ParaX: Boosting Deep Learning for Big Data Analytics on Many-Core CPUs
Summary: ParaX enables scalable DL on many-core CPUs by assigning one input per core, removing barriers that impede bandwidth. Ultralight scheduler overlaps heavy and compute layers; NUMA-aware gradient server reduces synchronization, boosting throughput 2.93×. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Lujia Yin
- 2. Yiming Zhang
- 3. Zhaoning Zhang
- 4. Yuxing Peng
- 5. Peng Zhao
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,216 | User-Defined Operators: Efficiently Integrating Custom Algorithms into Modern Databases | 2022 | VLDB | 6.8898033e-05 |
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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 |
|---|---|---|---|---|
| 1,134 | DimmWitted: A Study of Main-Memory Statistical Analytics | 2014 | VLDB | 0.00012114015 |
| 1,294 | Data Management in Machine Learning: Challenges, Techniques, and Systems | 2017 | SIGMOD | 0.00011355506 |
| 4,885 | Machine Learning for Big Data | 2013 | SIGMOD | 6.5252891e-05 |
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