High Performance and Secure Execution Environments for Emerging Architectures

Energy-efficiency and performance have been the driving forces of system architectures and designers in the last century. Given the diversity of workloads and the significant performance and power improvements when running workloads on customized processing elements, system vendors are drifting towards new system architectures (e.g., FAM or HMM). Such architectures are being developed with the purpose of improving the system\u27s performance, allow easier data sharing, and reduce the overall power consumption. Additionally, current computing systems suffer from a very wide attack surface, mainly due to the fact that such systems comprise of tens to hundreds of sub-systems that could be manufactured by different vendors. Vulnerabilities, backdoors, and potentially hardware trojans injected anywhere in the system form a serious risk for confidentiality and integrity of data in computing systems. Thus, adding security features is becoming an essential requirement in modern systems. In the purpose of achieving these performance improvements and power consumption reduction, the emerging NVMs stand as a very appealing option to be the main memory building block or a part of it. However, integrating the NVMs in the memory system can lead to several challenges. First, if the NVM is used as the sole memory, incorporating security measures can exacerbate the NVM\u27s write endurance and reduce its lifetime. Second, integrating the NVM as a part of the main memory as in DRAM-NVM hybrid memory systems can lead to higher performance overheads of persistent applications. Third, Integrating the NVM as a memory extension as in fabric-attached memory architecture can cause a high contention over the security metadata cache. Additionally, in FAM architectures, the memory sharing can lead to security metadata coherence problems. In this dissertation, we study these problems and propose novel solutions to enable secure and efficient integration of NVMs in the emerging architectures

Visualizations of Administered Health Services in Medicare

The CMS Data Entrepreneurs' Synthetic Public Use File (DE-SynPUF) provides sample data from Medicare beneficiaries and their claims.  This data, while synthetic, provides us with the means to generate useful tools and visualizations that can be applied accurately to real Medicare data.  Demographic, clinical, and financial data included in the data set will provide reasonable bounds and expectations of real Medicare data.  However,  multivariate analysis proves to be difficult as specific patterns were removed to avoid reidentification risks for beneficiaries.  Yet, multivariate analysis will still show interesting applications toward real data.   We hope to be able to demonstrate a visual analytics approach that will help reveal important underlying patterns across multiple variables throughout the three years of data provided