GoldMiner: Elastic Scaling of Training Data Pre-Processing Pipelines for Deep Learning

Summary: GoldMiner decouples data pre-processing from model training with stateless data workers that elastically pool cluster resources. By automatically extracting stateless pre-processing from pipelines, it scales across nodes, delivering up to 12.1x faster jobs and up to 2.5x better GPU utilization in large clusters. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6696
Venue: SIGMOD
Year: 2023
Pagerank: 5.4402488e-05
Overall Rank: 5,552 | 61.38%
DOI: 10.1145/3589773

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
8,348	FusionFlow: Accelerating Data Preprocessing for Machine Learning with CPU-GPU Cooperation	2024	VLDB	4.5410024e-05
8,737	Scheduling Data Processing Pipelines for Incremental Training on MLP-based Recommendation Models	2025	SIGMOD	4.456315e-05
10,492	Malleus: Straggler-Resilient Hybrid Parallel Training of Large-scale Models via Malleable Data and Model Parallelization	2025	SIGMOD	4.1945683e-05
10,580	GPEmu: A GPU Emulator for Faster and Cheaper Prototyping and Evaluation of Deep Learning System Research	2025	VLDB	4.1945683e-05
10,770	cedar: Optimized and Unified Machine Learning Input Data Pipelines	2025	VLDB	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
1,504	Analyzing and Mitigating Data Stalls in DNN Training	2021	VLDB	0.00011642333
2,170	tf.data: A Machine Learning Data Processing Framework	2021	VLDB	9.3821603e-05
3,698	Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines	2022	SIGMOD	6.8340435e-05

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