An Automated and Controlled Numerical Precision Reduction Framework for GPUs
This thesis proposes a GPU framework which establishes such a connection. The first part of the framework consists of a method for automatically selecting an appropriate precision for each floating-point value given the tolerable output deviation. The results show that by allowing a small, but acceptable, degradation of output quality, the number of bits needed to represent the floating-point values can be significantly reduced.
The second part of the framework is a novel GPU register file organization together with a register allocation algorithm capable of leveraging the precision-reduced floats given by the first part of the framework. The register allocation algorithm uses the precision-reduced floats to lower the register footprint of each thread. This is of great importance for GPUs since, unlike traditional CPU architectures, GPUs hide latency by keeping a large number of threads in flight simultaneously. Also, to enable fast context switching, the state of all active threads are readily available in the register file. As the thread register footprint limits the number of active threads, it might impede latency hiding. Our evaluation shows that the increase in active threads is translated into a significant performance improvement when using our proposed GPU register file organization, for a smaller cost than increasing the number of threads by using a larger register file.
Chalmers, Data- och informationsteknik, Datorteknik
A Framework for Automated and Controlled Floating-Point Accuracy Reduction in Graphics Applications on GPUs
Transactions on Architecture and Code Optimization,; Vol. 14(2017)
Artikel i vetenskaplig tidskrift
A. Angerd, E. Sintorn, P. Stenström. A Register File Organization to Support Variable Floating-Point Precision in GPUs
ACE: Approximativa algoritmer och datorsystem
Vetenskapsrådet (VR), 2015-01-01 -- 2018-12-31.
Technical report L - Department of Computer Science and Engineering, Chalmers University of Technology and Göteborg University: 182
Chalmers tekniska högskola
ES51, Rännvägen 6.
Opponent: Ass.Prof. Magnus Jahre, Norwegian University of Science and Technology, Norway.