Case Study
High-Performance Clustering Engine
Utah State UniversityArchitectureDistributed SystemsCUDA
Summary
Engineered a benchmarked multi-backend clustering pipeline to raise throughput on high-volume datasets.
Impact
Benchmarked parallel implementations and achieved multi-fold throughput gains for million-point datasets.
Challenge
Balancing algorithm quality, execution speed, and memory pressure across different hardware targets.
Architecture
Compute-intensive clustering with multiple execution backends (CUDA, MPI, OpenMP) behind a shared evaluation harness.
Key Decisions
Standardized benchmark inputs and instrumented each backend to compare tradeoffs objectively before selecting defaults.
Scale Considerations
Optimized memory access patterns and batching strategy to keep performance stable at higher data volumes.
Last updated: February 14, 2026