Performance evaluation of scheduling tasks in many-core systems utilizing processes and threads

This study evaluates the scalability of process-based and thread-based schedulers for many-core systems using a memory-intensive quick-sort workload on large tensors. The method compares bounded prolific, bounded collective, and three pipe-based producer-consumer schedulers for processes, alongside static, dynamic, guided, chunk-based, chunk-stealing, adaptive chunk, and AIMD adaptive scheduling strategies for threads. Experimental results on a 24-core x86-64 platform show that thread schedulers, particularly dynamic and guided, deliver the highest overall performance, while pipe-based process schedulers demonstrate strong scalability with one-to-one pipes excelling for smaller workloads and many-to-many pipes for larger workloads. This matters because it identifies lightweight thread scheduling as optimal for shared-memory row sorting, while AIMD/adaptive and pipe-based schedulers remain valuable for contention-aware execution and distributed-style heterogeneous workload management.