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Pecan:{Cost-Efficient}{ML} Data Preprocessing with Automatic Transformation Ordering and Hybrid Placement
D Graur, O Mraz, M Li, S Pourghannad… - 2024 USENIX Annual …, 2024 - usenix.org
Input data preprocessing is a common bottleneck in machine learning (ML) jobs, that can
significantly increase training time and cost as expensive GPUs or TPUs idle waiting for …
significantly increase training time and cost as expensive GPUs or TPUs idle waiting for …
A Selective Preprocessing Offloading Framework for Reducing Data Traffic in DL Training
Deep learning (DL) training is data-intensive and often bottlenecked by fetching data from
remote storage. Recognizing that many samples' sizes diminish during data preprocessing …
remote storage. Recognizing that many samples' sizes diminish during data preprocessing …
Lotus: Characterization of Machine Learning Preprocessing Pipelines via Framework and Hardware Profiling
Preprocessing input data is a crucial step in machine learning pipelines, involving tasks
such as loading, decoding, and applying transformations. Prior works have identified …
such as loading, decoding, and applying transformations. Prior works have identified …
Multi-Level Erasure Coded Storage Design and Its Relationship to Deep Learning Workloads
M Wang - 2024 - knowledge.uchicago.edu
Large-scale data centers store vast amounts of user data across numerous disks,
necessitating redundancy mechanisms like erasure coding (EC) to protect against disk …
necessitating redundancy mechanisms like erasure coding (EC) to protect against disk …
[การอ้างอิง][C] Analysis of Deep Learning Preprocessing Stage and Selective Offloading for Reducing Training Data Traffic Working Draft–Private View Only
M Wang, G Waldspurger, S Sundararaman, H Gunawi