**Motivation**

ICT energy use is growing fast and expected to reach 20% of global demand in 2030, from current 5% (https://www.nature.com/articles/d41586-018-06610-y). Supercomputers are part of this trend, and are reaching also the limits of power supply that can be provided within a single site.

Approximate computing is a class of techniques to reduce the energy consumpion across the lifetime of an application. Precision tuning is a subset of approximate computing aiming at trading off the precision of a computation against the time and energy spent on it.

As a simple example, consider the time you would spend to compute the area of a circle (square power of the radius, times Pi), when approximating Pi to 3.14 against the same computation performed using an approximation of Pi to 3.14159265359.

**TEXTAROSSA Contributions**

TEXTAROSSA develops techniques to automatically transform a program or fragment of a program to use smaller data while keeping the error under control, performing a number of adjustments during the operation of a running program to keep the approximation in line with the current data set on which the computation is running.

Our techniques are implemented as part of the LLVM compiler, an industry-standard tool that is used to generate the executable program from the source code produced by an application programmer.

In particular, we extend the TAFFO (https://taffo-org.github.io/) set of plugins to support heterogeneous accelerators such as graphic cards (GPUs), which are extensively used in supercomputing to provide fast and massively parallel computation facilities.

**Leading Partner**: CINI/POLIMI

## About the author