Eligibility criteria play a critical role in clinical trials by determining the target patient population, which significantly influences the outcomes of medical interventions. However, current approaches for designing eligibility criteria have limitations to support interactive exploration of the large space of eligibility criteria. They also ignore incorporating detailed characteristics from the original electronic health record (EHR) data for criteria refinement. To address these limitations, we proposed TrialCompass, a visual analytics system integrating a novel workflow, which can empower clinicians to iteratively explore the vast space of eligibility criteria through knowledge-driven and outcome-driven approaches. TrialCompass supports history-tracking to help clinicians trace the evolution of their adjustments and decisions when exploring various forms of data (i.e., eligibility criteria, outcome metrics, and detailed characteristics of original EHR data) through these two approaches. This feature can help clinicians comprehend the impact of eligibility criteria on outcome metrics and patient characteristics, which facilitates systematic refinement of eligibility criteria. Using a real-world dataset, we demonstrated the effectiveness of TrialCompass in providing insights into designing eligibility criteria for septic shock and sepsis-associated acute kidney injury. We also discussed the research prospects of applying visual analytics to clinical trials.

As cloud-based computation grows to be an increasingly important paradigm, providing a general computational interface to support datacenter-scale programming has become an imperative research agenda. Many cloud systems use existing virtual machine monitor (VMM) technologies, such as Xen, VMware, and Windows Hypervisor, to multiplex a physical host into multiple virtual hosts and isolate computation on the shared cluster platform. However, traditional multiplexing VMMs do not scale beyond one single physical host, and it alone cannot provide the programming interface and cluster-wide computation that a datacenter system requires. We design a new instruction set architecture, DISA, to unify myriads of compute nodes to form a big virtual machine called DVM and present programmers the view of a single computer, where thousands of tasks run concurrently in a large, unified, and snapshotted memory space. The DVM provides a simple yet scalable programming model and mitigates the scalability bottleneck of traditional distributed shared memory systems. Along with an efficient execution engine, the capacity of a DVM can scale up to support large clusters. We have implemented and tested DVM on four platforms, and our evaluation shows that DVM has excellent performance and scalability. On one physical host, the system overhead of DVM is comparable to that of traditional VMMs. On 16 physical hosts, the DVM runs 10 times faster than MapReduce/Hadoop and X10. On 160 compute nodes in the TH-1/GZ supercomputer, the DVM delivers a$\bf{12.99\times}$speedup over the computation on 10 compute nodes. The implementation of DVM also allows it to run above traditional VMMs, and we verify that DVM shows linear speedup on a parallelizable workload on 256 large EC2 instances.

As cloud-based computation becomes increasingly important, providing a general computational interface to support datacenter-scale programming has become an imperative research agenda. Many cloud systems use existing virtual machine monitor (VMM) technologies, such as Xen, VMware, and Windows Hypervisor, to multiplex a physical host into multiple virtual hosts and isolate computation on the shared cluster platform. However, traditional multiplexing VMMs do not scale beyond one single physical host, and it alone cannot provide the programming interface and cluster-wide computation that a datacenter system requires. We design a new instruction set architecture, DISA, to unify myriads of compute nodes to form a big virtual machine called DVM, and present programmers the view of a single computer where thousands of tasks run concurrently in a large, unified, and snapshotted memory space. The DVM provides a simple yet scalable programming model and mitigates the scalability bottleneck of traditional distributed shared memory systems. Along with an efficient execution engine, the capacity of a DVM can scale up to support large clusters. We have implemented and tested DVM on three platforms, and our evaluation shows that DVM has excellent performance in terms of execution time and speedup. On one physical host, the system overhead of DVM is comparable to that of traditional VMMs. On 16 physical hosts, the DVM runs 10 times faster than MapReduce/Hadoop and X10. On 256 EC2 instances, DVM shows linear speedup on a parallelizable workload.