Paper Info
Title
Improving Gang Scheduling through job performance analysis and malleability
Abstract
The OpenMP programming model provides parallel applications a very important feature: job malleability. Job malleability is the capacity of an application to dynamically adapt its parallelism to the number of processors allocated to it. We believe that job malleability provides to applications the flexibility that a system needs to achieve its maximum performance. We also defend that a system has to take its decisions not only based on user requirements but also based on run-time performance measurements to ensure the efficient use of resources. Job malleability is the application characteristic that makes possible the run-time performance analysis. Without malleability applications would not be able to adapt their parallelism to the system decisions. To support these ideas, we present two new approaches to attack the two main problems of Gang Scheduling: the excessive number of time slots and the fragmentation. Our first proposal is to apply a scheduling policy inside each time slot of Gang Scheduling to distribute processors among applications considering their efficiency, calculated based on run-time measurements. We call this policy Performance-Driven Gang Scheduling. Our second approach is a new re-packing algorithm, Compress&Join, that exploits the job malleability. This algorithm modifies the processor allocation of running applications to adapt it to the system necessities and minimize the fragmentation and number of time slots. These proposals have been implemented in a SGI Origin 2000 with 64 processors. Results show the validity and convenience of both, to consider the job performance analysis calculated at run-time to decide the processor allocation, and to use a flexible programming model that adapts applications to system decisions.
Year
DOI
Venue
2001
10.1145/377792.377852
I4CS
Keywords
Field
DocType
run-time measurement,time slot,improving gang,system necessity,job performance analysis,run-time performance analysis,malleability application,job malleability,system decision,maximum performance,processor allocation,gang scheduling,programming model,user requirements,job performance,graphical user interface,application
Programming paradigm,Computer science,Scheduling (computing),Parallel computing,Gang scheduling,Exploit,Real-time computing,Graphical user interface,Malleability,Job performance,User requirements document,Distributed computing
Conference
ISBN
Citations 
PageRank 
1-58113-410-X
13
0.74
References 
Authors
20
3
Name
Order
Citations
PageRank
Julita Corbalan117412.00
Xavier Martorell21470125.40
Jesus Labarta3138992.33