The number of possible combinations is prohibitively large and therefore impossible to explore. When we are able to emulate the effects of reduced precision, we can proceed with the design of the tests that will give us knowledge of the sensitivity of the model variables regarding their numerical precision. Using the RPE and a list of parameters containing the precisions that will be used for each real variable in the code, it is possible within a single binary to emulate the effect on the outputs of a specific choice of precision. Instead, the method presented in this paper relies on the use of a tool called a reduced-precision emulator (RPE) that can significantly streamline the process.
#How to use gmd speed time code
Modifying and recompiling the code for each case that has to be evaluated would require a prohibitive amount of effort. Then we can evaluate the level of precision that they truly need. It consists of a simple idea: we reduce the precision of a group of variables and measure how it affects the outputs. This paper presents a novel method that enables modern and legacy codes to benefit from a reduction of the precision of certain variables without sacrificing accuracy. The only input that is needed is a method to determine which real variables can be represented with fewer bits without affecting the accuracy of the results. Consequently, it is possible to improve computational performance by establishing a more appropriate choice of precision. Most scientific codes have overengineered the numerical precision, leading to a situation in which models are using more resources than required without knowing where they are required and where they are not. Mixed-precision approaches can provide substantial speed-ups for both computing- and memory-bound codes with little effort.
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