Task Packing: Efficient task scheduling in unbalanced parallel programs to maximize CPU utilization

Abstract

Load imbalance in parallel systems can be generated by external factors to the currently running applications like operating system noise or the underlying hardware like a heterogeneous cluster. HPC applications working on irregular data structures can also have difficulties to balance their computations across the parallel tasks. In this article we extend, improve and evaluate more deeply the Task Packing mechanism proposed in a previous work. The main idea of the mechanism is to concentrate the idle cycles of unbalanced applications in such a way that one or more CPUs are freed from execution. To achieve this, CPUs are stressed with just useful work of the parallel application tasks, provided performance is not degraded. The packing is solved by an algorithm based on the Knapsack problem, in a minimum number of CPUs and using oversubscription. We design and implement a more efficient version of such mechanism. To that end, we perform the Task Packing "in place", taking advantage of idle cycles generated at synchronization points of unbalanced applications. Evaluations are carried out on a heterogeneous platform using FT and miniFE benchmarks. Results showed that our proposal generates low overhead. In addition the amount of freed CPUs are related to a load imbalance metric which can be used as a prediction for it.

Bi-objective scheduling algorithm for scientific workflows on cloud computing platform with makespan and monetary cost minimization approach

“…If t i is an entry task in a DAG or it is the first task must be assigned in the VM k s task list that the V M k .List ∅ shows the case, the EST (t i , VM k ) is equal to the boot-up time of VM k , shown by BUT(VM k ); otherwise, the term EST (t i , VM k ) is calculated with Eq. (11). In this equation, t γ j is γ-th duplication of task t j that is a member of the predecessor of t i , otherwise task t j is an original predecessor When a new virtual machine VM k is intended to be started before the task scheduling can be performed, it is needed to boot up the virtual machine VM k in the system; where the function BUT(VM k ) is considered to measure this boot-up time.…”

“…An energyaware workflow scheduling algorithm has been presented with the aim of datacenter power management and keeping users' service level agreement (SLA) in [10]. Utrera et al [11] have proposed an efficient algorithm to balance imbalance parallelizable programs on spare nodes with the aid of maximum resource utilization. Although it improves infrastructure resource utilization by packing tasks on the same processor, it does not consider user requirements as one of the most important stakeholders in the system.…”