Mixing precision for model acceleration

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By applying high-precision calculations only where they’re needed most, a KAUST-led research team has been able to significantly speed up modeling of large geospatial datasets without overall precision loss. The approach, implemented on a high-performance computing system based on highly parallelized graphics processor units (GPUs), will allow larger datasets to be analyzed in shorter time.

While computers have the capacity to perform very large calculations very quickly, the result can sometimes be less precise than hand calculations because of the limitation of how numbers are stored in digital systems. Standard or “single” precision numbers effectively have 6–9 actual decimal digits of precision, meaning that any calculation resulting in a longer sequence of digits will be truncated, thereby losing information.

While double-precision numbers can be used, this doubles the memory and calculation intensity. For geospatial datasets where the accumulation of such precision errors can lead to erroneous modeling results, this imparts a major limitation on the size of dataset that can be calculated precisely.

Sameh Abdulah and colleagues including Hatem Ltaief, Marc Genton, Ying Sun and David Keyes from KAUST, in collaboration with researchers from the University of Tennessee, Knoxville (UTK) in the U.S., have now developed an elegant solution to this problem by mixing precision as required.

“For decades, modeling of environmental data relied on double-precision arithmetic to predict missing data,” says Abdulah. “Today, there is high-performance computing hardware that can run single- and half-precision arithmetic with a speedup of 16 and 32 times compared with double-precision arithmetic. To take advantage of this, we propose a three-precision framework that can exploit the acceleration of lower precision while maintaining accuracy by using double-precision arithmetic for vital information.”

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