KUZIE CHEN

A high-performance, lock-free memory pool allocator designed for efficient memory management in C++. It is optimized for scenarios with frequent allocations and deallocations of small objects, especially in multi-threaded environments.

• Developed a flexible memory bucket system that dynamically selects optimal pools based on requested object size, significantly reducing allocation time compared to standard operator new() by 500% at least.
• By aligning memory padding for each allocation, cache hit rate is improved.
• Using lock-free concurrent programming and atomic operations to replace mutex lock, reduces lock contention and improves concurrency performance.
• Included comprehensive benchmarking and test suites to demonstrate performance gains and reliability across various data size.

A high-concurrency, thread-safe cache system written in C++. It supports multiple cache replacement strategies including LRU, LFU, and ARC, aiming to improve responsiveness and hit rate under concurrent access.

Currently transforming into a distributed architecture featuring consistent hashing for load balancing, gRPC inter-node communication, and etcd service discovery to create a comprehensive cache solution spanning from memory optimization to distributed systems.

• Implemented multiple cache replacement strategies tailored to diverse access patterns.
• Introduced cache sharding for LRU and LFU to reduce lock contention and boost performance under high concurrency.
• Enhanced LRU with LRU-k to prevent cache pollution by avoiding hot data replacement with cold entries.
• Improved LFU by applying a maximum average access frequency to evict stale hot data and boost overall efficiency.
• Developed the ARC to dynamically balance LRU and LFU weights and enhance hit rates in complex conditions.

• Research and adapt the Conflict-Based Search, Space-Time A* search and multi-agent search to solve a new form of multi-agent pathfinding problem in C++.
• Developed automation bash script to convert the result of the C++ pathfinding algorithm into a Python Matplot program which dynamically display routes for all the agents and the map.

Participated in a summer school focused on automated planning and scheduling, learning about state-of-the-art techniques and applications.

• Developed planning models using PDDL (Planning Domain Definition Language)
• Implemented and tested planning algorithms