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Tailoring personalized treatments demands the analysis of a patient's characteristics, which may be scattered over a wide variety of sources. These features include family history, life habits, comorbidities, and potential treatment side effects. Moreover, the analysis of the services visited the most by a patient before a new diagnosis, as well as the type of requested tests, may uncover patterns that contribute to earlier disease detection and treatment effectiveness. Built on knowledge-driven ecosystems, we devise DE4LungCancer, a health data ecosystem of data sources for lung cancer. In this data ecosystem, knowledge extracted from heterogeneous sources, e.g., clinical records, scientific publications, and pharmacological data, is integrated into knowledge graphs. Ontologies describe the meaning of the combined data, and mapping rules enable the declarative definition of the transformation and integration processes. DE4LungCancer is assessed regarding the methods followed for data quality assessment and curation. Lastly, the role of controlled vocabularies and ontologies in health data management is discussed, as well as their impact on transparent knowledge extraction and analytics. This paper presents the lessons learned in the DE4LungCancer development. It demonstrates the transparency level supported by the proposed knowledge-driven ecosystem, in the context of the lung cancer pilots of the EU H2020-funded project BigMedilytic, the ERA PerMed funded project P4-LUCAT, and the EU H2020 projects CLARIFY and iASiS. [ABSTRACT FROM AUTHOR]

Different communication-aware mapping techniques were proposed in recent years for improving the performance of distributed systems based on both, off-chip and on-chip networks. Some of these proposals were based on heuristic search for finding pseudo-optimal assignments of tasks and processing elements. However, the technology integration improvements have allowed a significant increase in the number of network nodes, requiring the acceleration of the heuristic search. In this paper, we propose a comparative study of the local search method used in a communication-aware mapping technique, when implemented on different parallel architectures. We compare the performance provided by a version of the local search method when executed on a single Graphics Processing Unit (GPU) with the one provided by the MPI version executed on a supercomputer with the same theoretical performance of the GPU platform, in order to study a fair scenario. We have considered a GPU based on the Fermi architecture, evaluating the improvements achieved by some new architectural features of this platform. The results show that a mixed parallel implementation on a single GPU outperforms the MPI implementation of the local search method. These results validate the GPU implementation as a very cost-effective accelerator for the local search method. [ABSTRACT FROM AUTHOR]

The computing capabilities of current multi-core and many-core architectures have been used in crowd simulations for both enhancing crowd rendering and simulating continuum crowds. However, improving the scalability of crowd simulation systems by exploiting the inherent parallelism of these architectures is still an open issue. In this paper, we propose different parallelization strategies for the collision check procedure that takes place in agent-based simulations. These strategies are designed for exploiting the parallelism in both multi-core and many-core architectures like graphic processing units (GPUs). As for the many-core implementations, we analyse the bottlenecks of a previous GPU version of the collision check algorithm, proposing a new GPU version that removes the bottlenecks detected. In order to fairly compare the GPU with the multi-core implementations, we propose a parallel CPU version that uses read--copy update (RCU), a new synchronization method which significantly improves performance. We perform a comparison study of these different implementations. On the one hand, the comparison study shows the first performance evaluation of RCU in a real user-space application with complex data structures. On the other hand, the comparison shows that the GPU greatly accelerates the collision test with respect to any other implementation optimized for multi-core CPUs. In addition, we analyse the efficiency of the different implementations taking into account the theoretical performance and power consumption of each platform. The evaluation results show that the GPU-based implementation consumes less energy and provides a minimum speedup of 45× with respect to any of the CPU-based implementations. Since interactivity is a hard constraint in crowd simulations, this acceleration of the collision check process represents a significant improvement in the overall system throughput and response time. Therefore, the simulations are significantly accelerated, and the system throughput and scalability are improved. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, we look at the acceleration of weakly coupled electromechanics using the graphics processing unit (GPU). Specifically, we port to the GPU a number of components of [ABSTRACT FROM AUTHOR]

Abstract: Interactive applications like crowd simulations need to properly render the virtual world while simulating the interaction of thousands of agents at the same time. The computational workload generated by these two tasks highly increases with the number of the simulated agents, requiring a scalable design of the multiagent system. In this paper, we present, in an unified manner, a distributed multiagent system architecture that can manage large crowds of autonomous agents at interactive rates while rendering multiple views of the virtual world being simulated. This architecture consists of a distributed multiagent system and a complementary distributed visualization subsystem. We also present a new scalability study of the distributed multiagent system architecture, as well as an application example. The scalability study shows that, since no system bottlenecks are present in the proposed multiagent system architecture, it can efficiently simulate population sizes of different orders of magnitude by simply adding more hardware, provided that the ratio of clients per server is appropriate for the computer platforms acting as servers. On the other hand, the application example shows that these improvements are achieved without affecting the interactivity of the simulation. Therefore, the scalability of the proposed multiagent system architecture is validated for interactive large-scale applications. [Copyright &y& Elsevier]

The visualization system of large-scale crowd simulations should scale up with both the number of visuals (views of the virtual world) and the number of agents displayed in each visual. Otherwise, we could have large scale crowd simulations where only a small percentage of the population is displayed. Several approaches have been proposed in order to efficiently render crowds of animated characters. However, these approaches either render crowds animated with simple behaviors or they can only support a few hundreds of user-driven entities. In this paper, we propose a distributed visualization system for large crowds of autonomous agents that allows the visualization of crowds animated with complex behaviors without adding significant overhead to the simulation servers. The proposed implementation can be hosted on dedicated computers different from the servers, and it takes advantage of the Graphics Processor Unit (GPU) capabilities. As a result, the performance evaluation shows that thousands of agents can be rendered without affecting the system performance. Also, the results show that the design of the visual client allows to add multiple visuals for displaying large crowds. [ABSTRACT FROM AUTHOR]