 | This article may rely excessively on sources too closely associated with the subject, potentially preventing the article from being verifiable and neutral. (February 2024) |
Simultaneous and heterogeneous multithreading (SHMT) is a software framework that takes advantage of heterogeneous computing systems that contain a mixture of central processing units (CPUs), graphics processing units (GPUs), and special purpose machine learning hardware, for example Tensor Processing Units (TPUs).[1][2]
Each component processes information differently. Often data has to move among processors, which can create bottlenecks, with one processor starving while waiting on another to finish.[1]
- ^ a b McClure, Paul (February 22, 2024). "Software tweak doubles computer processing speed, halves energy use". New Atlas. Retrieved 2024-02-25.
- ^ Hsu, Kuan-Chieh; Tseng, Hung-Wei (2023-12-08). "Simultaneous and Heterogenous Multithreading". 56th Annual IEEE/ACM International Symposium on Microarchitecture. MICRO '23. New York, NY, USA: Association for Computing Machinery. pp. 137–152. doi:10.1145/3613424.3614285. ISBN 979-8-4007-0329-4.