RUNTIME METHODS TO IMPROVE ENERGY EFFICIENCY IN SUPERCOMPUTING APPLICATIONS

Energy efficiency in supercomputing is critical to limit operating costs and carbon footprints. While the energy efficiency of future supercomputing centers needs to improve at all levels, the energy consumed by the processing units is a large fraction of the total energy consumed by High Performance Computing (HPC) systems. HPC applications use a parallel programming paradigm like the Message Passing Interface (MPI) to coordinate computation and communication among thousands of processors. With dynamically-changing factors both in hardware and software affecting energy usage of processors, there exists a need for power monitoring and regulation at runtime to achieve savings in energy. This dissertation highlights an adaptive runtime framework that enables processors with core-specific power control by dynamically adapting to workload characteristics to reduce power with little or no performance impact. Two opportunities to improve the energy efficiency of processors running MPI applications are identified - computational workload imbalance and waiting on memory. Monitoring of performance and power regulation is performed by the framework transparently within the MPI runtime system, eliminating the need for code changes to MPI applications. The effect of enforcing power limits (capping) on processors is also investigated. Experiments on 32 nodes (1024 cores) show that in presence of workload imbalance, the runtime reduces Central Processing Unit (CPU) frequency on cores not on the critical path, thereby reducing power and hence energy usage without deteriorating performance. Using this runtime, six MPI mini-applications and a full MPI application show an overall 20% decrease in energy use with less than 1% increase in execution time. In addition, the lowering of frequency on non-critical cores reduces run-to-run performance variation and improves performance. For the full application, an average speedup of 11% is seen, while the power is lowered by about 31% for an energy savings of up to 42%. Another experiment on 16 nodes (256 cores) that are power capped also shows performance improvement along with power reduction. Thus, energy optimization can also be a performance optimization. For applications that are limited by memory access times, memory metrics identified facilitate lowering of power by up to 32% without adversely impacting performance.Doctor of Philosoph

An Exploratory Study of Patient Falls

Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ≥ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body

Stakeholder Governance: Empirical and Theoretical Developments

Stakeholder governance receives attention across many disciplines, resulting in fragmented knowledge. The inherent complexity of stakeholder governance requires the integration of this knowledge to develop comprehensive and inclusive theories to better conceptualize this phenomenon. In this research, we develop stakeholder governance through empirical and theoretical approaches. In the first essay, we use multiple case comparisons to empirically examine how and why organizations manage food waste to develop grounded theory through contextualized explanations. We contribute grounded theoretical and empirical evidence to show that food waste represents a significant business problem. Our data suggests that dimensions of logistics and stakeholder governance dictate how and why organizations manage food waste. These findings stimulate a deeper dive into stakeholder governance, revealing fragmentations in knowledge that require holistic, interdisciplinary review and synthesis. In the second essay, we identify definitions and terminologies, review the evolution of theories and orientations, organize mechanisms and conceptualizations, synthesize key theoretical tensions, and offer suggestions for future research to contribute theoretical developments for stakeholder governance. We contribute pluralist conceptual frameworks that integrate knowledge across disciplines to provide a comprehensive overview and recommendations. Overall, we contribute empirical and theoretical research to advance theory development for stakeholder governance

Energy Demand Response for High-Performance Computing Systems

The growing computational demand of scientific applications has greatly motivated the development of large-scale high-performance computing (HPC) systems in the past decade. To accommodate the increasing demand of applications, HPC systems have been going through dramatic architectural changes (e.g., introduction of many-core and multi-core systems, rapid growth of complex interconnection network for efficient communication between thousands of nodes), as well as significant increase in size (e.g., modern supercomputers consist of hundreds of thousands of nodes). With such changes in architecture and size, the energy consumption by these systems has increased significantly. With the advent of exascale supercomputers in the next few years, power consumption of the HPC systems will surely increase; some systems may even consume hundreds of megawatts of electricity. Demand response programs are designed to help the energy service providers to stabilize the power system by reducing the energy consumption of participating systems during the time periods of high demand power usage or temporary shortage in power supply. This dissertation focuses on developing energy-efficient demand-response models and algorithms to enable HPC system\u27s demand response participation. In the first part, we present interconnection network models for performance prediction of large-scale HPC applications. They are based on interconnected topologies widely used in HPC systems: dragonfly, torus, and fat-tree. Our interconnect models are fully integrated with an implementation of message-passing interface (MPI) that can mimic most of its functions with packet-level accuracy. Extensive experiments show that our integrated models provide good accuracy for predicting the network behavior, while at the same time allowing for good parallel scaling performance. In the second part, we present an energy-efficient demand-response model to reduce HPC systems\u27 energy consumption during demand response periods. We propose HPC job scheduling and resource provisioning schemes to enable HPC system\u27s emergency demand response participation. In the final part, we propose an economic demand-response model to allow both HPC operator and HPC users to jointly reduce HPC system\u27s energy cost. Our proposed model allows the participation of HPC systems in economic demand-response programs through a contract-based rewarding scheme that can incentivize HPC users to participate in demand response

Scientific Grand Challenges: Crosscutting Technologies for Computing at the Exascale - February 2-4, 2010, Washington, D.C.

The goal of the "Scientific Grand Challenges - Crosscutting Technologies for Computing at the Exascale" workshop in February 2010, jointly sponsored by the U.S. Department of Energy’s Office of Advanced Scientific Computing Research and the National Nuclear Security Administration, was to identify the elements of a research and development agenda that will address these challenges and create a comprehensive exascale computing environment. This exascale computing environment will enable the science applications identified in the eight previously held Scientific Grand Challenges Workshop Series

Decision Making of Environmental Engineers on Project Selection

Some environmental engineers do not understand how to perceive profitable opportunities in redeveloping the large number of contaminated brownfield sites in New Jersey. The purpose of this qualitative exploratory case study was to find effective decision-making strategies that help environmental engineers acquire profitable environmental redevelopment projects. The target population consisted of 4 environmental engineers in an environmental organization in Camden County, New Jersey who possessed proven decision-making strategies that helped them acquire profitable environmental redevelopment projects in the past 5 years. The conceptual framework for this study was the multiple criteria decision method (MCDM). Semistructured interviews were conducted with the engineers, and company documents were additional sources of data gathered. Triangulation and member checking were used to ensure the trustworthiness of interpretations. Five themes emerged from the analyses relating to strategies for an MCDM assessment in project management, a go/no-go assessment in project selections, education and training, ethics as an organizational value, and project management. These findings may lead to social change in Camden County, New Jersey community organizations, such as schools, daycare centers, and local businesses, which may benefit from the knowledge and safety recommendations of remediation decision making. Furthermore, these findings may provide opportunities for environment organizations to teach and train stakeholders on environmental processes while providing profitable opportunities to shareholders through sustainable practices

Supporting organizational adaptation through successful strategic and operational project portfolio management

A major challenge for organizations’ strategic management is to cope with uncertainty and constantly adapt to a turbulent environment. In organizations, project portfolios constitute the responsible entities to operationalize the adaptation by developing new opportunities and ensuring the implementation of an adapted organizational strategy. Thus, in this dissertation, I investigate how project portfolio and project management can strategically and operationally cope with uncertainty, increase portfolio and project success, and thereby support an organization’s successful adaptation. In regards to the strategic decision-making of portfolio management, I empirically reveal that decision-makers can effectively cope with projects’ and environment’s uncertainty through real options reasoning and thereby increase portfolio innovativeness and success. Furthermore, I show that portfolios consisting of agile projects benefit the valuable recognition of emergent strategies. In addition, I highlight the important role of entrepreneurial orientation and innovation climate as antecedents and moderators of portfolio management decisions. In regards to the operational implementation of a portfolio’s strategy, I investigate how portfolio and project managers can cope with projects’ uncertainty in their operational decisions. I show that project management effort causally increases projects’ profitability and that the marginal impact of project management effort increases for higher project complexity. Furthermore, I show that project managers’ reporting behavior is significantly associated with projects’ future performance. This dissertation contributes new insights on portfolio and project management in general and portfolio management’s strategic and operational decision-making under uncertainty in particular. Furthermore, it adds new aspects to the interaction between portfolio and project management and thereby opens up new perspectives on behavioral and contextual decision-making in portfolio and project management

Strategies to Reduce Cost Overruns and Schedule Delays in Construction Projects

Senior managers fail to control time and costs of construction projects despite available advanced project management tools. Based on project management theory, the objective of this multiple case study was to explore the strategies senior managers use to reduce cost overruns and schedule delays in construction projects. Primary data were obtained from semistructured interviews with 3 senior managers from different construction project management companies who have successfully managed construction projects in Qatar. Data analysis process included a modified Van Kaam method. The transcribed interviews were interpreted and coded to generate themes and were validated through member checking and archival documents. The most centralized themes included (a) master planning, (b) processes and procedures, (c) managing design stage, (d) procurement management, (e) use of proper software, (f) setting project cost and time, and (g) deciding clear scope. A construction project may not succeed without appropriate planning for all stages of the project lifecycle. Managing the approval of the project components during the design stage contributes to reducing changes during construction, which is helpful to control cost and time. The project processes and procedures are meaningful roadmaps for the managers and decision makers. The implications for positive social change include the potential to maintain a cleaner Earth by reducing design and construction wastes. Reducing wastes improves the cost of construction and provides opportunities for people to own property at more affordable costs