TGrid - Grid runtime support for hierarchically structured task-parallel programs

2007 IEEE International Conference on Cluster Computing

Rünger

2007

Self Cite

Abstract-In this article we tackle the problem of scheduling a dynamically generated DAG of multi-processor tasks (M-tasks). At first, we outline the need of such a scheduling approach in the context of TGrid. TGrid is an M-task runtime system for heterogeneous clusters. Then, we propose a dynamic scheduling algorithm called Reuse Processors Algorithm (RePA). The main objective of RePA is to reduce the communication and redistribution costs by trying to map child tasks to processors which are assigned to parent tasks (reuse processors). The algorithm is implemented using the SimGrid toolkit and is evaluated by comparing the makespan of the schedules produced by RePA and M-HEFT.

Section: A the Tgrid Environmentmentioning

confidence: 99%

“…In previous work we have introduced the TGrid framework which is an execution environment for multi-processor tasks on clusters of clusters [6]. It supports the dynamic creation of M-tasks, leading to aforementioned dynamically created DAGs.…”

Section: Introductionmentioning

confidence: 99%

Dynamic scheduling of multi-processor tasks on clusters of clusters

2007 IEEE International Conference on Cluster Computing

Rünger

2007

Self Cite

“…In previous work, we have introduced the TGrid framework which is an runtime environment for the execution of M-tasks in clusters of clusters [4]. It supports the dynamic creation of M-tasks, leading to aforementioned dynamically created DAGs.…”

Section: Introductionmentioning

confidence: 99%

Scheduling Dynamic Workflows onto Clusters of Clusters using Postponing

2008 Eighth IEEE International Symposium on Cluster Computing and the Grid (CCGRID)

Suter

2008

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“…Such structures arises naturally in many applications (see [1] for a discussion of the benefits of mixed parallelism and for application examples). A programming environment designed to express and execute mixed-parallel applications on the grid is TGrid [2]. This framework takes a task graph as input, finds ready nodes in the task graph, and maps (schedules) them onto different grid sites.…”

Section: Introductionmentioning

confidence: 99%

Redistribution aware two-step scheduling for mixed-parallel applications

2008 IEEE International Conference on Cluster Computing

Suter³

2008

Self Cite

Abstract-Applications raising in many scientific fields exhibit both data and task parallelism that have to be exploited efficiently. A classic approach is to structure those applications by a task graph whose nodes represent parallel computations. Scheduling such mixed-parallel applications is challenging even on a single homogeneous platform, such as a cluster. Most of the mixed-parallel application scheduling algorithms rely on two decoupled steps: allocation and mapping. This separation can induce unnecessary or costly data redistributions that have an impact on the overall performance. This is particularly true for data intensive applications. In this paper, we propose an original approach in which the allocations determined in the first step can be adapted during the second step in order to minimize the impact of these data redistributions. Two redistribution aware mapping strategies are detailed and a study of their impact on the schedule length is proposed through a comparison with an efficient two step algorithm over a broad range of experimental scenarios.