RATS: A resource allocator for optimizing the execution of tumor simulations over HPC infrastructures

Abstract

In this work, we introduce RATS (Resource Allocator for Tumor Simulations), the first optimizer for the execution of tumor simulations over HPC infrastructures. Given a set of drug therapies under in-silico study, the optimization framework of RATS can: (i) devise the optimal number of cores and prescribe the required number of core hours; and (ii) under core capacity constraints, RATS schedules the execution of simulations minimizing the overall number of core hours, simultaneously prioritizing the execution of expectedly promising in-silico trials higher compared to unpromising ones. RATS is deployed by life scientists at the Barcelona Supercomputing Center to remove the burden of blindly guessing the core hours needing to be reserved from HPC admins to study various tumor treatment methodologies, as well as to rapidly distinguish effective drug combinations, thus, potentially cutting time to market for new cancer therapies. The latter is further elevated by the RATS+ extension we plug into the initial framework. RATS+ employs a Transfer Learning approach to leverage optimization models and decisions from prior in-silico studies, thereby reducing the optimization effort required for new studies in this domain.

Our experimental evaluation, on real-world data derived from the execution of more than 2500 tumor simulations on the MareNostrum4 supercomputer, confirms the effectiveness of both RATS and RATS+ across the aforementioned performance dimensions.