Contention and Locality-Aware Work-Stealing for Iterative Applications in Multi-Socket Computers
Quan Chen, Minyi Guo · IEEE Transactions on Computers · 2017
Modern large-scale computers have shifted to Multi-socket Multi-core (MSMC) architectures, where multiple CPU chips are integrated into a machine as sockets and multiple memory nodes are integrated into the shared main memory (NUMA). To improve the hardware utilization of MSMC computers, multiple programs are often executed concurrently. However, most work-stealing schedulers are designed for single-socket architectures and contention-free scenarios. Work-stealing programs sufferfrom very-high-frequency remote memory access and serious interference from co-located programs in MSMC architectures, which in turn significantly degrade their performance. To solve these two problems, we propose a Contentionand Locality-Aware Work-Stealing (CLAWS) scheduler. CLAWS first evenly distributes the data set of a program to all the memory nodes and allocates a task to the socket where the local memory node stores its data. Then, according to the real-time contention situation for each socket collected at runtime, CLAWS dynamically migrates data and re-allocates the corresponding tasks to balance the workload and reduce remote memory accesses. Experimental results show that CLAWS can improve the performance of memory-bound programs by 40.1 percent on average compared with traditional work-stealing schedulers. Meanwhile, CLAWS is also more energy efficient than traditional work-stealing schedulers.