Show HN: HashSmith – High-performance open-addressing hash tables for Java (github.com)

HashSmith has introduced high-performance open-addressing hash tables targeted at Java developers, specifically optimized for the Java Virtual Machine (JVM). Featuring implementations like SwissMap and RobinHoodMap, this library leverages advanced techniques such as SIMD-assisted probing and robust statistical methods to enhance memory efficiency and speed. SwissMap, inspired by Google's SwissTable, employs the incubating Vector API from JDK 21+ to facilitate SIMD acceleration, while RobinHoodMap utilizes backward-shift deletion to ensure predictable probe lengths and reduce per-entry overhead. This release is significant for the AI/ML community as efficient hash tables can drastically impact performance in data-heavy applications, including various machine learning algorithms. HashSmith outperforms the built-in JDK HashMap, particularly in scenarios with lighter payloads, potentially reducing memory usage by up to 43.7%. The library comes with comprehensive documentation and benchmarking capabilities, allowing developers to easily integrate these optimized data structures into their applications and assess their performance under different workloads. Integrating HashSmith could streamline data operations in AI/ML projects, enhancing overall system efficiency.