Search for: All records

An increasing number of distributed applications operate by dispatching function invocations across the nodes of a distributed system. To operate correctly, the code and data dependencies of the function must be distributed along with the invocations in some way. When translating applications to work on large scale distributed systems, managing these dependencies becomes challenging: delivery must be scalable to thousands of nodes; the dependencies must be consistent across the system; and the method must be usable by an unprivileged developer. As a solution, in this paper we present PONCHO, which is a lightweight Python based toolkit which allows users to discover, package, and deploy dependencies as an integral part of distributed applications. PONCHO encapsulates a set of commands to be executed within an environment. PONCHO offers a lightweight solution to create and manage environments increasing the portability of scientific applications as well as reproducibility. In this paper, we evaluate PONCHO with real-world applications in the fields of physics, computational chemistry, and hyperparameter optimization, We observe the challenges that arise when creating and distributing an environment and measure the overheads that emerge as a result.