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Workflow systems provide a convenient way for users to write large-scale applications by composing independent tasks into large graphs that can be executed concurrently on high-performance clus- ters. In many newer workflow systems, tasks are often expressed as a combination of function invocations in a high-level language. Because necessary code and data are not statically known prior to execution, they must be moved into the cluster at runtime. An obvious way of doing this is to translate function invocations into self-contained executable programs and run them as usual, but this brings a hefty performance penalty: a function invocation now needs to piggyback its context with extra code and data to a remote node, and the remote node needs to take extra time to reconstruct the invocation’s context before executing it, both detrimental to lightweight short-running functions. A better solution for workflow systems is to treat functions and invocations as first-class abstractions: subsequent invocations of the same function on a worker node should only pay for the cost of context setup once and reuse the context between different invocations. The remaining problems lie in discovering, distributing, and retaining the reusable context among workers. In this paper, we discuss the rationale and design requirement of these mechanisms to support context reuse, and implement them in TaskVine, a data- intensive distributed framework and execution engine. Our results from executing a large-scale neural network inference application and a molecular design application show that treating functions and invocations as first-class abstractions reduces the execution time of the applications by 94.5% and 26.9%, respectively. 

Many scientific applications are expressed as high-throughput workflows that consist of large graphs of data assets and tasks to be executed on large parallel and distributed systems. A chal- lenge in executing these workflows is managing data: both datasets and software must be efficiently distributed to cluster nodes; inter- mediate data must be conveyed between tasks; output data must be delivered to its destination. Scaling problems result when these actions are performed in an uncoordinated manner on a shared filesystem. To address this problem, we introduce TaskVine: a sys- tem for exploiting the aggregate local storage and network capacity of a large cluster. TaskVine tracks the lifetime of data in a workflow –from archival sources to final outputs– making use of local storage to distribute, and re-use data wherever possible. We describe the architecture and novel capabilities of TaskVine, and demonstrate its use with applications in genomics, high energy physics, molecular dynamics, and machine learning.