ArborX will speed up exascale applications for computational cosmology, multiphysics data transfer, computational mechanics, wind farm simulations, and other research areas.
A team of cancer researchers and computer scientists have applied machine learning (ML) ensemble techniques to reduce training time, mitigate task complexity, and improve accuracy and classification performance for information extraction with cancer pathology reports.
Scientists have demonstrated the value in two particle simulators of so-called rendezvous methods, which invoke a communication pattern useful when the processors sending and receiving information are unknown to each other.
The Exascale Computing Project (ECP) is working to combine two key technologies, LLVM and continuous integration (CI), to ensure that current and future compilers are stable and performant on high-performance computing (HPC) and exascale computer systems.
Stencils are a fundamental computational pattern in many parallel distributed HPC algorithms, notably grid-based and finite element methods.
One software tool available now and showing tremendous promise for the exascale era is the open-source RAJA Portability Suite. RAJA is part of the Exascale Computing Project (ECP) NNSA software portfolio and is also supported by the ECP Programming Models and Runtimes area.