THE MANUFACTURING INTERVIEW
E ngineering simulations have been one of manufacturing’ s most stubborn bottlenecks. Running the complex mathematical models that predict how an aircraft wing will flex, how heat flows through an engine block or how a semiconductor will behave under load could take days, even weeks, of computation. Entire specialist teams were required simply to set up, run and interpret the results.
That bottleneck now has a solution. NVIDIA is a California-based technology company best known for designing graphics processing units, the specialised chips originally built to render video game visuals at high speed. Those same chips, it turned out, were well suited to a much broader class of problems: any calculation that can be broken into thousands of parallel threads running simultaneously. Scientific computing, weather forecasting, drug discovery and artificial intelligence all benefit from this architecture.
The company has spent more than a decade building a software ecosystem around its hardware. That ecosystem, known as CUDA-X, is a collection of libraries and tools that allow engineers and developers to write programmes that run on NVIDIA chips efficiently. It is the foundation on which much of the world’ s accelerated computing now runs.
Leading the effort to connect this platform to the engineering and manufacturing industries is Tim Costa, Vice President and General Manager of Computational Engineering at
“ Innovation at its best does not undermine reliability. It compounds it”
Tim Costa Vice President and General Manager of Computational Engineering NVIDIA
NVIDIA. His role, as he describes it, is to connect accelerated computing and AI to engineering and design industries. His goal is to“ cut engineering and simulation cycles from days to hours and put workflows that used to require hero teams into the hands of every designer and every engineer”.
From maths to manufacturing Tim’ s background began in mathematics. His doctorate at Oregon State University focused on multiscale methods: techniques for modelling systems where behaviour at the microscopic level, such as the movement of atoms in a material, determines what happens at the engineering scale, like whether a component fractures under load.
After graduating, he moved to Sandia National Laboratories, a US government research institution focused on science and engineering for national security, where he worked on simulation techniques for solid mechanics. From there, he spent years as an engineer building the mathematical libraries that
24 June 2026