Symbol T
T is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.
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Why this formula appears here
The underlying trade-off is a classical one from checkpoint/restart theory, and it is worth making explicit because it is the model every specific policy below is an instance of. If a checkpoint costs a fixed time C to write and failures arrive with mean time between them M , then choosing a checkpoint interval T trades two costs against each other: writing more often burns time on overhead, and writing less often burns time re-doing work lost since the last save. Minimising the sum of those two costs — checkpoint overhead C/T plus expected rework T/2M — over T gives the classical optimum:
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Symbol M
M is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.
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Read this expression with the definitions, units, and assumptions supplied by the article.
Research cited beside this formula
- [8] Announcing the MLPerf Storage v2.0 Checkpointing Workload ↗
- [2] The Llama 3 Herd of Models ↗
- [5] Check-N-Run: A Checkpointing System for Training Deep Learning Recommendation Models ↗
- [4] Characterization of Large Language Model Development in the Datacenter ↗
- [3] Revisiting Reliability in Large-Scale Machine Learning Research Clusters ↗
Published contexts (1)
A symbol can carry a different meaning in another article. Each occurrence keeps its own guide and term definitions.
Equation 5 · Datacenters
AI Datacenter Systems Engineering in Practice: An Advanced Technical Guide
This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text.
The underlying trade-off is a classical one from checkpoint/restart theory, and it is worth making explicit because it is the model every specific policy below is an instance of. If a checkpoint costs a fixed time C to write and failures arrive with mean time between them M , then choosing a checkpoint interval T trades two costs against each other: writing more often burns time on overhead, and writing less often burns time re-doing work lost since the last save. Minimising the sum of those two costs — checkpoint overhead C/T plus expected rework T/2M — over T gives the classical optimum:
Equation guide → · Article →