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.