A Comparative Xeon and CBE Performance Analysis

The Cell Broadband Engine is a high performance multicore processor with superb performance on certain types of problems. However, it does not perform as well running other algorithms, particularly those with heavy branching. The Intel Xeon processor is a high performance superscalar processor. It utilizes a high clock speed and deep pipelines to help it achieve superior performance. But deep pipelines can perform poorly with frequent memory accesses. This paper is a study and attempt at quantifying the types of programmatic structures that are more suitable to a particular architecture. It focuses on the issues of pipelines, memory access and branching on these two microprocessor architectures

Microprocessors: the engines of the digital age

The microprocessor—a computer central processing unit integrated onto a single microchip—has come to dominate computing across all of its scales from the tiniest consumer appliance to the largest supercomputer. This dominance has taken decades to achieve, but an irresistible logic made the ultimate outcome inevitable. The objectives of this Perspective paper are to offer a brief history of the development of the microprocessor and to answer questions such as: where did the microprocessor come from, where is it now, and where might it go in the future

Investigation of parallel programming on heterogeneous multiprocessors

Multi-core processors have become ordinary in modern commodity computers. Computationally intensive applications, like video processing, that previously only ran on specialized hardware, are now common on home computers. However, the demand for more computing power is ever-increasing, and with the introduction of high definition video, more performance is desired. As an alternative to having multiple identical processor cores, heterogeneous multiprocessors have cores with different capabilities. This allows tasks to be processed on simple cores with specialized functionality. The simplicity furthers low power consumption, small die usage, and low price. Dealing with heterogeneous cores increases the complexity of writing programs for the architecture. The reasons for this includes different capabilities of the cores, and some heterogeneous architectures do not have shared memory. Without shared memory, accessing main memory requires explicit transfers to local memory. In this thesis, we consider two architectures, the STI Cell/B.E. and Intel IXP2400, and evaluate parallelization strategies and performance for real-world problems. Our tests show promising throughput for some applications, and we propose a scheme for offloading computationally intensive parts of an existing application

Survey of Autonomic Computing and Experiments on JMX-based Autonomic Features

Autonomic Computing (AC) aims at solving the problem of managing the rapidly-growing complexity of Information Technology systems, by creating self-managing systems. In this thesis, we have surveyed the progress of the AC field, and studied the requirements, models and architectures of AC. The commonly recognized AC requirements are four properties - self-configuring, self-healing, self-optimizing, and self-protecting. The recommended software architecture is the MAPE-K model containing four modules, namely - monitor, analyze, plan and execute, as well as the knowledge repository. In the modern software marketplace, Java Management Extensions (JMX) has facilitated one function of the AC requirements - monitoring. Using JMX, we implemented a package that attempts to assist programming for AC features including socket management, logging, and recovery of distributed computation. In the experiments, we have not only realized the powerful Java capabilities that are unknown to many educators, we also illustrated the feasibility of learning AC in senior computer science courses

ANALYTICAL MODEL FOR CHIP MULTIPROCESSOR MEMORY HIERARCHY DESIGN AND MAMAGEMENT

Continued advances in circuit integration technology has ushered in the era of chip multiprocessor (CMP) architectures as further scaling of the performance of conventional wide-issue superscalar processor architectures remains hard and costly. CMP architectures take advantageof Moore¡¯s Law by integrating more cores in a given chip area rather than a single fastyet larger core. They achieve higher performance with multithreaded workloads. However,CMP architectures pose many new memory hierarchy design and management problems thatmust be addressed. For example, how many cores and how much cache capacity must weintegrate in a single chip to obtain the best throughput possible? Which is more effective,allocating more cache capacity or memory bandwidth to a program?This thesis research develops simple yet powerful analytical models to study two newmemory hierarchy design and resource management problems for CMPs. First, we considerthe chip area allocation problem to maximize the chip throughput. Our model focuses onthe trade-off between the number of cores, cache capacity, and cache management strategies.We find that different cache management schemes demand different area allocation to coresand cache to achieve their maximum performance. Second, we analyze the effect of cachecapacity partitioning on the bandwidth requirement of a given program. Furthermore, ourmodel considers how bandwidth allocation to different co-scheduled programs will affect theindividual programs¡¯ performance. Since the CMP design space is large and simulating only one design point of the designspace under various workloads would be extremely time-consuming, the conventionalsimulation-based research approach quickly becomes ineffective. We anticipate that ouranalytical models will provide practical tools to CMP designers and correctly guide theirdesign efforts at an early design stage. Furthermore, our models will allow them to betterunderstand potentially complex interactions among key design parameters

CONSERVE: A framework for the selection of techniques for monitoring containers security

Context:\ua0Container-based virtualization is gaining popularity in different domains, as it supports continuous development and improves the efficiency and reliability of run-time environments.\ua0Problem:\ua0Different techniques are proposed for monitoring the security of containers. However, there are no guidelines supporting the selection of suitable techniques for the tasks at hand.\ua0Objective:\ua0We aim to support the selection and design of techniques for monitoring container-based virtualization environments.\ua0Approach: First, we review the literature and identify techniques for monitoring containerized environments. Second, we classify these techniques according to a set of categories, such as technical characteristic, applicability, effectiveness, and evaluation. We further detail the pros and cons that are associated with each of the identified techniques.\ua0Result:\ua0As a result, we present CONSERVE, a multi-dimensional decision support framework for an informed and optimal selection of a suitable set of container monitoring techniques to be implemented in different application domains.\ua0Evaluation:\ua0A mix of eighteen researchers and practitioners evaluated the ease of use, understandability, usefulness, efficiency, applicability, and completeness of the framework. The evaluation shows a high level of interest, and points out to potential benefits

Energy Efficiency Analysis And Optimization For Mobile Platforms

The introduction of mobile devices changed the landscape of computing. Gradually, these devices are replacing traditional personal computer (PCs) to become the devices of choice for entertainment, connectivity, and productivity. There are currently at least 45.5 million people in the United States who own a mobile device, and that number is expected to increase to 1.5 billion by 2015. Users of mobile devices expect and mandate that their mobile devices have maximized performance while consuming minimal possible power. However, due to the battery size constraints, the amount of energy stored in these devices is limited and is only growing by 5% annually. As a result, we focused in this dissertation on energy efficiency analysis and optimization for mobile platforms. We specifically developed SoftPowerMon, a tool that can power profile Android platforms in order to expose the power consumption behavior of the CPU. We also performed an extensive set of case studies in order to determine energy inefficiencies of mobile applications. Through our case studies, we were able to propose optimization techniques in order to increase the energy efficiency of mobile devices and proposed guidelines for energy-efficient application development. In addition, we developed BatteryExtender, an adaptive user-guided tool for power management of mobile devices. The tool enables users to extend battery life on demand for a specific duration until a particular task is completed. Moreover, we examined the power consumption of System-on-Chips (SoCs) and observed the impact on the energy efficiency in the event of offloading tasks from the CPU to the specialized custom engines. Based on our case studies, we were able to demonstrate that current software-based power profiling techniques for SoCs can have an error rate close to 12%, which needs to be addressed in order to be able to optimize the energy consumption of the SoC. Finally, we summarize our contributions and outline possible direction for future research in this field

Activating supply chain business models' value potentials through Systems Engineering

New business opportunities, driven by smart digitalization technology and initiatives such as Industry 4.0, significantly change business models and their innovation rate. The complexity of methodologies developed in recent decades for balancing exploration and exploitation activities of digital transformation has risen. Still, the desired integration levels across organizational levels were often not reached. Systems thinking promises to holistically consider interdisciplinary relationships and objectives of various stakeholders across supply chain ecosystems. Systems theory-based concepts can simultaneously improve value identification and aligned transformation among supply networks' organizational and technical domains. Hence, the study proposes synthesizing management science concepts such as strategic alignment with enterprise architecture concepts and artificial intelligence (AI)-driven business process optimization to increase innovation productivity and master the increasing rate of business dynamics at the same time. Based on a critical review, the study explores concepts for innovation, transformation, and alignment in the context of Industry 4.0. The essence has been compiled into a systems engineering-driven framework for agile value generation on operational processes and high-order capability levels. The approach improves visibility for orchestrating sustainable value flows and transformation activities by considering the ambidexterity of exploring and exploiting activities and the viability of supply chain systems and sub-systems. Finally, the study demonstrates the need to harmonize these concepts into a concise methodology and taxonomy for digital supply chain engineering.OA-hybri

Detecting Communities and Analysing Interactions with Learning Objects in Online Learning Repositories

The widespread use of online learning object repositories has raised the need of studies that assess the quality of their contents, and their user’s performance and engagement. The present research addresses two fundamental problems that are central to that need: the need to explore user interaction with these repositories and the detection of emergent communities of users. The current dissertation approaches those directions through investigating and mining the Khan Academy repository as a free, open access, popular online learning repository addressing a wide content scope. It includes large numbers of different learning objects such as instructional videos, articles, and exercises. In addition to a large number of users. Data was collected using the repository’s public application programming interfaces combined with Web scraping techniques to gather data and user interactions. Different research activities were carried out to generate useful insights out of the gathered data. We conducted descriptive analysis to investigate the learning repository and its core features such as growth rate, popularity, and geographical distribution. A number of statistical and quantitative analysis were applied to examine the relation between the users’ interactions and different metrics related to the use of learning objects in a step to assess the users’ behaviour. We also used different Social Network Analysis (SNA) techniques on a network graph built from a large number of user interactions. The resulting network consisted of more than 3 million interactions distributed across more than 300,000 users. The type of those interactions is questions and answers posted on Khan Academy’s instructional videos (more than 10,000 video). In order to analyse this graph and explore the social network structure, we studied two different community detection algorithms to identify the learning interactions communities emerged in Khan Academy then we compared between their effectiveness. After that, we applied different SNA measures including modularity, density, clustering coefficients and different centrality measures in order to assess the users’ behaviour patterns and their presence. Using descriptive analysis, we discovered many characteristics and features of the repository. We found that the number of learning objects in Khan Academy’s repository grows linearly over time, more than 50% of the users do not complete the watched videos, and we found that the average duration for video lessons 5 to 10 minutes which aligns with the recommended duration in literature. By applying community detection techniques and social network analysis, we managed to identify learning communities in Khan Academy’s network. The size distribution of those communities found to follow the power-law distribution which is the case of many real-world networks. Those learning communities are related to more than one domain which means the users are active and interacting across domains. Different centrality measures we applied to focus on the most influential players in those communities. Despite the popularity of online learning repositories and their wide use, the structure of the emerged learning communities and their social networks remain largely unexplored. Our findings could be considered initial insights that may help researchers and educators in better understanding online learning repositories, the learning process inside those repositories, and learner behaviou