2025/03/14 by Alessandro Fogli, Fogli, Alessandro, Peter Pietzuch +4 · 1 citation
Computer Science · #Cloud Computing and Resource Management #Distributed #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #FOS: Electrical engineering #Hardware Architecture (cs.AR) #Parallel #Parallel Computing and Optimization Techniques #Performance (cs.PF) #Systems and Control (eess.SY) #and Cluster Computing (cs.DC) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2503.11460
openalex publication_date 2025/03/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The growing disparity between CPU core counts and available memory bandwidth has intensified memory contention in servers. This particularly affects highly parallelizable applications, which must achieve efficient cache utilization to maintain performance as CPU core counts grow. Optimizing cache utilization, however, is complex for recent chiplet-based CPUs, whose partitioned L3 caches lead to varying latencies and bandwidths, even within a single NUMA domain. Classical NUMA optimizations and task scheduling approaches unfortunately fail to address the performance issues of chiplet-based CPUs. We describe Adaptive Runtime system for Chiplet-Aware Scheduling (ARCAS), a new runtime system designed for chiplet-based CPUs. ARCAS combines chiplet-aware task scheduling heuristics, hardware-aware memory allocation, and fine-grained performance monitoring to optimize workload execution. It implements a lightweight concurrency model that combines user-level thread features-such as individual stacks, per-task scheduling, and state management-with coroutine-like behavior, allowing tasks to suspend and resume execution at defined points while efficiently managing task migration across chiplets. Our evaluation across diverse scenarios shows ARCAS's effectiveness for optimizing the performance of memory-intensive parallel applications.