Runtime Support for Heterogeneous Multi-core Systems

Multi-core processing platforms are one of the major steps forward in offering high-performance computing platforms. The idea is to increase the performance by employing more processing elements to perform a job. However, this creates a challenge for both hardware developers who build such systems and software designers who program those platforms. On the hardware side, we can mention the problems on the interconnects management, memory hierarchies complexities and cache coherency problem. While on the software side, problems mainly arise in resource management, resource sharing and synchronization. One more fundamental problem on the software side is the inability to program such platforms with the conventional programming models. This is mainly because programming such platforms requires in-depth knowledge of hardware design. In this dissertation, we address the software side problems by proposing a comprehensive runtime system which is responsible to manage the system resources and resolve all the conflicting issues when accessing computing resources. Furthermore, the runtime system offers the application developers with APIs and system primitives that abstract away the platform dependent details, and provides a consistent programming model. These primitives decouple the process of software development from hardware design and results in the software to be independent of the underlying hardware platform. The proposed runtime system consists of a scheduler, a profiler, a transformer, a JIT compiler and a kernel library. A detailed description of each component is presented and the performance of the whole system as well as the imposed overhead of the component is discussed.Microelectronics & Computer EngineeringElectrical Engineering, Mathematics and Computer Scienc

VMODEX: a novel visualization tool for rapid analysis of heuristic-based multi-objective design space exploration of heterogeneous MPSoC architectures

System-level computer architecture simulations create large volumes of simulation data to explore alternative architectural solutions. Interpreting and drawing conclusions from this amount of simulation results can be extremely cumbersome. In other domains that also struggle with interpreting large volumes of data, such as scientific computing, data visualization is an invaluable tool. Such visualization is often domain specific and has not become widely studied and utilized for evaluating the results of computer architecture simulations. In this paper, we introduce our novel interactive visualizationtool, called VMODEX, which is developed to support system-level designspaceexploration of MPSoCarchitectures. In our tool, the designspace is modeled as a tree in which both the design parameters and criteria are shown in a single view. VMODEX is able to handle large designspaces and allows designers to look at the data from different perspectives and at multiple levels of abstraction. Due to the exponential designspace in real problems and multiple criteria to be considered, heuristic searching algorithms are often used to trim down a large designspace into a finite set of points and provide the designer a set of tradable solutions with respect to the design criteria. In VMODEX, besides the techniques provided for visualizing the DSE results, additional capabilities are developed to understand the dynamic search behavior of heuristic searching algorithms

Extensions of the hArtes Tool Chain

In this chapter, we describe functionality which has also been developed in the context of the hArtes project but that were not included in the final release or that are separately released. The development of the tools described here was often initiated after certain limitations of the current toolset were identified. This was the case of the memory analyser QUAD which does a detailed analysis of the memory accesses. Other tools, such as the rSesame tool, were developed and explored in parallel with the hArtes tool chain. This tool assumes a KPN-version of the application and then allows for high level simulation and experimentation with different mappings and partitionings. Finally, ReSP was developed to validate the partitioning results before a real implementation was possible

A preliminary study on poultry farm environmental monitoring using internet of things and blockchain technology

This paper presents the application of Internet of Things (IoT) and blockchain technology in poultry industry. Production of poultry is highly depending on the environment parameter like temperature, humidity, air and lighting to maintain and to boost the production of poultry. Monitoring all the environmental parameters is crucial for a large poultry farm, especially in traditional way. Recently, the adoption of IoT and Blockchain is used for monitoring and controlling the farm automatically. It is proven that it can reduce cost and the poultry farm well-managed. At first, the data of temperature are monitored using IoT-based temperature and humidity sensor and recorded in JSON format. From the results, the data is successfully transmitted to IOTA blockchain. Blockchain will ensure the data is secured and transparent to consumers