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Balancing Thread-Level and Task-Level Parallelism for Data-Intensive Workloads on Clusters and Clouds.

, , , , , and . CLUSTER, page 390-393. IEEE Computer Society, (2015)

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A Syntactic Approach for Privacy-Preserving Federated Learning., , , , , , and . ECAI, volume 325 of Frontiers in Artificial Intelligence and Applications, page 1762-1769. IOS Press, (2020)Predicting Adverse Drug Reactions on Distributed Health Data using Federated Learning., , , , , and . AMIA, AMIA, (2019)A Blockchain Framework for Ensuring Data Quality in Multi-Organizational Clinical Trials., , , and . ICHI, page 1-9. IEEE, (2019)Balancing Thread-Level and Task-Level Parallelism for Data-Intensive Workloads on Clusters and Clouds., , , , , and . CLUSTER, page 390-393. IEEE Computer Society, (2015)Auto-Generation of Smart Contracts from Domain-Specific Ontologies and Semantic Rules., , , , and . iThings/GreenCom/CPSCom/SmartData, page 963-970. IEEE, (2018)Anonymizing Data for Privacy-Preserving Federated Learning., , , , , , and . CoRR, (2020)Scaling Up Bioinformatics Workflows with Dynamic Job Expansion: A Case Study Using Galaxy and Makeflow., , , , , and . e-Science, page 332-341. IEEE Computer Society, (2015)Accelerating Comparative Genomics Workflows in a Distributed Environment with Optimized Data Partitioning., , , and . CCGRID, page 711-719. IEEE Computer Society, (2014)HAPI-Gen: Highly Accurate Phasing and Imputation of Genotype Data., , and . BCB, page 78-87. ACM, (2016)Expanding Tasks of Logical Workflows Into Independent Workflows for Improved Scalability., , , , and . CCGRID, page 548-549. IEEE Computer Society, (2014)