Google patents AI tool to predict server workload speed using less hardware
Google describes a method that predicts how fast a large computer task will run without using the full set of servers. It runs a smaller version of the task on limited hardware and uses a machine learning model to estimate the extra time needed for data transfer between servers.
In the filing’s own words
Aspects of the disclosure are directed to estimating the performance of workloads with reduced hardware utilization. This results in significant reduction in processing and memory costs to estimate the performance of workloads, as less hardware resources are involved to accurately estimate the performance of a workload. To estimate the performance with reduced hardware utilization, the workload is rewritten for local operations on the reduced hardware and the rewritten workload is run on the reduced hardware to generate a reduced hardware profile, including a reduced hardware performance. The workload is also input to a machine learning model trained to determine a communication cost for the workload. The machine learning model outputs the communication cost for the workload. The reduced hardware profile is combined with the communication cost to generate an estimated profile, including the estimated performance for the workload.
What exactly they claim (claim 1)
1. A method for estimating a performance of a workload comprising: receiving, by one or more processors, a workload to be run on a plurality of hardware resources; rewriting, by the one or more processors, the workload to run on reduced hardware resources; running, by the one or more processors, the workload on the reduced hardware resources to generate a reduced hardware performance; estimating, by the one or more processors, a communication cost for the workload to run on the plurality of hardware resources using a machine learning model; combining, by the one or more processors, the reduced hardware performance with the communication cost to generate an estimated performance for the workload; and outputting, by the one or more processors, the estimated performance.
Facts
- Company
- Google (Google LLC)
- Official title
- Machine Learning Based Performance Estimator for Large Scale Workloads
- Publication number
- US20260300021A1
- Application number
- 19530475
- Filed
- Feb 5, 2026
- Published
- Oct 1, 2026
- Inventors
- 8
- Claims
- 20
Classified under
G06F 9/5044 Physics & computingG06F 9/5038 Physics & computingMore from Google in AI & software
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Summaries are simplified from the filing’s own abstract and first claim. A patent filing is not a product announcement.
Patent data and drawings: U.S. Patent and Trademark Office (USPTO). Nebula is not affiliated with or endorsed by the USPTO.