🤖 AI Summary
Existing user-space coroutine/fiber synchronization mechanisms implicitly assume kernel scheduling, introducing unnecessary latency on critical paths and limiting high-concurrency throughput. This paper proposes Combine-and-Exchange Scheduling (CES), a novel synchronization paradigm for purely user-space cooperative scheduling. CES eliminates cross-thread overhead by retaining critical sections on the same thread during lock contention, while dynamically redistributing parallelizable tasks to idle threads. Crucially, it co-designs user-space synchronization primitives with the scheduler to fully bypass kernel intervention. Experimental evaluation demonstrates that CES achieves up to 3× higher throughput on application-level benchmarks and up to 8× speedup on microbenchmarks—significantly outperforming state-of-the-art user-space synchronization approaches.
📝 Abstract
Coroutines are experiencing a renaissance as many modern programming languages support the use of cooperative multitasking for highly parallel or asynchronous applications. One of the greatest advantages of this is that concurrency and synchronization is manged entirely in the userspace, omitting heavy-weight system calls. However, we find that state-of-the-art userspace synchronization primitives approach synchronization in the userspace from the perspective of kernel-level scheduling. This introduces unnecessary delays on the critical path of the application, limiting throughput. In this paper, we re-think synchronization for tasks that are scheduled entirely in the userspace (e.g., coroutines, fibers, etc.). We develop Combine-and-Exchange Scheduling (CES), a novel scheduling approach that ensures contended critical sections stay on the same thread of execution while parallelizable work is evenly spread across the remaining threads. We show that our approach can be applied to many existing languages and libraries, resulting in 3-fold performance improvements in application benchmarks as well as 8-fold performance improvements in microbenchmarks.