PkMin: Peak Power Minimization for Multi-Threaded Many-Core Applications

Abstract

Multiple multi-threaded tasks constitute a modern many-core application. An accompanying generic Directed Acyclic Graph (DAG) represents the execution precedence relationship between the tasks. The application comes with a hard deadline and high peak power consumption. Parallel execution of multiple tasks on multiple cores results in a quicker execution, but higher peak power. Peak power single-handedly determines the involved cooling costs in many-cores, while its violations could induce performance-crippling execution uncertainties. Less task parallelization, on the other hand, results in lower peak power, but a more prolonged deadline violating execution. The problem of peak power minimization in many-cores is to determine task-to-core mapping configuration in the spatio-temporal domain that minimizes the peak power consumption of an application, but ensures application still meets the deadline. All previous works on peak power minimization for many-core applications (with or without DAG) assume only single-threaded tasks. We are the first to propose a framework, called PkMin, which minimizes the peak power of many-core applications with DAG that have multi-threaded tasks. PkMin leverages the inherent convexity in the execution characteristics of multi-threaded tasks to find a configuration that satisfies the deadline, as well as minimizes peak power. Evaluation on hundreds of applications shows PkMin on average results in 49.2% lower peak power than a similar state-of-the-art framework.

Improving characteristics of LUT-based Mealy FSMs

“…The balanced decomposition allows finding a solution which maximizes advantages and minimizes disadvantages of two previous strategies (Michalski and Kokosiński, 2016). These approaches are used, for example, in the systems DEMAIN (Rawski et al, 1997) or PKmin (PKmin, 2020). Obviously, there are program tools for functional decomposition in any CAD targeting FPGA-based design.…”