Energy and Peak Power Efficiency Analysis for the Single Voltage Approximation (SVA) Scheme

Abstract

Energy-efficiency is an important issue in computing systems, and operating within a safe power budget is a necessary constraint. This paper presents a simple and practical solution both for energy minimization and peak power reduction, called Single Voltage Approximation (SVA) scheme, for periodic realtime tasks on multi-core systems with a shared supply voltage in a voltage island. SVA is inspired by the Single Frequency Approximation (SFA) scheme, in which all the cores in the island run at a single voltage and frequency such that all tasks can meet their deadlines. In SVA, all the cores in the island are also executed at the same single voltage as in SFA. However, the frequency of each core is individually chosen, such that the tasks in each core can meet their deadlines, but without running at unnecessarily high frequencies. Thus, all the cores are executing tasks all the time, and there is no need for any Dynamic Power Management (DPM) technique for reducing the energy consumption for idling. For task partitioning, SVA is combined with the Double Largest Task First (DLTF) partitioning scheme.Most importantly, this paper provides comprehensive analysis for combining DLTF and SVA, deriving its worst-case behavior both for energy minimization and peak power reduction, compared against the optimal solutions. Our analysis shows that, depending on the hardware, the energy consumption by combining DLTF and SVA is at most 1.95 (2.21, 2.42, 2.59, respectively), compared to the optimal solutions, when the voltage island has up to 4 (8, 16, 32, respectively) cores, which outperforms the worst-case factors of SFA when the cores fail to sleep efficiently. For peak power reduction, due to running at slower frequencies, combining DLTF and SVA always outperforms SFA, both in average and corner cases. Finally, we extend our analysis considering multi-core systems with discrete voltage and frequency pairs, and multiple voltage islands.

DELICIOUS: Deadline-Aware Approximate Computing in Cache-Conscious Multicore

“…Minimizing energy in recent CMP based real-time systems has become a topic of paramount importance [38], [39]. Scheduling time-critical dependent tasks on CMP platform while maintaining the energy/power constraint is gradually becoming challenging with technology scaling [22].…”

ACCURATE: Accuracy Maximization for Real-Time Multicore Systems With Energy-Efficient Way-Sharing Caches

“…Now-a-days, energy minimization in contemporary multiprocessor embedded systems has become a topic of paramount importance [11], [12]. Energy efficient scheduling for the timecritical tasks, with precedence constraints on multiprocessor platform, imposes significant research challenges [13], [14].…”