Why traditional simulation is slow
Most simulation tools were built for CPUs. A CPU is powerful, but it processes instructions largely in sequence. When you are simulating a melt pool with millions of interacting particles, that means calculating forces, temperatures, and positions one particle at a time — or at best a few dozen at a time across a handful of cores.
A typical laser track simulation involves millions of time steps, each requiring calculations for every particle in the domain. On a CPU, this takes hours. A full parameter study with 50+ variants? Days to weeks.
What GPUs do differently
A modern GPU has thousands of processing cores. Each one is simpler than a CPU core, but when you have a problem where the same calculation needs to happen for millions of elements simultaneously, GPUs dominate.
Particle-based simulation is a natural fit. Every particle follows the same physics equations. Every particle needs its forces, temperatures, and positions updated at every time step. This is exactly the kind of massively parallel workload that GPUs are built for.
From hours to minutes
Our simulation engine was designed for GPUs from the ground up. It is not a CPU code that was ported to the GPU as an afterthought. Every data structure, every algorithm, every memory access pattern was built to exploit GPU parallelism.
The result: a single-track simulation that takes 8 hours on a CPU completes in about 20 minutes on a GPU. That is a 24x speedup.
This changes what is practically possible. Instead of simulating one or two parameter sets and hoping for the best, you can run 50, 80, or even 125 variations overnight. By morning, you have a complete map of your process window — which parameters work, which do not, and where the boundaries are.
Why it matters for your workflow
Speed is not just about convenience. When simulation takes days, it gets used once at the end of a project — if at all. When it takes minutes, it becomes part of the daily workflow. Engineers can test ideas, iterate quickly, and make decisions based on physics rather than intuition.
That is the real impact of GPU acceleration: it makes simulation practical enough to actually change how you work.