This is a talk for people who need to perform large numeric calculations. They could be scientists, developers working in close contact with scientists, or even people working on finance and other quantitative fields. Such people are routinely confronted with issues like 1 parallelism: how to parallelize calculations efficiently 2 data: how to store and manage large amounts of data efficiently 3 memory: how to avoid running out of memory 4 performance: how to be fast The goal of the talk is to teach some lessons learned after several years of doing numeric simulations in a context were micro-optimizations are the least important factor, while overall architecture, design choices and good algorithms are of paramount importance.