The Argonne Training Program on Extreme-Scale Computing (ATPESC) is an intensive, two-week training opportunity for approximately 70 participants. This workshop focuses on programming methodologies that are effective across a variety of supercomputers and that are expected to be applicable to exascale systems.
|Event Name||Argonne Training Program on Extreme-Scale Computing (ATPESC 2023)|
|Event Date||July 30, 2023 – August 11, 2023|
|Submission Deadline||March 1, 2023. Please see event website for exact deadline, extensions and future updates.|
|Registration and Other Information||https://extremecomputingtraining.anl.gov/application/|
The Argonne Training Program on Extreme-Scale Computing (ATPESC) provides intensive, two-week training on the key skills, approaches, and tools to design, implement, and execute computational science and engineering applications on current high-end computing systems and the leadership-class computing systems of the future. ATPESC 2023 will take place July 30 – August 11, 2023. The event will be held in the Chicago area.
The core of the program will focus on programming methodologies that are effective across a variety of supercomputers and that are expected to be applicable to exascale systems. Additional topics to be covered include computer architectures, mathematical models and numerical algorithms, approaches to building community codes for HPC systems, and methodologies and tools relevant for Big Data applications.
The ATPESC program fills the gap that exists in the training computational scientists typically receive through formal education or other shorter courses. With around 70 participants accepted each year, admission to the ATPESC program is highly competitive. ATPESC is part of the Exascale Computing Project, a collaborative effort of the DOE Office of Science and the National Nuclear Security Administration.
Renowned scientists, HPC experts, and leaders will serve as lecturers and will guide the hands-on laboratory sessions. The core curriculum will address:
- Computer architectures and their predicted evolution
- Programming methodologies effective across a variety of today’s supercomputers and that are expected to be applicable to exascale systems, such as those with GPUs.
- Approaches for performance portability among current and future architectures
- Numerical algorithms and mathematical software
- Performance measurement and debugging tools
- Data analysis, visualization, and methodologies and tools for Big Data applications
- Approaches to building community codes for HPC systems
- Machine Learning and Data Science.