  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. This trend requires current SWfMS to adapt:
Native support for parallel workflow execution must be provided to increase
performance; dynamically scalable "pay-per-use" compute infrastructures have to
be integrated to diminish hardware costs; adaptive scheduling of workflows in
distributed compute environments is required to optimize resource utilization.
In this survey we give an overview of parallelization techniques for SWfMS,
both in theory and in their realization in concrete systems. We find that
current systems leave considerable room for improvement and we propose key
advancements to the landscape of SWfMS.
