PETRAS

Abstract

Many computing systems today are heterogeneous in that they consist of a mix of different types of processing units (e.g., CPUs, GPUs). Each of these processing units has different execution capabilities and energy consumption characteristics. Job mapping and scheduling play a crucial role in such systems as they strongly affect the overall system performance, energy consumption, peak power and peak temperature. Allocating resources (e.g., core scaling, threads allocation) is another challenge since different sets of resources exhibit different behavior in terms of performance and energy consumption. Many studies have been conducted on job scheduling with an eye on performance improvement. However, few of them takes into account both performance and energy. We thus propose our novel Performance, Energy and Thermal aware Resource Allocator and Scheduler (PETRAS) which combines job mapping, core scaling, and threads allocation into one scheduler. Since job mapping and scheduling are known to be NP-hard problems, we apply an evolutionary algorithm called a Genetic Algorithm (GA) to find an efficient job schedule in terms of execution time and energy consumption, under peak power and peak temperature constraints. Experiments conducted on an actual system equipped with a multicore CPU and a GPU show that PETRAS finds efficient schedules in terms of execution time and energy consumption. Compared to performance-based GA and other schedulers, on average, PETRAS scheduler can achieve up to a 4.7x of speedup and an energy saving of up to 195%.