gpu cluster Techniques for Computing

Tecnologia 38 Visitas

gpu cluster Techniques for Computing, Efforts of this blog are centered on constructing a Virtual Machine over a Cluster system that provides the double functionality of executing traditional workstation jobs as well as distributed applications efficiently.

To solve the problem, two major considerations must be addressed:

* How share and schedule the workstation resources (especially the CPU) between the local and distributed applications.

* How to manage and control the overall system for the efficient execution of both application kinds.

Coscheduling is the base principle used for the sharing and scheduling of the CPU. Coscheduling is based on reducing the communication waiting time of distributed applications by scheduling their forming tasks, or a subset of them at the same time.

Consequently, non-communicating distributed applications (CPU bound ones) will not be favored by the application of coscheduling. Only the performance of distributed applications with remote communication can be increased with coscheduling.

Coscheduling techniques follow two major trends: explicit and implicit control. This classification is based on the way the distributed tasks are managed and controlled. Basically, in explicit-control, such work is carried out by specialized processes and (or) processors. In contrast, in implicit-control, coscheduling is performed by making local scheduling decisions depending on the events occurring in each workstation.

Two coscheduling mechanisms which follow the two different control trends are presented in this project. They also provide additional features including usability, good performance in the execution of distributed applications, simultaneous execution of distributed applications, low overhead and also low impact on local workload performance. T

he design of the coscheduling techniques was mainly influenced by the optimization of these features. An implicit-control coscheduling model is also presented.

Some of the features it provides include collecting on-time performance statistics and the usefulness as a basic scheme for developing new coscheduling policies. The presented implicit-control mechanism is based on this model.

The good scheduling behavior of the coscheduling models presented is shown firstly by simulation, and their performance compared with other coscheduling techniques in the literature. A great effort is also made to implement the principal studied coscheduling techniques in a real Cluster system. Thus, it is possible to collect performance measurements of the different coscheduling techniques and compare them in the same environment.

The study of the results obtained will provide an important orientation for future research in coscheduling because, to our knowledge, no similar work (in the literature) has been done before.

Measurements in the real Cluster system were made by using various distributed benchmarks with different message patterns:

regular and irregular communication patterns, token rings, all-to-all and so on. Also, communication primitives such as barriers and basic sending and receiving using one and two directional links were separately measured. By using this broad range of distributed applications, an accurate analysis of the usefulness and applicability of the presented coscheduling techniques in Cluster computing is performed.

Next, a variation of DTS, named HPDT (High Priority Distributed Tasks) ispresented. HPDT also follows an explicit-control trend. It always assigns max-imum scheduling priority to distributed tasks. The maximum distributed perfor-mance in Cluster computing should be reached when distributed processes makingup the distributed applications always have more scheduling priority than the lo-cal ones.

For this reason, this particular kind of coscheduling technique is studiednext. Assuming that the maximum distributed performance will be reached usingthis method, this will serve as a performance referencing point for comparing thecoscheduling techniques.

Finally, the implementation of the Predictive model is analyzed in depth. Theimplementation of the Dynamic version scarcely varies with respect to the Predic-tive one. So, only some observations on the changes performed in the Predictivemodel are made. «cluster»

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