code_aster and HPC computing

• You're running increasingly large computations but don't know when or how to parallelize, or where the actual computational cost lies.
• You're unsure whether to use shared memory, distributed memory, or MPI/OpenMP hybridization, and can't gauge the impact of that choice on your resources and turnaround time.
• You've tried enabling parallelism in code_aster but aren't sure which keywords to use or how to structure your input data accordingly.
• You're getting disappointing speed-up on a cluster and don't know how to diagnose whether the issue lies with the solver, the model partitioning, or the sizing of the requested resources.

From fundamentals to industrial computation

You start from the basics of parallel computing and work up to an industrial case run and measured on a cluster. Every theoretical concept is immediately connected to hands-on practice in code_aster.

Two hands-on sessions to make it stick

You don't just flip on some options: you parallelize a computation and measure its speed-up, then you diagnose and optimize the performance of a computation that scales poorly.

Hands-on trainers, right there with you

Our engineers and PhDs use code_aster daily on industrial projects at Simvia. On site, they share best practices and pitfalls in real time — the kind you won't find in any documentation.

Close, hands-on supervision

Small group of trainees, with a trainer on hand to unblock anyone stuck during the hands-on work.

A reusable diagnostic method

You leave with a simple framework for identifying, on your own computations, whether the bottleneck comes from the solver, the partitioning, or the resource sizing.

• Industry: computation engineers running code_aster on large-scale models who want to reduce turnaround time.
• Academia: researchers and PhD students running intensive computations that need to scale.

Basic practice of code_aster (setting up input data, running a computation). General notions of parallel computing are helpful but not required. No prior HPC experience needed.