MLB: A Memory-aware Load Balancing for Mitigating Memory Contention

Most of the current CPUs have not single cores, but multicores integrated in the Symmetric MultiProcessing (SMP) architecture, which share the resources such as Last Level Cache (LLC) and Integrated Memory Controller (IMC). On the SMP platforms, the contention for the resources may lead to huge performance degradation. To mitigate the contention, various methods were developed and used; most of these methods focus on finding which tasks share the same resource assuming that a task is the sole owner of a CPU core. However, less attention has been paid to the methods considering the multitasking case. To mitigate contention for memory subsystems, we have devised a new load balancing method, Memory-aware Load Balancing (MLB). MLB dynamically recognizes contention by using simple contention models and performs inter-core task migration. We have evaluated MLB on an Intel i7-2600 and a Xeon E5-2690, and found that our approach can be effectively taken in an adaptive manner, leading to noticeable performance improvements of memory intensive tasks on the different CPU platforms. Also, we have compared MLB with the state of the art method in the multitasking case, Vector Balancing & Sorted Co-scheduling (VBSC), finding out that MLB can lead to performance improvements compared with VBSC without the modification of timeslice mechanism and is more effective in allowing I/O bound applications to be performed. Also, it can effectively handle the worst case where many memory intensive tasks are co-scheduled when non memory intensive ones terminate in contrast to VBSC. In addition, MLB can achieve performance improvements in CPU-GPU communication in discrete GPU systems.