Dynamic coscheduling on workstation clusters

Abstract

Coscheduling has been shown to be a critical factor in achieving efficient parallel execution in timeshared environments [12,19,4]. However, the most common approach, gang scheduling, has limitations in scaling, can compromise good interactive response, and requires that communicating processes be identified in advance.We explore a technique called dynamic coscheduling (DCS) which produces emergent coscheduling of the processes constituting a parallel job. Experiments are performed in a workstation environment with high performance networks and autonomous timesharing schedulers for each CPU. The results demonstrate that DCS can achieve effective, robust coscheduling for a range of workloads and background loads. Empirical comparisons to implicit scheduling and uncoordinated scheduling are presented.Under spin-block synchronization, DCS reduces job response times by up to 20% over implicit scheduling while maintaining fairness; and under spinning synchronization, DCS reduces job response times by up to two decimal orders of magnitude over uncoordinated scheduling. The results suggest that DCS is a promising avenue for achieving coordinated parallel scheduling in an environment that coexists with autonomous node schedulers.

Coscheduling under Memory Constraints in a NOW Environment

“…For this reason, if the task to be inserted on the RQ has any incoming message in the RMQ queue (Ø × Ö ¼µ and the main memory in such node is overloaded (AE Ñ Ñ AE Å ), the inserted task is led to the top of the RQ (ÊÉ ¼℄µ. Thus, CSM applies a dynamic technique [15] to ensure that fine-grained distributed applications are coscheduled. In this technique, the more scheduling priority is assigned to tasks the more the receiving frequency is.…”

“…Thus, coscheduling may be applied to reduce messages waiting time and make good use of the idle CPU cycles when distributed applications are executed in a cluster or NOW system. Coscheduling decisions are made taking implicit runtime information of the jobs into account, basically execution CPU cycles and communication events [12,13,14,15,16,22]. Our framework will be focused on an implicit coscheduling environment, such as scheduling the correspondents -the most recent communicated processes-in the overall system at the same time, taking into account both high message communication frequency and low penalty introduction into the delayed processes.…”

Design and Implementation of a Contention-Aware Coscheduling Strategy on Multi-Programmed Heterogeneous Clusters

“…Two main strategies for coordinating individual local schedulers have been proposed: gang scheduling (GS) [8], [14] and communication-driven coscheduling (CDC) [2]- [5], [11], [12], [15], [19]. GS uses explicit global synchronization to schedule all the processes of a job simultaneously.…”

Process Tracking for Parallel Job Control

“…Explicit gang-scheduling systems always run all the tasks of a job simultaneously [5-10, 17, 21]. In contrast, communication-driven systems [3,19,20] are more loosely coupled, and schedule tasks based on message arrival. Independent of the inter-task scheduling approach, we address those cases in which all processes within a task are closely coupled, like the csh example of the first paragraph.…”