Exploring Distributed Vector Databases Performance on HPC Platforms

Overview

This research paper, to be presented at SC'25 Workshop in October 2025, represents a first step toward characterizing vector database performance on high-performance computing (HPC) platforms, specifically focusing on Qdrant.

Research Motivation

As AI workloads increasingly run on HPC infrastructure, understanding vector database behavior in these environments becomes critical for:

• Scientific computing applications
• Large-scale AI model training and inference
• Data-intensive research workflows
• Multi-node distributed computing scenarios

Key Contributions

Performance Characterization

• Throughput analysis across node counts
• Latency measurements under various loads
• Scalability patterns in HPC environments
• Resource utilization (CPU, memory, network)
• I/O characteristics

HPC-Specific Insights

• Impact of high-bandwidth interconnects (InfiniBand)
• Effects of parallel file systems
• Scaling behavior with compute node count
• Comparison with cloud-based deployments

Experimental Setup

Hardware

• Modern HPC cluster configuration
• Multi-node distributed deployment
• High-performance networking
• Parallel storage systems

Workloads

• Scientific dataset vectors
• Various dimensionalities (128 to 2048)
• Different dataset sizes (millions to billions of vectors)
• Mixed read/write patterns

Findings

• Vector databases show promise on HPC platforms
• Network topology significantly impacts distributed performance
• Storage backend choice affects write performance
• Opportunities for HPC-specific optimizations identified

Implications

For HPC Centers

• Guidance on deploying vector databases
• Infrastructure recommendations
• Resource allocation strategies

For Vector Database Developers

• HPC-specific optimization opportunities
• Integration points with HPC tools
• Performance tuning recommendations

Future Research Directions

• GPU acceleration on HPC platforms
• Integration with HPC schedulers
• Multi-tenancy in HPC environments
• Optimization for scientific workflows

Conference

Presented at SC'25 (International Conference for High Performance Computing, Networking, Storage, and Analysis) Workshop, October 2025.