On multicore systems, contention for shared resources occurs when memory-intensive threads are co-scheduled on cores that share parts of the memory hierarchy, such as last-level caches and memory controllers. Previous work investigated how contention could be addressed via scheduling. A contention-aware scheduler separates competing threads onto separate memory hierarchy domains to eliminate resource sharing and, as a consequence, to mitigate contention. However, all previous work on contention-aware scheduling assumed that the underlying system is UMA (uniform memory access latencies, single memory controller). Modern multicore systems, however, are NUMA, which means that they feature non-uniform memory access latencies and multiple memory controllers.We discovered that state-of-the-art contention management algorithms fail to be effective on NUMA systems and may even hurt performance relative to a default OS scheduler. In this paper we investigate the causes for this behavior and design the first contention-aware algorithm for NUMA systems.
Asymmetric multicore processors (AMP) consist of cores exposing the same instruction-set architecture (ISA) but varying in size, frequency, power consumption and performance. AMPs were shown to be more power efficient than conventional symmetric multicore processors, and it is therefore likely that future multicore systems will include cores of different types. AMPs derive their efficiency from core specialization: instruction streams can be assigned to run on the cores best suited to their demands for architectural resources. System efficiency is improved as a result. To perform effective matching of threads to cores, the thread scheduler must be asymmetry-aware; and while asymmetry-aware schedulers for operating systems are a well studied topic, asymmetry-awareness in hypervisors has not been addressed. A hypervisor must be asymmetry-aware to enable proper functioning of asymmetry-aware guest operating systems; otherwise they will be ineffective in virtual environments. Furthermore, a hypervisor must ensure that asymmetric cores are shared among multiple guests in a fair fashion or in accordance with their priorities. This work for the first time implements simple changes to the hypervisor scheduler, required to make it asymmetry-aware, and evaluates the benefits and overheads of these asymmetry-aware mechanisms. Our evaluation was performed using an open source hypervisor Xen on a real multicore system where asymmetry was emulated via CPU frequency scaling. We compared the asymmetry-aware hypervisor to default Xen. Our results indicate that asymmetry support can be implemented with low overheads, and resulting performance improvements can be significant, reaching up to 36% in our experiments. Most performance improvements are derived from the fact that an asymmetry-aware hypervisor ensures that the fast cores do not go idle before slow cores and from the fact that it maps virtual cores to physical cores for asymmetry-aware guests according to the guest's expectations. Other benefits from asymmetry awareness are fairer sharing of computing resources among VMs and more stable execution times.
Asymmetric multicore processors (AMP) consist of cores exposing the same instruction-set architecture (ISA) but varying in size, frequency, power consumption and performance. AMPs were shown to be more power efficient than conventional symmetric multicore processors, and it is therefore likely that future multicore systems will include cores of different types. AMPs derive their efficiency from core specialization: instruction streams can be assigned to run on the cores best suited to their demands for architectural resources. System efficiency is improved as a result. To perform effective matching of threads to cores, the thread scheduler must be asymmetry-aware; and while asymmetry-aware schedulers for operating systems are a well studied topic, asymmetry-awareness in hypervisors has not been addressed. A hypervisor must be asymmetry-aware to enable proper functioning of asymmetryaware guest operating systems; otherwise they will be ineffective in virtual environments. Furthermore, a hypervisor must ensure that asymmetric cores are shared among multiple guests in a fair fashion or in accordance with their priorities.This work for the first time implements simple changes to the hypervisor scheduler, required to make it asymmetry-aware, and evaluates the benefits and overheads of these asymmetryaware mechanisms. Our evaluation was performed using an open source hypervisor Xen on a real multicore system where asymmetry was emulated via CPU frequency scaling. We compared the asymmetry-aware hypervisor to default Xen. Our results indicate that asymmetry support can be implemented with low overheads, and resulting performance improvements can be significant, reaching up to 36% in our experiments. Most performance improvements are derived from the fact that an asymmetry-aware hypervisor ensures that the fast cores do not go idle before slow cores and from the fact that it maps virtual cores to physical cores for asymmetryaware guests according to the guest's expectations. Other benefits from asymmetry awareness are fairer sharing of computing resources among VMs and more stable execution times.
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