Adopting Mixed Precision
Scientific computing is a tool that’s used by scientists to push the limits of the known. Biology, physics, chemistry, and cosmology are examples of areas that rely on scientific computing to simulate and model the real world. In these fields of knowledge, numeric precision is paramount to yield coherent results. Since each decimal place matters in this case, scientific computing usually adopts double-precision data types to represent numbers with the highest possible precision.
However, that need for extra information comes with a price. The higher the numeric precision, the higher the computing power required to process those numbers. Besides that, higher precision also demands a higher memory space, increasing memory consumption.
In the face of those drawbacks, we must ask ourselves: do we need so much precision to build our models? Usually, we do not! In this sense, we can reduce the numeric precision for a few operations, thus bursting the...
