Node Sharing for Increased Throughput and Shorter Runtimes : an Industrial Co-Scheduling Case Study

The allocation of jobs to nodes and cores in industrial clusters is often based on queue-system standard settings, guesses or perceived fairness between different users and projects. Unfortunately, hard empirical data is often lacking and jobs are scheduled and co-scheduled for no apparent reason. In this case-study, we evaluate the performance impact of co-scheduling jobs using three types of applications and an existing 450+ node cluster at a company doing large-scale parallel industrial simulations. We measure the speedup when co-scheduling two applications together, sharing two nodes, compared to running the applications on separate nodes. Our results and analyses show that by enabling co-scheduling we improve performance in the order of 20% both in throughput and in execution times, and improve the execution times even more if the cluster is running with low utilization. We also find that a simple reconfiguration of the number of threads used in one of the applications can lead to a performance increase of 35-48% showing that there is a potentially large performance increase to gain by changing current practice in industry

Power Bounded Computing on Current & Emerging HPC Systems

Power has become a critical constraint for the evolution of large scale High Performance Computing (HPC) systems and commercial data centers. This constraint spans almost every level of computing technologies, from IC chips all the way up to data centers due to physical, technical, and economic reasons. To cope with this reality, it is necessary to understand how available or permissible power impacts the design and performance of emergent computer systems. For this reason, we propose power bounded computing and corresponding technologies to optimize performance on HPC systems with limited power budgets. We have multiple research objectives in this dissertation. They center on the understanding of the interaction between performance, power bounds, and a hierarchical power management strategy. First, we develop heuristics and application aware power allocation methods to improve application performance on a single node. Second, we develop algorithms to coordinate power across nodes and components based on application characteristic and power budget on a cluster. Third, we investigate performance interference induced by hardware and power contentions, and propose a contention aware job scheduling to maximize system throughput under given power budgets for node sharing system. Fourth, we extend to GPU-accelerated systems and workloads and develop an online dynamic performance & power approach to meet both performance requirement and power efficiency. Power bounded computing improves performance scalability and power efficiency and decreases operation costs of HPC systems and data centers. This dissertation opens up several new ways for research in power bounded computing to address the power challenges in HPC systems. The proposed power and resource management techniques provide new directions and guidelines to green exscale computing and other computing systems