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Scheduling Data Processing Pipelines for Incremental Training on MLP-based Recommendation Models
Summary: Intra-pipeline prefetching hides feature processing during embedding lookup in incremental MLP training on CPU-limited clusters. Inter-pipeline scheduling overlaps critical comms with idle non-critical tasks, yielding 1.36x speedup via RECS on TensorFlow.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 7118
- Venue
- SIGMOD
- Year
- 2025
- Pagerank
- 4.4520434e-05
- Overall Rank
- 8,733 | 39.31%
- DOI
-
10.1145/3722212.3724454
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 1,044 |
DimmWitted: A Study of Main-Memory Statistical Analytics |
2014 |
VLDB |
0.00014465007 |
| 1,500 |
Analyzing and Mitigating Data Stalls in DNN Training |
2021 |
VLDB |
0.00011636174 |
| 2,175 |
tf.data: A Machine Learning Data Processing Framework |
2021 |
VLDB |
9.3745231e-05 |
| 2,456 |
Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities |
2021 |
SIGMOD |
8.7649259e-05 |
| 2,678 |
HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework |
2022 |
VLDB |
8.3224016e-05 |
| 2,690 |
Accelerating Recommendation System Training by Leveraging Popular Choices |
2022 |
VLDB |
8.2911466e-05 |
| 3,694 |
Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines |
2022 |
SIGMOD |
6.8316905e-05 |
| 4,175 |
FastFlow: Accelerating Deep Learning Model Training with Smart Offloading of Input Data Pipeline |
2023 |
VLDB |
6.3772575e-05 |
| 5,169 |
HET-GMP: A Graph-based System Approach to Scaling Large Embedding Model Training |
2022 |
SIGMOD |
5.642415e-05 |
| 5,560 |
GoldMiner: Elastic Scaling of Training Data Pre-Processing Pipelines for Deep Learning |
2023 |
SIGMOD |
5.4350242e-05 |
| 6,464 |
Materialization and Reuse Optimizations for Production Data Science Pipelines |
2022 |
SIGMOD |
5.0471003e-05 |
| 8,047 |
MultiBiSage: A Web-Scale Recommendation System Using Multiple Bipartite Graphs at Pinterest |
2023 |
VLDB |
4.5946116e-05 |
| 9,091 |
FEC: Efficient Deep Recommendation Model Training with Flexible Embedding Communication |
2023 |
SIGMOD |
4.393856e-05 |
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| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 4,175 |
FastFlow: Accelerating Deep Learning Model Training with Smart Offloading of Input Data Pipeline |
2023 |
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6.3772575e-05 |
| 4,437 |
TencentRec: Real-time Stream Recommendation in Practice |
2015 |
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| 1,788 |
StreamRec: A Real-Time Recommender System |
2011 |
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0.00010541173 |
| 6,464 |
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5.0471003e-05 |
| 2,175 |
tf.data: A Machine Learning Data Processing Framework |
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9.3745231e-05 |
| 3,694 |
Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines |
2022 |
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| 5,997 |
DLRover-RM: Resource Optimization for Deep Recommendation Models Training in the Cloud |
2024 |
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| 9,091 |
FEC: Efficient Deep Recommendation Model Training with Flexible Embedding Communication |
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SIGMOD |
4.393856e-05 |
| 10,541 |
IncrCP: Decomposing and Orchestrating Incremental Checkpoints for Effective Recommendation Model Training |
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4.1905499e-05 |
| 2,690 |
Accelerating Recommendation System Training by Leveraging Popular Choices |
2022 |
VLDB |
8.2911466e-05 |