Constrained Precision Tuning

Precision tuning or customized precision number representations is emerging, in these recent years, as one of the most promising techniques that make it possible to save the resources on the available processors. In contrast to the uni-form precision, mixed precision tuning assigns different finite-precision types to each variable and arithmetic operation of a program and offers many additional optimization opportunities. However, this technique introduces new challenge related to the cost of operations or type conversions which can overload the program execution after tuning. In this article, we extend our tool POP, with efficient ways to limit the number of drawbacks of mixed precision and to achieve best compromise between performance and memory consumption. The results of our evaluation are discussed on a well-known set of tests from the FPBench suite.