Decoupled I/O-Dominant Pipelines for Large-Scale Whole-Slide Image Embedding Extraction
Whole-slide images (WSIs) are central to computational pathology but are prohibitively large, making patch-based processing the practical unit for foundation model inference. At scale, however, generating and handling massive numbers of patches on quickly introduces significant I/O and orchestration overhead, often dominating end-to-end performance. We present a decoupled, I/O-aware pipeline for large-scale WSI embedding extraction that decomposes the workflow into three stages: (1) patch generation and staging, (2) embarrassingly parallel embedding inference, and (3) sharded vector database ingestion. This design isolates data movement from compute, enabling efficient patch delivery, sca...
| Authors | Mayanka Chandrashekar, Xi Zhang, Ethan Seefried et al. |
|---|---|
| Primary category | cs.DC |
| Published | 27 August 2026 |
| Last updated at source | 27 August 2026 |
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