Modern applications are being increasingly demanding in terms of computing capabilities, and high performance is required at all times. Chip multiprocessors (CMPs) comprise multiple cores and have been widely employed to address this demand. However, the cores of a CMP share several components of the memory hierarchy for which concurrent executing applications compete to access at run-time. This contention can lead to severe performance loss and has a different impact on each application, resulting in potential starvation for selected applications. Thus, there is a need for a scheduling policy to efficiently address this contention-induced unfairness. In this work, we utilize matrix reconstruction techniques to enhance scheduling decisions at run-time, ensuring the fair and efficient execution of any given application workload. Our evaluation shows that our proposed scheduling policy can achieve up to 25.8% gains in fairness when compared to the Linux completely fair scheduler, and up to 6.1% when compared to another state-of-the-art approach, without inflicting performance degradation.

Modern Chip Multiprocessors (CMPs) are integrating an increasing amount of cores to address the continually growing demand for high-application performance. The cores of a CMP share several components of the memory hierarchy, such as Last-Level Cache (LLC) and main memory. This allows for considerable gains in multithreaded applications while also helping to maintain architectural simplicity. However, sharing resources can also result in performance bottleneck due to contention among concurrently executing applications. In this work, we formulate a fine-grained application characterization methodology that leverages Performance Monitoring Counters (PMCs) and Cache Monitoring Technology (CMT) in Intel processors. We utilize this characterization methodology to develop two contention-aware scheduling policies, one static and one dynamic , that co-schedule applications based on their resource-interference profiles. Our approach focuses on minimizing contention on both the main-memory bandwidth and the LLC by monitoring the pressure that each application inflicts on these resources. We achieve performance benefits for diverse workloads, outperforming Linux and three state-of-the-art contention-aware schedulers in terms of system throughput and fairness for both single and multithreaded workloads. Compared with Linux, our policy achieves up to 16% greater throughput for single-threaded and up to 40% greater throughput for multithreaded applications. Additionally, the policies increase fairness by up to 65% for single-threaded and up to 130% for multithreaded ones.

Platform heterogeneity prevails as a solution to the throughput and computational challenges imposed by parallel applications and technology scaling. Specifically, Graphics Processing Units (GPUs) are based on the Single Instruction Multiple Thread (SIMT) paradigm and they can offer tremendous speedup for parallel applications. However, GPUs were designed to execute a single application at a time. In case of simultaneous multi-application execution, due to the GPUs' massive multi-threading paradigm, applications compete against each other using destructively the shared resources (caches and memory controllers) resulting in significant throughput degradation. In this paper, a methodology for minimizing interference in shared resources and provide efficient concurrent execution of multiple applications on GPUs is presented. Particularly, the proposed methodology (i) performs application classification; (ii) analyzes the per-class interference; (iii) finds the best matching between classes; and (iv) employs an efficient resource allocation. Experimental results showed that the proposed approach increases the throughput of the system for two concurrent applications by an average of 36% compared to the default execution and 10% compared to an exahustive profile-based optimization technique.

Scheduling is a decision-making process that deals with the assignment of resources to tasks over given periods, aiming to optimize one or more objectives. Responsible for efficient distribution of the CPU time among the processes, scheduler has become an essential part of computer systems. While applications run on neighboring cores of a many-core system, they compete with each other for the shared resources (cache, memory etc.). This contention can result in great performance degradation for the applications that are concurrently executed. For this reason, treating the cores of a many-core systems as isolated and independent units is a very optimistic abstraction and can cause great problems to the objectives a scheduler tries to optimize. This paper presents a scheduler that focuses on improving the system's fairness by deciding the group of applications that will be executed together based on the progress they have performed. Results shows that the proposed scheduler achieves on average 86% fairness improvement compared to two state-of-art schedulers.