🤖 AI Summary
This study investigates the scalability and performance of process and thread schedulers under memory-intensive workloads in multi-core shared-memory systems, focusing on a 3D tensor row-sorting task. The authors design and evaluate several scheduling strategies: on the thread side, an AIMD-based adaptive chunking mechanism inspired by TCP congestion control is introduced, coupled with exponential weighted moving average to dynamically adjust concurrency; on the process side, a bounded prolific/collective model is employed alongside one-to-one, one-to-many, and many-to-many pipelined communication patterns to enable flexible task distribution. Experimental results on a 24-core x86-64 platform demonstrate that thread-level scheduling consistently outperforms process-level scheduling, with dynamic and guided strategies achieving the best performance, while the many-to-many pipeline exhibits superior scalability for large-scale tasks.
📝 Abstract
This study assesses the scalability of process-based and thread-based schedulers for many-core shared-memory systems using a memory-intensive row-wise quick-sort workload on large three-dimensional tensors. The process-based evaluation considers bounded prolific, bounded collective, and three pipe-based producer-consumer schedulers: one-to-one, one-to-many, and many-to-many. These pipe schedulers dynamically stream task identifiers to worker processes, exchanging increased inter-process communication overhead for enhanced runtime load balancing and flexible chunk-based task dispatching. The thread-based evaluation examines static, dynamic, guided, chunk-based, chunk-stealing, adaptive chunk, and AIMD adaptive scheduling strategies. The AIMD scheduler employs an additive-increase multiplicative-decrease policy inspired by TCP congestion control, utilizing an exponentially weighted moving average (EWMA) of CPU utilization to regulate a contention window that limits the number of concurrently active chunks. The adaptive chunk scheduler further modifies chunk size based on observed per-thread execution speed. Experimental results on a 24-core x86-64 platform indicate that thread schedulers deliver the highest overall performance, with dynamic and guided scheduling yielding the most favorable practical outcomes. Among process schedulers, pipe-based designs demonstrate the strongest scalability, with one-to-one pipes excelling for smaller workloads and many-to-many pipes preferred for larger workloads. In summary, lightweight thread scheduling is optimal for shared-memory row sorting, while AIMD/adaptive scheduling and pipe-based process scheduling remain valuable for contention-aware execution, explicit inter-process coordination, and distributed-style heterogeneous workload management.