PY-PITS: A Scalable Python Runtime System for the Computation of Partially Idempotent Tasks

The popularization of multi-core architectures and cloud services has allowed users access to high performance computing infrastructures. However, programming for these systems might be cumbersome due to challenges involving system failures, load balancing, and task scheduling. Aiming at solving these problems, we previously introduced SPITS, a programming model and reference architecture for executing bag-of-task applications. In this work, we discuss how this programming model allowed us to design and implement PY-PITS, a simple and effective open source runtime system that is scalable, tolerates faults and allows dynamic provisioning of resources during computation of tasks. We also discuss how PY-PITS can be used to improve utilization of multi-user computational clusters equipped with queues to submit jobs and propose a performance model to aid users to understand when the performance of PY-PITS scales with the number of Workers.