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SUMMARY:MICDE Seminar: John Tramm\, Assistant Computational Scientist\, Argonne National Laboratory
DESCRIPTION:Dr. John Tramm is an assistant scientist in the computational science division at Argonne National Laboratory. He received his PhD in computational nuclear engineering from MIT in 2018. John’s research efforts are focused on improving neutron transport methods that drive massively parallel simulations of nuclear reactors using some of the world’s largest supercomputers. John also has deep experience developing and optimizing simulation applications for GPU-based systems. \n  \nTHE RISE OF PORTABLE GPU PROGRAMMING: EXPERIENCES DEVELOPING GPU-BASED SCIENTIFIC SIMULATION APPLICATIONS FOR INTEL\, NVIDIA\, AND AMD GPUs \nHistorically\, portability has not been important for GPU programming as NVIDIA has dominated the high performance computing (HPC) GPU market. With only one major GPU vendor available to choose from\, it has always made sense to develop scientific HPC apps using NVIDIA’s proprietary CUDA programming model. However\, in 2022 both AMD and Intel are releasing HPC GPU products with the intention of competing directly with NVIDIA. In fact\, the world’s first exascale supercomputer (Oak Ridge National Laboratory’s Frontier) is powered by AMD GPUs\, with another even larger exascale supercomputer (Aurora) powered by Intel GPUs set to arrive at Argonne National Laboratory shortly. These new computers highlight a trend not just from CPU to GPU in HPC\, but also a trend from proprietary CUDA into a number of different portable performance models for GPU. Thus\, scientific application developers are now confronted with not only the difficultly of porting or developing apps for GPU architectures\, but also with selecting from a wide variety of portable GPU programming models (for instance\, OpenMP offloading\, HIP\, SYCL/DPC++\, OpenCL\, Kokkos\, RAJA\, and OCCA). \nIn this talk\, I will briefly introduce the newest supercomputing systems and will give an overview of the many different portable performance models now available for GPUs. I will show a few snippets of an example kernel implemented in a variety of different models\, and will even compare performance of a scientific mini-app\, XSBench\, across all major programming models and GPU architectures. Subjective “pros and cons” of each programming model will be discussed along with quantitative performance comparisons. Next\, I will use a full scientific GPU application (the OpenMC Monte Carlo particle transport code) as a case study to discuss real-world issues affecting portable scientific GPU applications and how bleeding-edge GPU compiler technology stacks are faring. I will also briefly discuss a few of the algorithmic performance optimizations that we developed for OpenMC to give a feel for what types of changes are required to achieve high performance on GPU. \n  \n\n  \nThe MICDE Fall 2022 Seminar Series is open to all. \nThis seminar is hosted by the Michigan Institute for Computational Discovery & Engineering (MICDE). Dr. Tramm will be hosted by Prof. Brendan Kochunas\, Assistant Professor of Nuclear Engineering and Radiological Sciences. \nThis is an in-person event\, Zoom link will only be provided upon request. This seminar will not be recorded! \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-john-tramm-assistant-computational-scientist-argonne-national-laboratory/
LOCATION:1010 H. H. Dow\, 2300 Hayward St\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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SUMMARY:MICDE Seminar: Reese Jones\, Distinguished Member of the Technical Staff\, Sandia National Laboratories
DESCRIPTION:Reese Jones is currently a staff scientist at Sandia National Laboratories in Livermore\, CA. He is engaged in materials science and computational physics research with scales ranging from atomic/molecular to the continuum. He has made contributions to multi-scale methods\, electrochemical and thermal transport\, atomic-level fracture\, and contact. Recently he has been developing and applying machine learning methods to provide constitutive models for\ncomplex materials\, quantify material uncertainty\, and interpret materials imaging for reliability analysis. \nPREDICTING FAILURE IN POROUS METALS USING CONVOLUTIONAL NEURAL NETWORKS \nPredicting whether defects are critical or not is a high-value task in medicine\, materials engineering\, and other fields. Tools that augment expert opinion are needed in the current era of high resolution imaging that can reveal an overwhelming number of defects. In particular\, porosity is a persistent feature of additively manufactured materials and determines failure locations through complex mechanics that exhibit sensitivity to the initial pore locations. In the case of materials engineering expensive direct numerical simulations are available and can be used to train efficient surrogates. Neural networks\, such as the one we have developed\, enable more complete analysis of potential outcomes. \nIn this work\, we develop convolutional neural networks as surrogate models for predicting failure\nlocations. The binary classification problem of categorizing intact/failed voxels is first regularized by recasting it as a regression problem for the continuous damage field subjected to pre-processing transformations. An apparent challenge is the damage fields display a relatively small number of voxels close to failure leading to a form of class imbalance for regression that can cause the optimizer to converge to a poor local minimum. We address this through a re-weighting of the loss function which accounts for the relative frequencies of damage values. Another challenging aspect is the high sensitivity of the outcomes to the porosity field which typically creates multiple regions of high damage competing for failure. This motivates the use of Bayesian neural networks to capture sensitivities in the prediction through uncertainty quantification. We use these uncertainties to rank the likelihood of failure of any particular cluster of porosity in a reliability analysis. Lastly\, to aid transferability of the network and reduce the training burden when it is applied to new materials and processes\, we are exploring transfer learning techniques. \n  \n\n  \nThe MICDE Fall 2022 Seminar Series is open to all. \nThis seminar is hosted by the Michigan Institute for Computational Discovery & Engineering (MICDE). Dr. Jones will be hosted by Prof. Krishna Garikipati\, Professor of Mechanical Engineering and Mathematics and Director of MICDE. \nThis is an in-person event\, Zoom link will only be provided upon request. This seminar will not be recorded. \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-reese-jones-distinguished-member-of-the-technical-staff-sandia-national-laboratories/
LOCATION:1303 EECS\, 1301 Beal Ave\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Education,Featured Events,MICDE Seminar Series,Seminar
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