[2107.14210] uiCA: Accurate Throughput Prediction of Basic Blocks on Recent Intel Microarchitectures

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Computer Science > Performance

arXiv:2107.14210 (cs)

[Submitted on 29 Jul 2021 (v1), last revised 19 May 2022 (this version, v3)]

Title:uiCA: Accurate Throughput Prediction of Basic Blocks on Recent Intel Microarchitectures

Authors:Andreas Abel, Jan Reineke<br>View a PDF of the paper titled uiCA: Accurate Throughput Prediction of Basic Blocks on Recent Intel Microarchitectures, by Andreas Abel and Jan Reineke

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Abstract:Performance models that statically predict the steady-state throughput of basic blocks on particular microarchitectures, such as IACA, Ithemal, llvm-mca, OSACA, or CQA, can guide optimizing compilers and aid manual software optimization. However, their utility heavily depends on the accuracy of their predictions. The average error of existing models compared to measurements on the actual hardware has been shown to lie between 9% and 36%. But how good is this? To answer this question, we propose an extremely simple analytical throughput model that may serve as a baseline. Surprisingly, this model is already competitive with the state of the art, indicating that there is significant potential for improvement.

To explore this potential, we develop a simulation-based throughput predictor. To this end, we propose a detailed parametric pipeline model that supports all Intel Core microarchitecture generations released between 2011 and 2021. We evaluate our predictor on an improved version of the BHive benchmark suite and show that its predictions are usually within 1% of measurement results, improving upon prior models by roughly an order of magnitude. The experimental evaluation also demonstrates that several microarchitectural details considered to be rather insignificant in previous work, are in fact essential for accurate prediction.

Our throughput predictor is available as open source at this https URL.

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Performance (cs.PF)

Cite as:<br>arXiv:2107.14210 [cs.PF]

(or<br>arXiv:2107.14210v3 [cs.PF] for this version)

https://doi.org/10.48550/arXiv.2107.14210

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arXiv-issued DOI via DataCite

Journal reference:<br>2022 International Conference on Supercomputing (ICS '22), June 28--30, 2022, Virtual Event, USA

Related DOI:

https://doi.org/10.1145/3524059.3532396

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Submission history<br>From: Andreas Abel [view email]<br>[v1]<br>Thu, 29 Jul 2021 17:48:04 UTC (625 KB)

[v2]<br>Thu, 17 Feb 2022 15:53:49 UTC (603 KB)

[v3]<br>Thu, 19 May 2022 20:56:41 UTC (657 KB)

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