The Nas Parallel Benchmarks

Bailey, David H.; Barszcz, Eric; Barton, John T.; Browning, D. S.; Carter, R.L.; Dagum, Leonardo; Fatoohi, Rod; Frederickson, Paul O.; Lasinski, T. A.; Schreiber, Robert; Simon, Horst D.; Venkatakrishnan, V.; Weeratunga, Sisira

doi:10.1177/109434209100500306

Cited by 1,170 publications

(181 citation statements)

References 1 publication

Supporting

Mentioning

174

Contrasting

Unclassified

Order By: Relevance

“…Experiments were performed with a variety of MPI-based parallel applications. We use the MPI implementation of the NAS Parallel Benchmarks (NPB) [1], version 3.3.1. Experiments were performed with input class C, which represents a medium-large input size.…”

Section: Methodology Of the Analysismentioning

confidence: 99%

Leveraging Cloud Heterogeneity for Cost-Efficient Execution of Parallel Applications

Roloff

Diener

Carreno

et al. 2017

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Public cloud providers offer a wide range of instance types, with different processing and interconnection speeds, as well as varying prices. Furthermore, the tasks of many parallel applications show different computational demands due to load imbalance. These differences can be exploited for improving the cost efficiency of parallel applications in many cloud environments by matching application requirements to instance types. In this paper, we introduce the concept of heterogeneous cloud systems consisting of different instance types to leverage the different computational demands of large parallel applications for improved cost efficiency. We present a mechanism that automatically suggests a suitable combination of instances based on a characterization of the application and the instance types. With such a heterogeneous cloud, we are able to improve cost efficiency significantly for a variety of MPI-based applications, while maintaining a similar performance.

show abstract

Section: Methodology Of the Analysismentioning

confidence: 99%

Leveraging Cloud Heterogeneity for Cost-Efficient Execution of Parallel Applications

Roloff

Diener

Carreno

et al. 2017

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…In such work, the 1D-FFT and IS [13], Cholesky and Nbody [14], Maxflow [15], 3D-FFT and MG [16] parallel applications are evaluated executing on real environments. In the experiments, some informations have been captured such as the message sending and receiving moments, size of messages and destination.…”

Section: L(t I)mentioning

confidence: 99%

Model for Simulation of Heterogeneous High-Performance Computing Environments

Mello

Senger

2007

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. This paper proposes a new model to predict the process execution behavior on heterogeneous multicomputing environments. This model considers the process execution costs such as processing, hard disk acessing, message transmitting and memory allocation. A simulator of this model was developed which help to predict the execution behavior of processes on distributed environments under different scheduling techniques. Besides the simulator, it was developed a suite of benchmark tools in order to parameterize the proposed model with data collected from real environments. Experiments were conduced to evaluate the proposed model which used a parallel application executing on a heterogeneous system. The obtained results show the model ability to predict the actual system performance, providing an useful model for developing and evaluating techniques for scheduling and resource allocation over heterogeneous and distributed systems.

show abstract

“…Over time the community came to develop suites of kernels or "mini-apps" representing a spectrum of algorithms in use in HPC, such as the NAS Parallel Benchmarks (Bailey et al, 1991) and the HPC Challenge Suite (Luszczek et al, 2005). These were supposed to characterize a broad range of issues including clock speed, parallel arithmetic, memory bandwidth, cache efficiency, and the like.…”

mentioning

confidence: 99%

CPMIP: measurements of real computational performance of Earth system models in CMIP6

et al. 2017

View full text Add to dashboard Cite

Abstract. A climate model represents a multitude of processes on a variety of timescales and space scales: a canonical example of multi-physics multi-scale modeling. The underlying climate system is physically characterized by sensitive dependence on initial conditions, and natural stochastic variability, so very long integrations are needed to extract signals of climate change. Algorithms generally possess weak scaling and can be I/O and/or memory-bound. Such weakscaling, I/O, and memory-bound multi-physics codes present particular challenges to computational performance.Traditional metrics of computational efficiency such as performance counters and scaling curves do not tell us enough about real sustained performance from climate models on different machines. They also do not provide a satisfactory basis for comparative information across models.We introduce a set of metrics that can be used for the study of computational performance of climate (and Earth system) models. These measures do not require specialized software or specific hardware counters, and should be accessible to anyone. They are independent of platform and underlying parallel programming models. We show how these metrics can be used to measure actually attained performance of Earth system models on different machines, and identify the most fruitful areas of research and development for performance engineering.We present results for these measures for a diverse suite of models from several modeling centers, and propose to use these measures as a basis for a CPMIP, a computational performance model intercomparison project (MIP).

show abstract

The Nas Parallel Benchmarks

Cited by 1,170 publications

References 1 publication

Leveraging Cloud Heterogeneity for Cost-Efficient Execution of Parallel Applications

Leveraging Cloud Heterogeneity for Cost-Efficient Execution of Parallel Applications

Model for Simulation of Heterogeneous High-Performance Computing Environments

CPMIP: measurements of real computational performance of Earth system models in CMIP6

Contact Info

Product

Resources

About