Latest revision as of 20:40, 1 February 2021

Abstract

Computer games are complex performance-critical graphical applications which require specialized GPU hardware. For this reason, GPU drivers often include many heuristics to help optimize throughput. Recently however, new APIs are emerging which sacrifice many heuristics for lower-level hardware control and more predictable driver behavior. This shifts the burden for many optimizations from GPU driver developers to game programmers, but also provides numerous opportunities to exploit application-specific knowledge.

This paper examines different opportunities for specializing GPU code and reducing redundant data transfers. Static analysis of commercial games shows that 5-18% of GPU code is specializable by pruning dead data elements or moving portions to different graphics pipeline stages. In some games, up to 97% of the programs’ data inputs of a particular type, namely uniform variables, are unused, as well as up to 62% of those in the GPU internal vertex-fragment interface. This shows potential for improving memory usage and communication overheads. Insome test scenarios, removing dead uniform data can lead to 6x performance improvements.

We also explore the upper limits of specialization if all dynamic inputs are constant at run-time. For instance, if uniform inputs are constant, up to 44% of instructions can be eliminated in some games, with a further 14% becoming constant-foldable at compile time. Analysis of run-time traces, reveals that 48-91% of uniform inputs are constant in real games, so values close to the upper limit may be achieved in practice.

Original document

The different versions of the original document can be found in:

• https://www.pure.ed.ac.uk/ws/files/140939179/Specialization_Opportunities_CRAWFORD_DOA09072019_AFV.pdf

• http://xplorestaging.ieee.org/ielx7/8890589/8891061/08891638.pdf?arnumber=8891638,

http://dx.doi.org/10.1109/pact.2019.00029

• https://dblp.uni-trier.de/db/conf/IEEEpact/pact2019.html#CrawfordO19,

https://academic.microsoft.com/#/detail/2986419017

• https://www.research.ed.ac.uk/portal/en/publications/specialization-opportunities-in-graphical-workloads(8ff0fd55-c8b3-42be-ab6f-4d294c021e16).html,

https://doi.org/10.1109/PACT.2019.00029,

http://hdl.handle.net/20.500.11820/8ff0fd55-c8b3-42be-ab6f-4d294c021e16,

https://www.pure.ed.ac.uk/ws/files/140939179/Specialization_Opportunities_CRAWFORD_DOA09072019_AFV.pdf,

https://ieeexplore.ieee.org/document/8891638