy of the future El Capitan exascale supercomputing system, which will have in excess of 2 exaflops of raw computing power spread across nodes.

Our researchers will be well represented at the virtual SIAM Conference on Computational Science and Engineering (CSE21) on March 1–5. SIAM is the Society for Industrial and Applied Mathematics with an international community of more than 14,500 individual members.

A near node local storage innovation called Rabbit factored heavily into LLNL’s decision to select Cray’s proposal for its CORAL-2 machine, the lab’s first exascale-class supercomputer, El Capitan.

Proxy apps serve as specific targets for testing and simulation without the time, effort, and expertise that porting or changing most production codes would require.

Highlights include response to the COVID-19 pandemic, high-order matrix-free algorithms, and managing memory spaces.

The Maestro Workflow Conductor is a lightweight, open-source Python tool that can launch multi-step software simulation workflows in a clear, concise, consistent, and repeatable manner.

TEIMS manages collaborative tasks, site characterization, risk assessment, decision support, compliance monitoring, and regulatory reporting for the Environmental Restoration Department.

Researchers develop innovative data representations and algorithms to provide faster, more efficient ways to preserve information encoded in data.

Computational Scientist Ramesh Pankajakshan came to LLNL in 2016 directly from the University of Tennessee at Chattanooga. But unlike most recent hires from universities, he switched from research professor to professional researcher.

Highlights include perspectives on machine learning and artificial intelligence in science, data driven models, autonomous vehicle operations, and the OpenMP standard 5.0.

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.

Computer scientist Greg Becker contributes to HPC research and development projects for LLNL’s Livermore Computing division.

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

Users need tools that address bottlenecks, work with programming models, provide automatic analysis, and overcome the complexities and changing demands of exascale architectures.

This open-source file system framework supports hierarchical HPC storage systems by utilizing node-local burst buffers.

Highlights include CASC director Jeff Hittinger's vision for the center as well as recent work with PruneJuice DataRaceBench, Caliper, and SUNDIALS.

LLNL's interconnection networks projects improve the communication and overall performance of parallel applications using interconnect topology-aware task mapping.