Resource Aware Scheduling for EDA Regression Jobs

Typical Integrated Circuit (IC) design projects use Electronic Design Automation (EDA) tool flows to launch thousands of regressions every day on shared compute grids to complete the IC design verification process. These regressions in turn launch compute jobs with varied resource requirements and inter-job dependency constraints. Traditional grid schedulers, such as the Univa Grid Engine (UGE) [12] prioritize fairness over performance to maximize the number of jobs run with equal distribution of resources at any time. A constant challenge in day-to-day operations is to schedule these jobs for minimum overall job completion time so that developers can expect predictable regression turn-around time (TAT). We propose a resource-aware scheduling mechanism that balances performance and fairness for real-word EDA-centric workloads. We present an analysis of historical profile information from a set of regressions with complex inter-job dependencies and highly variable resource requirements to show that many of these regression jobs are well suited for efficient packing on grid machines. We formulate the regression scheduling problem as a variant of the bin packing problem, where the size of bins and balls may vary according to job-resource requirements and differing server configurations on the grid. We propose using two analytic techniques - namely k-means clustering [8] and adaptive binning [10], to solve this problem. We then evaluate the performance of our proposed solution using real workloads from daily regressions on an enterprise compute grid.