An Effective and Practical Performance Prediction Model for Parallel Computing on Nondedicated Heterogeneous NOW

Yang, Yong; Zhang, Xiaodong; Song, Yongsheng

doi:10.1006/jpdc.1996.0129

Cited by 52 publications

(21 citation statements)

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“…Heterogeneous resources will be characterized based on the same set of parameters and metrics. We can consider this approach as using a virtual resource model to cover resource heterogeneity; the research in [14,24] has demonstrated concrete examples based on this approach.…”

Section: Discussionmentioning

confidence: 99%

“…This approach allows heterogeneous models developed by various performance modelling techniques to be integrated. Modelling techniques which are considered most appropriate are chosen based on the types of component tasks and the types of platforms on which component jobs are to be run, such as computationintensive tasks [10] vs. I/O-intensive tasks [11,12], or multiprocessors [13] vs. heterogeneous clusters [14,15] for example. The resulting heterogeneous models, however, need to follow a common specification in order to be combined.…”

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confidence: 99%

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Predictive performance modelling of parallel component compositions

Zhao

Jarvis

2007

Cluster Comput

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Large-scale scientific computing applications frequently make use of closely-coupled distributed parallel components. The performance of such applications is therefore dependent on the component parts and their interaction at run-time. This paper describes a methodology for predictive performance modelling and evaluation of parallel applications composed of multiple interacting components. In this paper, the fundamental steps and required operations involved in the modelling and evaluation process are identified-including component decomposition, component model combination, M × N communication modelling, dataflow analysis and overall performance evaluation. A case study is presented to illustrate the modelling process and the methodology is verified through experimental analysis.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Predictive performance modelling of parallel component compositions

Zhao

Jarvis

2007

Cluster Comput

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“…In fact, the total parallel computation time is disturbed by other users, so the non-dedicated computation cost [44]:…”

Section: Nhbl Modelmentioning

confidence: 99%

Models of parallel computation: a survey and classification

Zhang

Chen

Sun

et al. 2007

Front. Comput. Sc. China

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In this paper, the state-of-the-art parallel computational model research is reviewed. We will introduce various models that were developed during the past decades. According to their targeting architecture features, especially memory organization, we classify these parallel computational models into three generations. These models and their characteristics are discussed based on three generations classification. We believe that with the ever increasing speed gap between the CPU and memory systems, incorporating non-uniform memory hierarchy into computational models will become unavoidable. With the emergence of multi-core CPUs, the parallelism hierarchy of current computing platforms becomes more and more complicated. Describing this complicated parallelism hierarchy in future computational models becomes more and more important. A semi-automatic toolkit that can extract model parameters and their values on real computers can reduce the model analysis complexity, thus allowing more complicated models with more parameters to be adopted. Hierarchical memory and hierarchical parallelism will be two very important features that should be considered in future model design and research.Keywords parallel computational models, hierarchical memory, hierarchical parallelism, three generations, memory model BackgroundThe simplified and abstract description of a computer is called a "computational model". A computer architect, algorithm designer and program developer can use such a model as a basis to assess their work including the suitability of one computer architecture to various applications, the computation complexity of an algorithm and the potential performance of one program on various computers, etc. A good computational model can simplify the complicated work of the architect, algorithm designer and program developer while mapping their work effectively onto real computers. Thus, such computational model is sometimes also called "Bridging model" [1]. The bridging model between the sequential computer and algorithm designer/program developer is the Von Neumann and RAM (Random Access Machine) Model [2]. However, no commonly recognized bridging models are found between parallel computer and parallel programs, and no other model exists that can map a user's parallel program so smoothly onto parallel computers as the Von Neumann and RAM Model do. This situation is largely due to the immature parallel computer design, i.e., there are so many different architectures for parallel computers that change rapidly each year, and the greater demand on performance [3]; a clean and simplified description is almost impossible. However, the trend of parallel computer design is converging and a common parallel computer architecture model can be realized (such as cluster), and the communication (we have standard MPI interface) of parallel computing is not so interconnect network dependent, thus we have the BSP and LogP models [1,7].Based on the historical development of parallel computational models, we think they can be classified ...

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“…In [25], each node in a system is described relative to the fastest processing element in the system, and the system modeled is reliant upon variances of the processing elements. The authors of [26] use a performance prediction model implemented in PVM and has a two level graphing approach. This approach includes an application, computes relative computing power towards the fastest machine in the system, examines communication overhead, and adds randomness to account for bus activity.…”

Section: Previous Workmentioning

confidence: 99%

Resource selection and allocation for dynamic adaptive computing in heterogeneous clusters

Duselis

Cauich

Wang

et al. 2009

2009 IEEE International Conference on Cluster Computing and Workshops

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This paper provides a framework for dynamic adaptive computing in heterogeneous clusters for computationally intensive applications. The framework considers a set of discoverable interconnected computational resources and either a parallel or sequential workload needing to be executed. An adaptive inclusion/exclusion algorithm is used to select the resources by using novel performance measurements and profiling techniques. Furthermore, contrary to a greedy approach where all the resources are seized for the workload application, our framework only harnesses the best fit resources measured against system-wide performance characterization, and is contingent upon the current workload definition. The intelligent selection of a subset of resources has proven to achieve better performance; especially in environments with a high level of heterogeneity where the characteristics of some resources may not achieve the best performance the cluster can provide. Additionally, this paper provides a novel analysis of the workload and cluster characteristics, exhibiting analytical starting points to be used in the resource selection.

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An Effective and Practical Performance Prediction Model for Parallel Computing on Nondedicated Heterogeneous NOW

Cited by 52 publications

References 0 publications

Predictive performance modelling of parallel component compositions

Predictive performance modelling of parallel component compositions

Models of parallel computation: a survey and classification

Resource selection and allocation for dynamic adaptive computing in heterogeneous clusters

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