System-Level Dynamic Thermal Management Key Techniques Research

With the rapid development of computer manufacturing technology, more computing resources are combined into one small chip area, which makes the on-chip power density and local temperature rise sharply then leading to temperature hotspots. Uneven on-chip temperature brings many challenges for processor and its cooling system design. Meanwhile, uneven chip temperature and temperature hotspots may lead to logic errors when the processor is running and even permanent physical damage. On the other hand, the design principles of cooling system have considered the situation of highest workloads. While in most cases, the processor is not high loaded, which means that the cost of the cooling system increases in disguise. The low-power, -energy and thermal management techonologies can do online control for system temperature, which reduces the cose of cooling system design radically. It is increasingly important to design effective dynamic techniques for processors'temperature control.In this thesis, I have investigated the advantages and disadvantages between several architectural-supported thermal control methods, such as DVFS and DPM techniques, and OS-levevl resource management approaches, such as task scheduling and memory allocation. Finally, I adopt the system-level approach to control processor temperature, which is more flexiable and powerful.The main research works in this thesis include:1. Summarize the current status of computer system developments and analyze the emerging challenges faced by the processor designers in the future. Then manifest the importance and emergency for processor's temperature control. Introduce the research status and achievements for temperature control at home and broad.2. Analyze the effects to processor's temperature brought by workloads. Then characterize workloads'hot-cool feature and find opportunities for online temperature control. Propose the workload characterization approach through combining different dynamic and static parameters and characterize tasks'hot-cool features.3. Analyze the runtime feature on architectural level and propose the workload characterization approach through combining cache miss distribution and CPI and characterize tasks'hot-cool features.4. Propose a temperature-aware task scheduling approach based on a heuristic corresponding principle. Then Formulating the problem of temperature-aware task scheduling into online learning model. Online learning is one of the machine learning methods. This method takes all of the past system states into consideration to make decision. Each decision is based on a process of loss factor evaluation.5. Design Time-Slice Scaling and Alternative Scaling schemes to reduce runtime chip temperature further or shorten the time length of peak temperature.6. Design and implementation on real Linux platform.The contributions and innovations of our works include:1. Applied the machine learning theory to online thermal control and design the online learning framework, which make it theoretically garanteed.2. Combining the CPI and Cache Miss Distribution to characterize workload and achieving hot-cool characterization for tasks.3. Proposing novel Time-Slice Scaling and Alternative Scheduling schemes to reduce chip temperature further or shorten the time length of peak temperature.