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- All Subjects: power management
- Genre: Academic theses
- Creators: Chakrabarti, Chaitali
- Resource Type: Text
- Status: Published
Description
With increasing transistor volume and reducing feature size, it has become a major design constraint to reduce power consumption also. This has given rise to aggressive architectural changes for on-chip power management and rapid development to energy efficient hardware accelerators. Accordingly, the objective of this research work is to facilitate software developers to leverage these hardware techniques and improve energy efficiency of the system. To achieve this, I propose two solutions for Linux kernel: Optimal use of these architectural enhancements to achieve greater energy efficiency requires accurate modeling of processor power consumption. Though there are many models available in literature to model processor power consumption, there is a lack of such models to capture power consumption at the task-level. Task-level energy models are a requirement for an operating system (OS) to perform real-time power management as OS time multiplexes tasks to enable sharing of hardware resources. I propose a detailed design methodology for constructing an architecture agnostic task-level power model and incorporating it into a modern operating system to build an online task-level power profiler. The profiler is implemented inside the latest Linux kernel and validated for Intel Sandy Bridge processor. It has a negligible overhead of less than 1\% hardware resource consumption. The profiler power prediction was demonstrated for various application benchmarks from SPEC to PARSEC with less than 4\% error. I also demonstrate the importance of the proposed profiler for emerging architectural techniques through use case scenarios, which include heterogeneous computing and fine grained per-core DVFS. Along with architectural enhancement in general purpose processors to improve energy efficiency, hardware accelerators like Coarse Grain reconfigurable architecture (CGRA) are gaining popularity. Unlike vector processors, which rely on data parallelism, CGRA can provide greater flexibility and compiler level control making it more suitable for present SoC environment. To provide streamline development environment for CGRA, I propose a flexible framework in Linux to do design space exploration for CGRA. With accurate and flexible hardware models, fine grained integration with accurate architectural simulator, and Linux memory management and DMA support, a user can carry out limitless experiments on CGRA in full system environment.
ContributorsDesai, Digant Pareshkumar (Author) / Vrudhula, Sarma (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Wu, Carole-Jean (Committee member) / Arizona State University (Publisher)
Created2013
Description
Multicore processors have proliferated in nearly all forms of computing, from servers, desktop, to smartphones. The primary reason for this large adoption of multicore processors is due to its ability to overcome the power-wall by providing higher performance at a lower power consumption rate. With multi-cores, there is increased need for dynamic energy management (DEM), much more than for single-core processors, as DEM for multi-cores is no more a mechanism just to ensure that a processor is kept under specified temperature limits, but also a set of techniques that manage various processor controls like dynamic voltage and frequency scaling (DVFS), task migration, fan speed, etc. to achieve a stated objective. The objectives span a wide range from maximizing throughput, minimizing power consumption, reducing peak temperature, maximizing energy efficiency, maximizing processor reliability, and so on, along with much more wider constraints of temperature, power, timing, and reliability constraints. Thus DEM can be very complex and challenging to achieve. Since often times many DEMs operate together on a single processor, there is a need to unify various DEM techniques. This dissertation address such a need. In this work, a framework for DEM is proposed that provides a unifying processor model that includes processor power, thermal, timing, and reliability models, supports various DEM control mechanisms, many different objective functions along with equally diverse constraint specifications. Using the framework, a range of novel solutions is derived for instances of DEM problems, that include maximizing processor performance, energy efficiency, or minimizing power consumption, peak temperature under constraints of maximum temperature, memory reliability and task deadlines. Finally, a robust closed-loop controller to implement the above solutions on a real processor platform with a very low operational overhead is proposed. Along with the controller design, a model identification methodology for obtaining the required power and thermal models for the controller is also discussed. The controller is architecture independent and hence easily portable across many platforms. The controller has been successfully deployed on Intel Sandy Bridge processor and the use of the controller has increased the energy efficiency of the processor by over 30%
ContributorsHanumaiah, Vinay (Author) / Vrudhula, Sarma (Thesis advisor) / Chatha, Karamvir (Committee member) / Chakrabarti, Chaitali (Committee member) / Rodriguez, Armando (Committee member) / Askin, Ronald (Committee member) / Arizona State University (Publisher)
Created2013
Description
Nearly 60% of the world population uses a mobile phone, which is typically powered by a system-on-chip (SoC). While the mobile platform capabilities range widely, responsiveness, long battery life and reliability are common design concerns that are crucial to remain competitive. Consequently, state-of-the-art mobile platforms have become highly heterogeneous by combining a powerful SoC with numerous other resources, including display, memory, power management IC, battery and wireless modems. Furthermore, the SoC itself is a heterogeneous resource that integrates many processing elements, such as CPU cores, GPU, video, image, and audio processors. Therefore, CPU cores do not dominate the platform power consumption under many application scenarios.
Competitive performance requires higher operating frequency, and leads to larger power consumption. In turn, power consumption increases the junction and skin temperatures, which have adverse effects on the device reliability and user experience. As a result, allocating the power budget among the major platform resources and temperature control have become fundamental consideration for mobile platforms. Dynamic thermal and power management algorithms address this problem by putting a subset of the processing elements or shared resources to sleep states, or throttling their frequencies. However, an adhoc approach could easily cripple the performance, if it slows down the performance-critical processing element. Furthermore, mobile platforms run a wide range of applications with time varying workload characteristics, unlike early generations, which supported only limited functionality. As a result, there is a need for adaptive power and performance management approaches that consider the platform as a whole, rather than focusing on a subset. Towards this need, our specific contributions include (a) a framework to dynamically select the Pareto-optimal frequency and active cores for the heterogeneous CPUs, such as ARM big.Little architecture, (b) a dynamic power budgeting approach for allocating optimal power consumption to the CPU and GPU using performance sensitivity models for each PE, (c) an adaptive GPU frame time sensitivity prediction model to aid power management algorithms, and (d) an online learning algorithm that constructs adaptive run-time models for non-stationary workloads.
Competitive performance requires higher operating frequency, and leads to larger power consumption. In turn, power consumption increases the junction and skin temperatures, which have adverse effects on the device reliability and user experience. As a result, allocating the power budget among the major platform resources and temperature control have become fundamental consideration for mobile platforms. Dynamic thermal and power management algorithms address this problem by putting a subset of the processing elements or shared resources to sleep states, or throttling their frequencies. However, an adhoc approach could easily cripple the performance, if it slows down the performance-critical processing element. Furthermore, mobile platforms run a wide range of applications with time varying workload characteristics, unlike early generations, which supported only limited functionality. As a result, there is a need for adaptive power and performance management approaches that consider the platform as a whole, rather than focusing on a subset. Towards this need, our specific contributions include (a) a framework to dynamically select the Pareto-optimal frequency and active cores for the heterogeneous CPUs, such as ARM big.Little architecture, (b) a dynamic power budgeting approach for allocating optimal power consumption to the CPU and GPU using performance sensitivity models for each PE, (c) an adaptive GPU frame time sensitivity prediction model to aid power management algorithms, and (d) an online learning algorithm that constructs adaptive run-time models for non-stationary workloads.
ContributorsGupta, Ujjwala (Author) / Ogras, Umit Y. (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Kishinevsky, Michael (Committee member) / Dutt, Nikil (Committee member) / Arizona State University (Publisher)
Created2018
Description
Many core modern multiprocessor systems-on-chip offers tremendous power and performance
optimization opportunities by tuning thousands of potential voltage, frequency
and core configurations. Applications running on these architectures are becoming increasingly
complex. As the basic building blocks, which make up the application, change during
runtime, different configurations may become optimal with respect to power, performance
or other metrics. Identifying the optimal configuration at runtime is a daunting task due
to a large number of workloads and configurations. Therefore, there is a strong need to
evaluate the metrics of interest as a function of the supported configurations.
This thesis focuses on two different types of modern multiprocessor systems-on-chip
(SoC): Mobile heterogeneous systems and tile based Intel Xeon Phi architecture.
For mobile heterogeneous systems, this thesis presents a novel methodology that can
accurately instrument different types of applications with specific performance monitoring
calls. These calls provide a rich set of performance statistics at a basic block level while the
application runs on the target platform. The target architecture used for this work (Odroid
XU3) is capable of running at 4940 different frequency and core combinations. With the
help of instrumented application vast amount of characterization data is collected that provides
details about performance, power and CPU state at every instrumented basic block
across 19 different types of applications. The vast amount of data collected has enabled
two runtime schemes. The first work provides a methodology to find optimal configurations
in heterogeneous architecture using classifiers and demonstrates an average increase
of 93%, 81% and 6% in performance per watt compared to the interactive, ondemand and
powersave governors, respectively. The second work using same data shows a novel imitation
learning framework for dynamically controlling the type, number, and the frequencies
of active cores to achieve an average of 109% PPW improvement compared to the default
governors.
This work also presents how to accurately profile tile based Intel Xeon Phi architecture
while training different types of neural networks using open image dataset on deep learning
framework. The data collected allows deep exploratory analysis. It also showcases how
different hardware parameters affect performance of Xeon Phi.
optimization opportunities by tuning thousands of potential voltage, frequency
and core configurations. Applications running on these architectures are becoming increasingly
complex. As the basic building blocks, which make up the application, change during
runtime, different configurations may become optimal with respect to power, performance
or other metrics. Identifying the optimal configuration at runtime is a daunting task due
to a large number of workloads and configurations. Therefore, there is a strong need to
evaluate the metrics of interest as a function of the supported configurations.
This thesis focuses on two different types of modern multiprocessor systems-on-chip
(SoC): Mobile heterogeneous systems and tile based Intel Xeon Phi architecture.
For mobile heterogeneous systems, this thesis presents a novel methodology that can
accurately instrument different types of applications with specific performance monitoring
calls. These calls provide a rich set of performance statistics at a basic block level while the
application runs on the target platform. The target architecture used for this work (Odroid
XU3) is capable of running at 4940 different frequency and core combinations. With the
help of instrumented application vast amount of characterization data is collected that provides
details about performance, power and CPU state at every instrumented basic block
across 19 different types of applications. The vast amount of data collected has enabled
two runtime schemes. The first work provides a methodology to find optimal configurations
in heterogeneous architecture using classifiers and demonstrates an average increase
of 93%, 81% and 6% in performance per watt compared to the interactive, ondemand and
powersave governors, respectively. The second work using same data shows a novel imitation
learning framework for dynamically controlling the type, number, and the frequencies
of active cores to achieve an average of 109% PPW improvement compared to the default
governors.
This work also presents how to accurately profile tile based Intel Xeon Phi architecture
while training different types of neural networks using open image dataset on deep learning
framework. The data collected allows deep exploratory analysis. It also showcases how
different hardware parameters affect performance of Xeon Phi.
ContributorsPatil, Chetan Arvind (Author) / Ogras, Umit Y. (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2019