A Framework for Automated and Controlled Floating-Point Accuracy Reduction in Graphics Applications on GPUs
Artikel i vetenskaplig tidskrift, 2017

Reducing the precision of floating-point values can improve performance and/or reduce energy expenditure in computer graphics, among other, applications. However, reducing the precision level of floating-point values in a controlled fashion needs support both at the compiler and at the microarchitecture level. At the compiler level, a method is needed to automate the reduction of precision of each floating-point value. At the microarchitecture level, a lower precision of each floating-point register can allow more floating-point values to be packed into a register file. This, however, calls for new register file organizations. This article proposes an automated precision-selection method and a novel GPU register file organization that can store floating-point register values at arbitrary precisions densely. The automated precision-selection method uses a data-driven approach for setting the precision level of floating-point values, given a quality threshold and a representative set of input data. By allowing a small, but acceptable, degradation in output quality, our method can remove a significant amount of the bits needed to represent floating-point values in the investigated kernels (between 28% and 60%). Our proposed register file organization exploits these lower-precision floating-point values by packing several of them into the same physical register. This reduces the register pressure per thread by up to 48%, and by 27% on average, for a negligible output-quality degradation. This can enable GPUs to keep up to twice as many threads in flight simultaneously.

approximate computing



register file

floating-point precision


Alexandra Angerd

Chalmers, Data- och informationsteknik, Datorteknik

Erik Sintorn

Chalmers, Data- och informationsteknik, Datorteknik

Per Stenström

Chalmers, Data- och informationsteknik, Datorteknik

Transactions on Architecture and Code Optimization

1544-3566 (ISSN) 1544-3973 (eISSN)

Vol. 14 4 46





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