Exascale Computing Project Contributes to Accelerating Cancer Research
The Exascale Computing Project's CANDLE application will improve cancer research techniques and clinical outcomes
The Exascale Computing Project's CANDLE application will improve cancer research techniques and clinical outcomes
The Exascale Era is here.
Lawrence Livermore National Lab is preparing for El Capitan, the National Nuclear Security Administration’s first exascale supercomputer.
The CANcer Distributed Learning Environment (CANDLE) project provides deep-learning computing methodologies for accelerating cancer research.
Project teams can improve the capabilities of math libraries, the foundation of scientific simulations, via cross-project research.
ECP's Birds-of-a-Feather Days, May 10–12, will give developers the chance to learn about leading-edge software and performance-analysis tools.
The Extreme-scale Scientific Software Stack (E4S) continues to evolve as a broad collection of software capabilities for scientific research.
The latest version of the Extreme-scale Scientific Software Stack (E4S) provides support for three different GPU architectures.
ECP plays key support roles in the CANDLE project, which is addressing three significant science challenge problems in cancer research.
The ExaSGD project wants to enable the day-to-day operation of the national power grid to hold a large number of renewable power sources.
ECP’s special podcast series on preparing code for the Aurora exascale system begins by focusing on an earthquake risk assessment application.
Simulating earthquake processes from end to end is key to being able to design bridges and buildings to be more resilient to earthquakes.
ECP's Data and Visualization portfolio is delivering data management software to store, save state, share, and facilitate the analysis of exascale data.
Spack (Supercomputer PACKage manager) is an R&D 100–Award winner because of its worldwide impact on high-performance computing.
The ZFP software development effort is tackling the critical task of overcoming the performance cost of data movement for exascale computing.
Machine learning, artificial intelligence, and data analytics are converging with high-performance computing to advance scientific discovery.
Productivity Sustainability Improvement Planning enables software developers to identify development bottlenecks and track progress to overcome them.
The Let's Talk Exascale podcast looks at the xSDK4ECP and hypre projects with Ulrike Meier Yang of Lawrence Livermore National Laboratory.
Christian Trott of Sandia National Laboratories shares insights about Kokkos, a programming model for numerous Exascale Computing Project applications.
Researchers involved with an input/output software product describe how it will enable users of high-performance computing systems to roll with the rapid pace of change.