Online thermal control methods for multiprocessor systems

Abstract

With technological advances, the number of cores integrated on a chip is increasing. This in turn is leading to thermal constraints and thermal design challenges. Temperature gradients and hotspots not only affect the performance of the system but also lead to unreliable circuit operation and affect the lifetime of the chip. Meeting temperature constraints and reducing hotspots are critical for achieving reliable and efficient operation of complex multi-core systems.In this article, we analyze the use of four of the most promising families of online control techniques for thermal management of multiprocessors system-on-chip (MPSoC). In particular, in our exploration, we aim at achieving an online smooth thermal control action that minimizes the performance loss as well as the computational and hardware overhead of embedding a thermal management system inside the MPSoC. The definition of the optimization problem to tackle in this work considers the thermal profile of the system, its evolution over time, and current time-varying workload requirements. Thus, this problem is formulated as a finite-horizon optimal control problem, and we analyze the control features of different online thermal control approaches. In addition, we implemented the policies on an MPSoC hardware simulation platform and performed experiments on a cycle-accurate model of the eight-core Niagara multi-core architecture using benchmarks ranging from Web-accessing to playing multimedia. Results show different trade-offs among the analyzed techniques regarding the thermal profile, the frequency setting, the power consumption, and the implementation complexity.

A Scalable Framework for Online Power Modelling of High-Performance Computing Nodes in Production

“…To solve these issues, several works in the literature [4], [5], [6], [7], [8] propose to take advantage of proactive thermal and power management strategies. These strategies all rely on the availability of compact predictive power models, capable of predicting future power consumption and, even more importantly to build a clear understanding on the sensitivity of power consumption on workload parameters and hardware knobs that can be controlled at run time.…”

Robust Identification of Thermal Models for In-Production High-Performance-Computing Clusters With Machine Learning-Based Data Selection

“…To solve these issues, several works in the literature [4], [5], [6], [7], [8], [9], [10] propose to take advantage of proactive thermal and power management strategies. These strategies all rely on the availability of compact predictive power and thermal models, capable of predicting future power consumption and temperature of the system and, even more importantly, to build a clear understanding on the sensitivity of these on workload parameters and hardware knobs that can be controlled at run time.…”

“…Compact models can be used in combination with optimization and artificial intelligence techniques to select in a robust fashion the optimal operating points from the target power and temperature and the current conditions [4], [5], [6], [7], [8]. Moreover, such compact models can be used also for detecting anomalous changes in the behaviour of the system, for example due to a failure.…”

Modeling and mitigation of parametric time-dependent variability in digital systems

“…Before the die is marketed, available techniques can be further split between non-intrusive [238,153,165] and intrusive [80,206,70]. Reliability techniques may take effect after the die is marketed and can be split between reactive [291,216,180] and proactive [180,282].…”

“…Pre-Fab Design Related Design Optimization [126,10], [286] Capability Addition [209,284] Process Technology Materials [82,266] Lithography [286], [205,285] Post-Fab Before Market Non-Intrusive [238,153,165] Intrusive [80,206,70] After Market Reactive [291,216], [180] Chapters 5 and 6…”