Netgauge: A Network Performance Measurement Framework

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

Schneider

Lumsdaine

2009

Self Cite

Section: Benchmark Resultsmentioning

confidence: 99%

The impact of network noise at large-scale communication performance

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

Schneider

Lumsdaine

2009

Self Cite

“…The network can either be benchmarked at smaller scale with network measurement tools [18][19][20][21][22] or modeled analytically [23]. The application models will provide insight into the communication pattern and expected data sizes.…”

Section: Modeling During System Design and Proposalmentioning

confidence: 99%

“…However, if this is not the case then the user can establish such models with the empirical modeling method. Several benchmarks are available to gather the parameters [18][19][20][21][22].…”

Section: Step E2: Determine Communication Parametersmentioning

confidence: 99%

Performance modeling for systematic performance tuning

State of the Practice Reports

Gropp

Kramer

et al. 2011

Self Cite

The performance of parallel scientific applications depends on many factors which are determined by the execution environment and the parallel application. Especially on large parallel systems, it is too expensive to explore the solution space with series of experiments. Deriving analytical models for applications and platforms allow estimating and extrapolating their execution performance, bottlenecks, and the potential impact of optimization options. We propose to use such "performance modeling" techniques beginning from the application design process throughout the whole software development cycle and also during the lifetime of supercomputer systems. Such models help to guide supercomputer system design and re-engineering efforts to adopt applications to changing platforms and allow users to estimate costs to solve a particular problem. Models can often be built with the help of well-known performance profiling tools. We discuss how we successfully used modeling throughout the proposal, initial testing, and beginning deployment phase of the Blue Waters supercomputer system.

“…We implemented our approach as a new communication pattern in the extensible open source Netgauge tool [28]. Netgauge is a modular network benchmarking tool that uses high-precision timers (e.g., the x86 RDTSC instruction [29]) to benchmark times accurately.…”

Section: Applying the Methodsmentioning

confidence: 99%

LogGP in theory and practice – An in-depth analysis of modern interconnection networks and benchmarking methods for collective operations

Simulation Modelling Practice and Theory

Schneider

Lumsdaine

2009

Accurate measurement and modeling of network performance is important for predicting and optimizing the running time of high-performance computing applications. Although the LogP family of models has proven to be a valuable tool for assessing the communication performance of parallel architectures, non-intrusive LogP parameter assessment of real systems remains a difficult task. Based on an analysis of accuracy and contention properties of existing measurement methods, we develop a new low-overhead measurement method which also assesses protocol changes in the underlying transport layers. We use the gathered parameters to simulate LogGP models of collective operations and demonstrate the errors in common benchmarking methods for collective operations. The simulations provide new insight into the nature of collective algorithms and their pipelining properties. We show that the error grows linearly with the system size.