Design and management of image processing pipelines within CPS: Acquired experience towards the end of the FitOptiVis ECSEL Project

Cyber-Physical Systems (CPSs) are dynamic and reactive systems interacting with processes, environment and, sometimes, humans. They are often distributed with sensors and actuators, characterized for being smart, adaptive, predictive and react in real-time. Indeed, image- and video-processing pipelines are a prime source for environmental information for systems allowing them to take better decisions according to what they see. Therefore, in FitOptiVis, we are developing novel methods and tools to integrate complex image- and video-processing pipelines. FitOptiVis aims to deliver a reference architecture for describing and optimizing quality and resource management for imaging and video pipelines in CPSs both at design- and run-time. The architecture is concretized in low-power, high-performance, smart components, and in methods and tools for combined design-time and run-time multi-objective optimization and adaptation within system and environment constraints

Design and management of image processing pipelines within CPS : Acquired experience towards the end of the FitOptiVis ECSEL Project

Cyber-Physical Systems (CPSs) are dynamic and reactive systems interacting with processes, environment and, sometimes, humans. They are often distributed with sensors and actuators, characterized for being smart, adaptive, predictive and react in real-time. Indeed, image- and video-processing pipelines are a prime source for environmental information for systems allowing them to take better decisions according to what they see. Therefore, in FitOptiVis, we are developing novel methods and tools to integrate complex image- and video-processing pipelines. FitOptiVis aims to deliver a reference architecture for describing and optimizing quality and resource management for imaging and video pipelines in CPSs both at design- and run-time. The architecture is concretized in low-power, high-performance, smart components, and in methods and tools for combined design-time and run-time multi-objective optimization and adaptation within system and environment constraints.Peer reviewe

Dynamic Orchestration of Massively Data Parallel Execution.

Graphics processing units (GPUs) are specialized hardware accelerators capable of rendering graphics much faster than conventional general-purpose processors. They are widely used in personal computers, tablets, mobile phones, and game consoles. Modern GPUs are not only efficient at manipulating computer graphics, but also are more effective than CPUs for algorithms where processing of large data blocks can be done in parallel. This is mainly due to their highly parallel architecture. While GPUs provide low-cost and efficient platforms for accelerating massively parallel applications, tedious performance tuning is required to maximize application execution efficiency. Achieving high performance requires the programmers to manually manage the amount of on-chip memory used per thread, the total number of threads per multiprocessor, the pattern of off-chip memory accesses, etc. In addition to a complex programming model, there is a lack of performance portability across various systems with different runtime properties. Programmers usually make assumptions about runtime properties when they write code and optimize that code based on those assumptions. However, if any of these properties changes during execution, the optimized code performs poorly. To alleviate these limitations, several implementations of the application are needed to maximize performance for different runtime properties. However, it is not practical for the programmer to write several different versions of the same code which are optimized for each individual runtime condition. In this thesis, we propose a static and dynamic compiler framework to take the burden of fine tuning different implementations of the same code off the programmer. This framework enables the programmer to write the program once and allow a static compiler to generate different versions of a data parallel application with several tuning parameters. The runtime system selects the best version and fine tunes its parameters based on runtime properties such as device configuration, input size, dependency, and data values.PhDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108805/1/mehrzads_1.pd

Efficient implementation of resource-constrained cyber-physical systems using multi-core parallelism

The quest for more performance of applications and systems became more challenging in the recent years. Especially in the cyber-physical and mobile domain, the performance requirements increased significantly. Applications, previously found in the high-performance domain, emerge in the area of resource-constrained domain. Modern heterogeneous high-performance MPSoCs provide a solid foundation to satisfy the high demand. Such systems combine general processors with specialized accelerators ranging from GPUs to machine learning chips. On the other side of the performance spectrum, the demand for small energy efficient systems exposed by modern IoT applications increased vastly. Developing efficient software for such resource-constrained multi-core systems is an error-prone, time-consuming and challenging task. This thesis provides with PA4RES a holistic semiautomatic approach to parallelize and implement applications for such platforms efficiently. Our solution supports the developer to find good trade-offs to tackle the requirements exposed by modern applications and systems. With PICO, we propose a comprehensive approach to express parallelism in sequential applications. PICO detects data dependencies and implements required synchronization automatically. Using a genetic algorithm, PICO optimizes the data synchronization. The evolutionary algorithm considers channel capacity, memory mapping, channel merging and flexibility offered by the channel implementation with respect to execution time, energy consumption and memory footprint. PICO's communication optimization phase was able to generate a speedup almost 2 or an energy improvement of 30% for certain benchmarks. The PAMONO sensor approach enables a fast detection of biological viruses using optical methods. With a sophisticated virus detection software, a real-time virus detection running on stationary computers was achieved. Within this thesis, we were able to derive a soft real-time capable virus detection running on a high-performance embedded system, commonly found in today's smart phones. This was accomplished with smart DSE algorithm which optimizes for execution time, energy consumption and detection quality. Compared to a baseline implementation, our solution achieved a speedup of 4.1 and 87\% energy savings and satisfied the soft real-time requirements. Accepting a degradation of the detection quality, which still is usable in medical context, led to a speedup of 11.1. This work provides the fundamentals for a truly mobile real-time virus detection solution. The growing demand for processing power can no longer satisfied following well-known approaches like higher frequencies. These so-called performance walls expose a serious challenge for the growing performance demand. Approximate computing is a promising approach to overcome or at least shift the performance walls by accepting a degradation in the output quality to gain improvements in other objectives. Especially for a safe integration of approximation into existing application or during the development of new approximation techniques, a method to assess the impact on the output quality is essential. With QCAPES, we provide a multi-metric assessment framework to analyze the impact of approximation. Furthermore, QCAPES provides useful insights on the impact of approximation on execution time and energy consumption. With ApproxPICO we propose an extension to PICO to consider approximate computing during the parallelization of sequential applications