Abstract

Abstract. We describe a new, non-FCFS policy to schedule parallel jobs on systems that may be part of a computational grid. Our algorithm continuously monitors the system (i.e., the intensity of incoming jobs and variability of their resource demands), and adapts its scheduling parameters according to workload fluctuations. The proposed policy is based on backfilling, which reduces resource fragmentation by executing jobs in an order different than their arrival without delaying certain previously submitted jobs. We maintain multiple job queues that effectively separate jobs according to their projected execution time. Our policy supports different job priorities and job reservations, making it appropriate for scheduling jobs on parallel systems that are part of a computational grid. Detailed performance comparisons via simulation using traces from the Parallel Workload Archive indicate that the proposed policy consistently outperforms traditional backfilling.

We investigated whether a growth mindset intervention could be leveraged to promote performance and interest in computer science, through what mechanisms it might do so, and whether effects were stronger for women than for men. In particular, we explored whether the growth mindset intervention improved academic performance and career interest by increasing intrinsic value. We developed and tested a scalable, online, 4-session growth mindset intervention at 7 universities, across 16 introductory computer science classes ( N = 491). The intervention did not have a significant total effect on academic performance, although it indirectly improved grades via value. Additionally, the intervention, relative to the control, improved interest in the field and value also mediated this effect. Counter to expectations, the intervention worked equally well for women and men. Theoretical and practical applications are discussed.

We focus on non-FCFS job scheduling policies for parallel

Traditionally, scheduling in high-end parallel systems focuses on how to minimize the average job waiting time and on how to maximize the overall system utilization. Despite the development of scheduling strategies that aim at maximizing system utilization, parallel supercomputing traces that span long time periods indicate that such systems are mostly underutilized. Much of the time there is simply not enough load to keep the system fully utilized, although time periods do exist where system utilization levels peak at nearly 95%. In this paper, we propose a new family of scheduling policies that aims at minimizing power consumption and cooling costs by selectively choosing to power down (or put in "sleep" mode) parts of the system during periods of low load. Our goal is the development of a scheduling mechanism that adaptively adjusts the number of processors to the offered load while meeting predefined service-level agreements (SLAs). This scheduling mechanism uses online simulation, i.e., lightweight simulation modules that can execute while the system and its scheduler are in operation, and can guide resource provisioning in parallel systems. Detailed experimentation using traces from the Parallel Workloads Archive indicates that the proposed online mechanism is a viable alternative to conserve energy while meeting performance-based SLAs.