Title | ||
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A Framework for Automated and Controlled Floating-Point Accuracy Reduction in Graphics Applications on GPUs. |
Abstract | ||
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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.
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Year | DOI | Venue |
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2017 | 10.1145/3151032 | TACO |
Keywords | Field | DocType |
Approximate computing, GPU, LLVM, floating-point precision, register file | Graphics,Floating point,Computer science,Parallel computing,Register file,Exploit,Compiler,Thread (computing),Computer graphics,Microarchitecture | Journal |
Volume | Issue | ISSN |
14 | 4 | 1544-3566 |
Citations | PageRank | References |
1 | 0.35 | 19 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
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Alexandra Angerd | 1 | 1 | 0.35 |
Erik Sintorn | 2 | 262 | 20.06 |
Per Stenström | 3 | 3048 | 234.09 |