Cuckoo-GPU: New CUDA Library Delivers 350x Faster Probabilistic Data Structure for High-Performance Computing
Key Takeaways
- ▸Cuckoo-GPU achieves 351x faster query performance versus CPU-based partitioned Cuckoo filters on NVIDIA GH200 hardware
- ▸Lock-free CUDA implementation supports batch insert, lookup, delete operations with configurable false positive rates and multiple eviction policies
- ▸Multi-GPU support and header-only design enable easy integration into existing high-performance computing workflows
Summary
Researchers have released Cuckoo-GPU, a high-performance CUDA implementation of the Cuckoo Filter that significantly outperforms existing probabilistic data structure alternatives on modern GPUs. The library achieves up to 351x faster query operations compared to CPU-based partitioned Cuckoo filters and demonstrates substantial speedups across insertion, lookup, and deletion operations when tested on NVIDIA's GH200 GPU.
Cuckoo-GPU is designed as a lock-free, header-only library optimized for batch operations with configurable fingerprint sizes, multiple eviction policies, and support for multi-GPU deployments via gossip protocols. The implementation includes experimental cross-process filter sharing via IPC and features optimizations like sorted insertion mode for improved memory coalescing.
Benchmark comparisons show Cuckoo-GPU consistently outperforms competing GPU implementations including Bulk Two-Choice Filters, Counting Quotient Filters, and other cuckoo hash table variants, particularly excelling at deletion operations where it achieves 108x-258x speedups. The library maintains competitive false positive rates while delivering dramatically improved throughput, making it suitable for applications requiring high-speed probabilistic membership testing at scale.
- Benchmarks demonstrate superior performance across most operations compared to competing GPU-accelerated probabilistic data structures (TCF, GQF, BCHT)
Editorial Opinion
Cuckoo-GPU represents a meaningful contribution to GPU-accelerated data structures, delivering substantial performance improvements that make probabilistic filtering viable for demanding, latency-sensitive applications. The comprehensive benchmark comparisons and open-source release position this as a valuable tool for researchers and engineers working on high-throughput systems. However, its use case specificity—excelling primarily at query and deletion operations while underperforming on insertions versus Bloom filters—suggests it will be most impactful for workloads dominated by membership lookups rather than write-heavy scenarios.
