FRAME: Fair Resource Allocation in Multi-process Environments

As the technology trend moves toward manufacturing many-core systems with hundreds of processing cores, the problem of efficiently managing multiple parallel jobs on such massively parallel systems becomes increasingly important. With traditional time-sharing each process assumes it is the only running process. This assumption can easily lead to system oversubscription and thereby, losing overall performance due to frequent context switches. Space-sharing techniques allocate a certain number of hardware cores to each process and by that malleable processes can set their parallelism level to their allocated number of cores, hence avoiding oversubscription. However, finding the optimal spatial allocation is not a trivial task. In this paper we propose FRAME, a resource allocation technique to maximize a system´s overall utility and fairness, running multiple malleable processes with CPU-bound workloads. First, we formalize the resource allocation problem as an NP-hard problem. Then, we use approximation techniques and convex optimization theory to find the optimal solution to the formulated problem, in pseudo-polynomial time. Our evaluation results show that our method is very fast and efficient in finding the optimal solution to the resource allocation problem. Also, the results suggests that the found solution increases the system´s overall utility by 48%, in average, with regard to the best alternative allocation policy.