Parallelization in Scientific Workflow Management Systems
Over the last two decades, the scientific community witnessed the establishment of computation as an integral part of research beside the traditional paradigms of theory and experiment. Over the last two decades, Scientific Workflow Management Systems (SWfMS) have emerged as a means to facilitate the design, execution, and monitoring of reusable scientific data processing pipelines. At the same time, the amounts of data generated in various areas of science outpaced enhancements in computational power and storage capabilities. This is especially true for the life sciences, where new technologies increased the sequencing throughput from kilobytes to terabytes per day.