Supported by the Advanced Simulation and Computing program, the open-source Axom project focuses on developing software infrastructure components that can be shared by HPC applications running on diverse computing platforms.
Computing relies on engineers like Stephanie Brink to keep the legacy codes running smoothly. “You’re only as fast as your slowest processor or your slowest function,” says Brink, who works in CASC. By analyzing a legacy code’s performance, Brink and her team can reduce the amount of time it takes to run and allow for more critical science to be accomplished.
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.
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.
The 2019 Department of Energy (DOE) Performance, Portability and Productivity meeting is slated for April 2–4, 2019, in Denver, CO, where attendees will have the opportunity to share ideas and updates on performance portability.
Performance analysis of parallel scientific codes is becoming increasingly difficult, and existing tools fall short in revealing the root causes of performance problems. We have developed the HAC model, which allows us to directly compare the data across domains and use data visualization and analysis tools available in other domains.
AutomaDeD is a tool that automatically diagnoses performance and correctness faults in MPI applications. It has two major functionalities: identifying abnormal MPI tasks and code regions and finding the least-progressed task. The tool produces a ranking of MPI processes by their abnormality degree and specifies the regions of code where faults are first manifested.
To overcome the shortcomings of the analytical and architectural approaches to performance modeling and evaluation, we are developing techniques that emulate the behavior of anticipated future architectures on current machines.