Resources

We have collected presentations from IXPUG workshops, annual meetings, and BOF sessions, and made them accessible here to view or download. You may search by event, keyword, science domain or author’s name. The database will be updated as new talks are made available.

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Search ResultShowing 1 - 10 of 356 Results

IXPUG Annual Fall Conference 2018 Dec 27, 2018

Here an optimization strategy based on code modernization concept is proposed and applies to the global MASNUM surface wave model, which has been used in several operational forecasting systems and earth system models.

Keyword(s): masnum,wave model

Author(s): Zhenya Song
Video(s): Optimization strategy for MASNUM surface wave model
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IXPUG Annual Fall Conference 2018 Dec 27, 2018

We present a complementary physics based, unsupervised approach that exploits the causal nature of spatiotemporal data sets generated by local dynamics (e.g. hydrodynamic flows). We illustrate how novel patterns and coherent structures can be discovered in cellular automata and outline the path from them to climate data.

Keyword(s): unsupervised learning,parallel programming

Author(s): Adam Rupe, Karthik Kashinath, James Crutchfield, Ryan James, Prabhat
Video(s): Project DisCo: Physics-based discovery of coherent structures in spatiotemporal systems
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IXPUG Annual Fall Conference 2018 Dec 27, 2018

Our work proposes several optimization techniques to improve the performance of a wave propagation model provided by Petrobras, a multinational corporation in the petroleum industry.

Keyword(s): performance optimization,oil & gas

Author(s): Eduardo Cruz, Philippe Navaux
Video(s): Improving Oil and Gas Extraction Simulation Performance using Intel® Xeon® and Xeon Phi™ Architectures
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IXPUG Annual Fall Conference 2018 Dec 27, 2018

The Energy Exascale Earth System Model (E3SM) is one of the top users of resources at NERSC, of which the Model for Prediction Across Scales - Ocean Core (MPAS-O) is a significant component, composed of 800,000 lines of Fortran and work by 50 contributors. When MPAS-O is migrated from the previous generation NERSC production system, Edison which hosts Ivy Bridge processors to the newer Knights Landing based Cori system, severe performance loss and scaling bottlenecks result. Performance analysis was used to reject a number of possible causes of this effect including load imbalance, cache behavior, and vectorization efficiency. It was found that a lower bound on the number of simulation cells mapped to an MPI rank combined with MPAS framework overhead caused by serialized thread structure is the overwhelming contributor to MPAS performance loss on Xeon Phi systems. Two framework optimizations which remove excessive thread barriers and recycle communications data structures have been incorporated into the E3SM master codebase for a 15% speed improvement when running MPAS-O at production scale on Xeon Phi processors.

Keyword(s): MPI,Climate and weather

Author(s): William Arndt
Video(s): Optimization of the Model for Prediction Across Scales: Ocean Core targeting Production Scale Use of Knights Landing Processor Architecture
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IXPUG Annual Fall Conference 2018 Dec 27, 2018

This work significantly improves the OpenMP threading performance of Quantum ESRESSO (QE) on Xeon and Xeon Phi processors.

Keyword(s): OpenMP,Density functional theory,3D FFT

Author(s): Ye Luo
Video(s): Improved threading performance of Quantum ESPRESSO
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IXPUG Annual Fall Conference 2018 Dec 27, 2018

With DM-HEOM, we developed the first Distributed Memory implementation of the HEOM method. In this talk we describe our interdisciplinary development workflow and provide guidelines and experiences for designing distributed, performance-portable HPC applications.

Keyword(s): HEOM,performance portability,OpenCL,MPI

Author(s): Matthias Noack
Video(s): DM-HEOM: A Case Study in Performance Portability
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IXPUG Annual Fall Conference 2018 Dec 27, 2018

SPARTA Heterogeneous Full Trinity Runs: Successes and Challenges

Keyword(s): keynote

Author(s): Stan Moore
Video(s): Keynote: SPARTA Heterogeneous Full Trinity Runs: Successes and Challenges
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IXPUG Annual Fall Conference 2018 Dec 27, 2018

In this brief presentation we show that the VTune™ Amplifier Application Performance Snapshot provides rich performance data, including IO, Memory, FPU, MPI and OpenMP details with no user setup, no recompilation, and minimal overhead. The Application Performance Snapshot tool collects whole program execution data and highlights problematic areas and the appropriate tools to investigate them further if needed.

Keyword(s): Performance Optimization,Profiling

Author(s): Carlos Rosales Fernandez, Dmitry Prohorov
Video(s): Configuration-Free Profiling at Scale
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IXPUG Annual Fall Conference 2018 Dec 27, 2018

The ability to run high-resolution global simulations efficiently on the world’s largest computers is a priority for the DOE. In this talk we demonstrate the optimizations carried out in the code that allow us to reach the required performance parameters as specified by the MPAS-O code developers.

Keyword(s): Weather,Compilers,Tools,Vectorization

Author(s): Timbwaoga Ouermi
Video(s): Performance Optimization Techniques for Accelerating WRF Physics Codes on Intel Micro-Architectures
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IXPUG Annual Fall Conference 2018 Dec 27, 2018

Maximum likelihood estimation is an important statistical technique for estimating missing data, for example in climate and environmental applications, which are usually large and feature data points that are irregularly spaced. In particular, the Gaussian log-likelihood function is the de facto model, which operates on the resulting sizable dense covariance matrix. The advent of high-performance systems with advanced computing power and memory capacity have enabled full simulations only for rather small dimensional climate problems, solved at the machine precision accuracy. The challenge for high dimensional problems lies in the computation requirements of the log-likelihood function, which necessitates O(n^2) storage and O(n^3) operations, where n represents the number of given spatial locations. This prohibitive computational cost may be reduced by using approximation techniques that not only enable large-scale simulations otherwise intractable but also maintain the accuracy and the fidelity of the spatial statistics model. In this paper, we extend the Exascale GeoStatistics software (i.e., ExaGeoStat) to support the Tile Low-Rank (TLR) approximation, which exploits the data sparsity of the dense covariance matrix by compressing the off-diagonal tiles up to a user-defined accuracy threshold. The underlying linear algebra operations may then be carried out on this data compression format, which may ultimately reduce the arithmetic complexity of the maximum likelihood estimation and the corresponding memory footprint. We analyze the performance of the proposed framework across most recent Intel architectures. We achieve up to 4.48X speedup using TLR approximation compared to a full-time solution on Intel 56-core salable Skylake chip, 9.16X speedup on Intel 28-core Broadwell chip, 6.21X speedup on Intel 36-Haswell chip, and 3X speedup on Knights Landing (KNL) chip. With a distributed system, performance results of TLR-based computations on Shaheen-II attain up to 5X speedup, compared to full accuracy simulations using synthetic and real datasets (up to 2M), while ensuring adequate prediction accuracy.

Keyword(s): maximum likelihood estimation,optimization

Author(s): Hatem Ltaief, Sameh Abdulah, Ying Sun, Marc Genton, David Keyes
Video(s): A Machine Learning Framework for Large-Scale Weather and Climate Prediction using Exact and Approximate Linear Algebra Computation
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