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The pitfalls of provisioning exascale networks: A trace replay analysis for understanding communication performance

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Kenny, Joseph P.; Sargsyan, Khachik S.; Knight, Samuel K.; Michelogiannakis, George; Wilke, Jeremiah J.

Data movement is considered the main performance concern for exascale, including both on-node memory and off-node network communication. Indeed, many application traces show significant time spent in MPI calls, potentially indicating that faster networks must be provisioned for scalability. However, equating MPI times with network communication delays ignores synchronization delays and software overheads independent of network hardware. Using point-to-point protocol details, we explore the decomposition of MPI time into communication, synchronization and software stack components using architecture simulation. Detailed validation using Bayesian inference is used to identify the sensitivity of performance to specific latency/bandwidth parameters for different network protocols and to quantify associated uncertainties. The inference combined with trace replay shows that synchronization and MPI software stack overhead are at least as important as the network itself in determining time spent in communication routines.

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TYPE Conference Poster YEAR 2018
ScopusOSTI

UQTk Version 3.0.4 User Manual

Sargsyan, Khachik S.; Safta, Cosmin S.; Chowdhary, Kamaljit S.; Castorena, Sarah C.; de Bord, Sarah d.; Debusschere, Bert D.

The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncertainty in numerical model predictions. Version 3.0.4 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sensitivity analysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.

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TYPE SAND Report YEAR 2017
OSTIDOI

Low-rank canonical-tensor decomposition of potential energy surfaces: application to grid-based diagrammatic vibrational Green’s function theory

Molecular Physics

Rai, Prashant R.; Sargsyan, Khachik S.; Najm, H.N.; Hermes, Matthew R.; Hirata, So

A new method is proposed for a fast evaluation of high-dimensional integrals of potential energy surfaces (PES) that arise in many areas of quantum dynamics. It decomposes a PES into a canonical low-rank tensor format, reducing its integral into a relatively short sum of products of low-dimensional integrals. The decomposition is achieved by the alternating least squares (ALS) algorithm, requiring only a small number of single-point energy evaluations. Therefore, it eradicates a force-constant evaluation as the hotspot of many quantum dynamics simulations and also possibly lifts the curse of dimensionality. This general method is applied to the anharmonic vibrational zero-point and transition energy calculations of molecules using the second-order diagrammatic vibrational many-body Green's function (XVH2) theory with a harmonic-approximation reference. In this application, high dimensional PES and Green's functions are both subjected to a low-rank decomposition. Evaluating the molecular integrals over a low-rank PES and Green's functions as sums of low-dimensional integrals using the Gauss–Hermite quadrature, this canonical-tensor-decomposition-based XVH2 (CT-XVH2) achieves an accuracy of 0.1 cm−1 or higher and nearly an order of magnitude speedup as compared with the original algorithm using force constants for water and formaldehyde.

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TYPE Journal Article YEAR 2017
ScopusOSTIDOI

Uncertainty quantification toolkit (UQTk)

Handbook of Uncertainty Quantification

Debusschere, Bert D.; Sargsyan, Khachik S.; Safta, Cosmin S.; Chowdhary, Kenny

The UQ Toolkit (UQTk) is a collection of tools for uncertainty quantification, ranging from intrusive and nonintrusive forward propagation of uncertainty to inverse problems and sensitivity analysis. This chapter first outlines the UQTk design philosophy, followed by an overview of the available methods and the way they are implemented in UQTk. The second part of this chapter is a detailed example that illustrates a UQ workflow from surrogate construction, and calibration, to forward propagation and attribution.

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TYPE Book YEAR 2017
ScopusOSTI

UQTk Version 3.0.3 User Manual

Sargsyan, Khachik S.; Safta, Cosmin S.; Chowdhary, Kamaljit S.; Castorena, Sarah C.; de Bord, Sarah d.; Debusschere, Bert D.

The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncertainty in numerical model predictions. Version 3.0.3 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sen- sitivity analysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.

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TYPE SAND Report YEAR 2017
OSTIDOI

Uncertainty quantification in LES of channel flow

International Journal for Numerical Methods in Fluids

Safta, Cosmin S.; Blaylock, Myra L.; Templeton, Jeremy A.; Domino, Stefan P.; Sargsyan, Khachik S.; Najm, H.N.

In this paper, we present a Bayesian framework for estimating joint densities for large eddy simulation (LES) sub-grid scale model parameters based on canonical forced isotropic turbulence direct numerical simulation (DNS) data. The framework accounts for noise in the independent variables, and we present alternative formulations for accounting for discrepancies between model and data. To generate probability densities for flow characteristics, posterior densities for sub-grid scale model parameters are propagated forward through LES of channel flow and compared with DNS data. Synthesis of the calibration and prediction results demonstrates that model parameters have an explicit filter width dependence and are highly correlated. Discrepancies between DNS and calibrated LES results point to additional model form inadequacies that need to be accounted for. Copyright © 2016 John Wiley & Sons, Ltd.

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TYPE Journal Article YEAR 2017
ScopusOSTIDOI

Performance scaling variability and energy analysis for a resilient ULFM-based PDE solver

Proceedings of ScalA 2016: 7th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems - Held in conjunction with SC16: The International Conference for High Performance Computing, Networking, Storage and Analysis

Morris, K.; Rizzi, F.; Cook, B.; Mycek, P.; LeMaitre, O.; Knio, O.M.; Sargsyan, Khachik S.; Dahlgren, K.; Debusschere, Bert D.

We present a resilient task-based domain-decomposition preconditioner for partial differential equations (PDEs) built on top of User Level Fault Mitigation Message Passing Interface (ULFM-MPI). The algorithm reformulates the PDE as a sampling problem, followed by a robust regression-based solution update that is resilient to silent data corruptions (SDCs). We adopt a server-client model where all state information is held by the servers, while clients only serve as computational units. The task-based nature of the algorithm and the capabilities of ULFM complement each other to support missing tasks, making the application resilient to clients failing.We present weak and strong scaling results on Edison, National Energy Research Scientific Computing Center (NERSC), for a nominal and a fault-injected case, showing that even in the presence of faults, scalability tested up to 50k cores is within 90%. We then quantify the variability of weak and strong scaling due to the presence of faults. Finally, we discuss the performance of our application with respect to subdomain size, server/client configuration, and the interplay between energy and resilience.

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TYPE Conference Poster YEAR 2017
ScopusOSTI

Inference of reaction rate parameters based on summary statistics from experiments

Proceedings of the Combustion Institute

Khalil, Mohammad K.; Chowdhary, K.; Safta, Cosmin S.; Sargsyan, Khachik S.; Najm, H.N.

Bayesian inference and maximum entropy methods were employed for the estimation of the joint probability density for the Arrhenius rate parameters of the rate coefficient of the H2/O2-mechanism chain branching reaction H + O2 → OH + O. A consensus joint posterior on the parameters was obtained by pooling the posterior parameter densities given each consistent data set. Efficient surrogates for the OH concentration were constructed using a combination of Padé and polynomial approximants. Gauss-Hermite quadrature with Gaussian proposal probability density functions for moment computation were used resulting in orders of magnitude speedup in data likelihood evaluation. The consistent data sets resulted in nearly Gaussian conditional parameter probability density functions. The resulting pooled parameter probability density function was propagated through stoichiometric H2-air auto-ignition computations to illustrate the necessity for correlation among the Arrhenius rate parameters of one reaction and across rate parameters of different reactions to be considered.

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TYPE Journal Article YEAR 2017
ScopusOSTIDOI

Uncertainty Quantification in LES Computations of Turbulent Multiphase Combustion in a Scramjet Engine ? ScramjetUQ ?

Najm, H.N.; Debusschere, Bert D.; Safta, Cosmin S.; Sargsyan, Khachik S.; Huan, Xun H.; Oefelein, Joseph C.; Lacaze, Guilhem M.; Vane, Zachary P.; Eldred, Michael S.; Geraci, Gianluca G.; Knio, Omar K.; Sraj, I.S.; Scovazzi, G.S.; Colomes, O.C.; Marzouk, Y.M.; Zahm, O.Z.; Menhorn, F.M.; Ghanem, R.G.; Tsilifis, P.T.

Abstract not provided.

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TYPE Conference Poster YEAR 2016
OSTI

Uncertainty Quantification in LES Computations of Turbulent Multiphase Combustion in a Scramjet Engine

Najm, H.N.; Debusschere, Bert D.; Safta, Cosmin S.; Sargsyan, Khachik S.; Huan, Xun H.; Oefelein, Joseph C.; Lacaze, Guilhem M.; Vane, Zachary P.; Eldred, Michael S.; Geraci, G.G.; Knio, O.K.; Sraj, I.S.; Scovazzi, G.S.; Colomes, O.C.; Marzouk, Y.M.; Zahm, O.Z.; Augustin, F.A.; Menhorn, F.M.; Ghanem, R.G.; Tsilifis, P.T.

Abstract not provided.

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TYPE Conference Poster YEAR 2016
OSTI

UQTk Version 3.0 User Manual

Sargsyan, Khachik S.; Safta, Cosmin S.; Chowdhary, Kamaljit S.; Castorena, Sarah C.; de Bord, Sarah d.; Debusschere, Bert D.

The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncer- tainty in numerical model predictions. Version 3.0 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sensitivity anal- ysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.

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TYPE SAND Report YEAR 2016
OSTIDOI
Results 51–100 of 235
Results 51–100 of 235