The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: Frontiers in Generative AI for HPC Science and Engineering: Foundations, Challenges, and Opportunities
Authors: Seth Ockerman (University of Wisconsin-Madison, Argonne National Laboratory (ANL)); Amal Gueroudji (Argonne National Laboratory (ANL)); Song Young Oh (University of Chicago); Robert Underwood and Nicholas Chia (Argonne National Laboratory (ANL)); Kyle Chard (University of Chicago, Argonne National Laboratory (ANL)); Robert Ross (Argonne National Laboratory (ANL)); and Shivaram Venkataraman (University of Wisconsin-Madison)
Abstract: Vector databases have rapidly grown in popularity, enabling efficient similarity search over data such as text, images, and video. They now play a central role in modern AI workflows, aiding large language models by grounding model outputs in external literature through retrieval-augmented generation. Despite their importance, little is known about the performance characteristics of vector databases in high-performance computing (HPC) systems that drive large-scale science. This work presents an empirical study of distributed vector database performance on the Polaris supercomputer in the Argonne Leadership Computing Facility. We construct a realistic biological-text workload from BV-BRC and generate embeddings from the peS2o corpus using Qwen3-Embedding-4B. We select Qdrant to evaluate insertion, index construction, and query latency with up to 32 workers. Informed by practical lessons from our experience, this work takes a first step toward characterizing vector database performance on HPC platforms to guide future research and optimization.
Back to Frontiers in Generative AI for HPC Science and Engineering: Foundations, Challenges, and Opportunities Archive Listing Back to Full Workshop Archive Listing