Please note: This master’s thesis presentation will take place in DC 1304 and online.
Armin Afsharian, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Martin Karsten
Large language models (LLMs) face fundamental challenges in symbolic reasoning, struggling with tasks requiring precise rule-following, logical consistency, and manipulation of structured representations. This thesis introduces a comprehensive neurosymbolic framework that addresses these limitations by iHigh-performance network servers depend on efficient I/O mechanisms to manage thousands of concurrent connections with minimal latency and overhead.
While traditional readiness-based interfaces (e.g., select, poll, epoll) notify applications when I/O operations can proceed, they still require synchronous system calls to execute the operations. This synchronous requirement causes frequent user–kernel transitions, which limits scalability under heavy load. In contrast, the io uring interface offers a fundamentally different approach by providing a completion-based I/O model that minimizes system call overhead and enables true asynchronous data transfer. Although the performance benefits of io uring are well established in storage systems, its integration into high-throughput network applications remains limited.
This thesis aims to bridge this integration gap by making the adoption of io uring accessible and providing a structured vehicle for evaluating its performance in network-bound environments. To this end, the io uring Convergence Layer (UringCL) is presented to transparently map the synchronous I/O calls of readiness-driven applications onto asynchronous io uring operations. The UringCL simplifies initialization, event handling, and data transfer while preserving the existing control flow of legacy applications, allowing for incremental migration toward completion-based I/O without major redesign. The UringCL architecture facilitates the practical integration of io during into established network architectures and provides a consistent framework for measuring its impact on throughput, latency, and CPU efficiency. Experimental results demonstrate significant performance advantages over traditional models. In bulk transfer workloads, the system delivers up to 40% higher throughput than epoll due to superior batching capabilities. In request-response scenarios involving Memcached, the integration achieves higher peak throughput and maintains significantly lower and more stable tail latency under heavy load. Furthermore, UringCL achieves these benefits with negligible overhead, proving that completion-based I/O can be adopted seamlessly to enhance the efficiency of modern network servers.
To attend this master’s thesis presentation:
- In person: DC 1304
- Online: Zoom