International audienceGang scheduling has been widely used as a practical solution to the dynamic parallel job scheduling problem. Parallel tasks of a job are scheduled for simultaneous execution on a partition of a parallel computer. Gang Scheduling has many advantages, such as responsiveness, efficient sharing of resources and ease of programming. However, there are two major problems associated with gang scheduling: scalability and the decision of what to do when a task blocks. In this paper we propose a class of scheduling policies, dubbed Concurrent Gang, that is a generalization of gang-scheduling, and allows for the flexible simultaneous scheduling of multiple parallel jobs with different characteristics. Besides that, scalability in...
The job workloads of general-purpose multiprocessors usually include both compute-bound parallel job...
The OpenMP programming model provides parallel applications a very important feature: job malleabili...
As part of the Massively Parallel Computing Initiative (MPCI) at the Lawrence Livermore National Lab...
Abstract In this paper we propose a new class of scheduling poli-cies, dubbed Concurrent Gang, that ...
[[abstract]]Gang scheduling has recently been shown to be an effective job scheduling policy for par...
[[abstract]]©2000 Institute of Information Science Academia Sinica-Gang scheduling has recently been...
Abstract: In this paper we study the performance of parallel job scheduling in a distributed system....
. Parallel job scheduling is beginning to gain recognition as an important topic that is distinct f...
Most commercial multicomputers use space-slicing schemes in which each scheduling decision has an un...
Gang scheduling provides both space-slicing and time-slicing of computer resources for parallel prog...
Gang scheduling is a resource management scheme for parallel and distributed systems that combines t...
Clusters of workstations have emerged as a cost-effective solution to high performance computing pro...
. We present a new scheduling method for batch jobs on massively parallel processor architectures. T...
Abstract The hardware trend toward higher core counts will likely result in a dynamic, bursty and in...
The hardware trend toward higher core counts will likely result in a dynamic, bursty and interactive...
The job workloads of general-purpose multiprocessors usually include both compute-bound parallel job...
The OpenMP programming model provides parallel applications a very important feature: job malleabili...
As part of the Massively Parallel Computing Initiative (MPCI) at the Lawrence Livermore National Lab...
Abstract In this paper we propose a new class of scheduling poli-cies, dubbed Concurrent Gang, that ...
[[abstract]]Gang scheduling has recently been shown to be an effective job scheduling policy for par...
[[abstract]]©2000 Institute of Information Science Academia Sinica-Gang scheduling has recently been...
Abstract: In this paper we study the performance of parallel job scheduling in a distributed system....
. Parallel job scheduling is beginning to gain recognition as an important topic that is distinct f...
Most commercial multicomputers use space-slicing schemes in which each scheduling decision has an un...
Gang scheduling provides both space-slicing and time-slicing of computer resources for parallel prog...
Gang scheduling is a resource management scheme for parallel and distributed systems that combines t...
Clusters of workstations have emerged as a cost-effective solution to high performance computing pro...
. We present a new scheduling method for batch jobs on massively parallel processor architectures. T...
Abstract The hardware trend toward higher core counts will likely result in a dynamic, bursty and in...
The hardware trend toward higher core counts will likely result in a dynamic, bursty and interactive...
The job workloads of general-purpose multiprocessors usually include both compute-bound parallel job...
The OpenMP programming model provides parallel applications a very important feature: job malleabili...
As part of the Massively Parallel Computing Initiative (MPCI) at the Lawrence Livermore National Lab...