TallnWide: Fast, Scalable and Geo-Distributed PCA for Big Data Analytics

We introduce TallnWide, a novel algorithm for performing Principal Component Analysis (PCA) on extremely large, high-dimensional datasets that are distributed across geographic locations, addressing both scalability and communication overhead in big data analytics.

• PCA is widely used for dimensionality reduction but faces scalability issues when both rows and columns grow large; existing methods can suffer from memory overflow and high communication cost in distributed environments.
• TallnWide leverages a zero-noise-limit Probabilistic PCA model with a block-division strategy to manage tall and wide data efficiently, suppressing intermediate data explosion.
• The algorithm introduces communication-efficient coordination among geographically distributed datacenters by transmitting only necessary parameters and minimizing idle computation time.
• Empirical evaluation on real large-scale datasets shows TallnWide handles significantly higher dimensions (10× larger) and achieves up to 2.9× faster runtime compared to conventional approaches in geo-distributed settings.
• For reproducibility and future extension, TallnWide’s implementation is available as open-source, enabling further research in scalable dimension reduction on distributed data.