Improving performance via mini-applications.

Crozier, Paul; Thornquist, Heidi K.; Numrich, Robert W.; Williams, Alan; Edwards, Harold; Keiter, Eric R.; Rajan, M. S. S.; Willenbring, James Michael; Doerfler, Douglas W.; Heroux, Michael A.

doi:10.2172/993908

Cited by 79 publications

(22 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We labeled this benchmark type a "kernel. " Some more recent collections, e.g., Mantevo [8], have expanded into collections of kernels with common co-occurrence, which we have labeled "mini-applications. " Although these two categories can capture most of the information needed for a performance specialist to draw conclusions regarding hardware and toolset suitability, they still represent an indirect measure of performance versus representative problems that are commonly seen in a particular facility.…”

Section: Parallelmentioning

confidence: 99%

The ESIF-HPC-2 benchmark suite

Chang¹,

Carpenter

Jones³

2020

Proceedings of the Workshop on Benchmarking in the Datacenter

View full text Add to dashboard Cite

We describe the development of the ESIF-HPC-2 benchmark suite, a collection of kernel and application benchmark codes for measuring computational and I/O performance from single nodes to full HPC systems that was used for acceptance testing in our recent HPC procurement. The configurations of the benchmarks used for our system is presented. We also describe a set of "dimensions" that can be used to classify benchmarks and assess coverage of a suite systematically. The collection is offered cost-free as a GitHub repository for general usage and further development.

show abstract

Section: Parallelmentioning

confidence: 99%

The ESIF-HPC-2 benchmark suite

Chang¹,

Carpenter

Jones³

2020

Proceedings of the Workshop on Benchmarking in the Datacenter

View full text Add to dashboard Cite

show abstract

“…We used 12 benchmarks from each of Splash2 and Parsec suites . The third benchmark suite is Mantevo [15]. Mantevo pioneered the concept of using miniapps to drive hardware/software co-design.…”

Section: Hardware Performance Counters and Benchmarksmentioning

confidence: 99%

A Study on Cross-Architectural Modelling of Power Consumption Using Neural Networks

2018

JSFI

View full text Add to dashboard Cite

On the path to Exascale, the goal of High Performance Computing (HPC) to achieve maximum performance becomes the goal of achieving maximum performance under strict power constraint. Novel approaches to hardware and software co-design of modern HPC systems have to be developed to address such challenges. In this paper, we study prediction of power consumption of HPC systems using metrics obtained from hardware performance counters. We argue that this methodology is portable across different micro-architecture implementations and compare results obtained on Intel R 64, IBM R POWER TM and Cavium ThunderX R ARMv8 microarchitectures. We discuss optimal number and type of hardware performance counters required to accurately predict power consumption. We compare accuracy of power predictions provided by models based on Linear Regression (LR) and Neural Networks (NN). We find that the NN-based model provides better accuracy of predictions than the LR model. We also find, that presently it is not yet possible to predict power consumption on a given microarchitecture using data obtained on a different microarchitecture. Results of our work can be used as a starting point for developing unified, cross-architectural models for predicting power consumption.

show abstract

“…For example, a group of researchers at Berkeley are using the term "motifs" (previously "dwarfs") to describe methods that use certain common templates for computations and communications [4,5]. Sandia National Laboratories runs the Mantevo project aimed at studying mini-applications, which represent computational kernels for various scientific and engineering applications [6,7]. A similar approach is used by the TORCH project [8][9][10].…”

Section: Background and Related Workmentioning

confidence: 99%

AlgoWiki: an Open Encyclopedia of Parallel Algorithmic Features

Voevodin

Antonov

Dongarra³

2015

JSFI

View full text Add to dashboard Cite

The main goal of this project is to formalize the mapping of algorithms onto the architecture of parallel computing systems. The basic idea is that features of algorithms are independent of any computing system. A detailed description of a given algorithm with a special emphasis on its parallel properties is made once, and after that it can be used repeatedly for various implementations of the algorithm on different computing platforms. Machine-dependent, part of this work is devoted to describing features of algorithms implementation for different parallel architectures. The proposed description of algorithms includes many non-trivial features such as: parallel algorithm complexity, resource of parallelism and its properties, features of the informational graph, computational cost of algorithms, data locality analysis as well as analysis of scalability potential, and many others. Descriptions of algorithms form the basis of AlgoWiki, which allows for collaboration with the computing community in order to produce different implementations and achieve improvement. Project website: http://algowiki-project.org/en/.

show abstract

Improving performance via mini-applications.

Cited by 79 publications

References 17 publications

The ESIF-HPC-2 benchmark suite

The ESIF-HPC-2 benchmark suite

A Study on Cross-Architectural Modelling of Power Consumption Using Neural Networks

AlgoWiki: an Open Encyclopedia of Parallel Algorithmic Features

Contact Info

Product

Resources

About