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检索条件"任意字段=5th IEEE/ACM Workshop on Machine Learning in HPC Environments, MLHPC 2019"

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Proceedings of mlhpc 2019: 5th workshop on machine learning in hpc environments - Held in conjunction with SC 2019: the International Conference for High Performance Computing, Networking, Storage and Analysis

Proceedings of MLHPC 2019: 5th Workshop on Machine Learning ...

引用

5th ieee/acm workshop on machine learning in hpc environments, mlhpc 2019

ISBN: (纸本)9781728159850

the proceedings contain 8 papers. the topics discussed include: scalable hyperparameter optimization with lazy Gaussian processes;understanding scalability and fine-grain parallelism of synchronous data parallel training;DisCo: physics-based unsupervised discovery of coherent structures in spatiotemporal systems;GradVis: visualization and second order analysis of optimization surfaces during the training of deep neural networks;metaoptimization on a distributed system for deep reinforcement learning;scheduling optimization of parallel linear algebra algorithms using supervised learning;parallel data-local training for optimizing Word2Vec embeddings for word and graph embeddings;and fine-grained exploitation of mixed precision for faster CNN training.

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Understanding Scalability and Fine-Grain Parallelism of Synchronous Data Parallel Training 5

Understanding Scalability and Fine-Grain Parallelism of Sync...

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5th ieee/acm workshop on machine learning in High Performance Computing environments (mlhpc)

作者： Li, Jiali Nicolae, Bogdan Wozniak, Justin Bosilca, George Univ Tennessee Knoxville TN 37996 USA Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9781728159850

In the age of big data, deep learning has emerged as a powerful tool to extract insight and exploit its value, both in industry and scientific applications. With increasing complexity of learning models and amounts of training data, data-parallel approaches based on frequent all-reduce synchronization steps are increasingly popular. Despite the fact that high performance computing (hpc) technologies have been designed to address such patterns efficiently, the behavior of data-parallel approaches on hpc platforms is not well understood. To address this issue, in this paper we study the behavior of Horovod, a popular data parallel approach that relies on MPI, on theta, a pre-Exascale machine at Argonne National Laboratory. Using two representative applications, we explore two aspects: (1) how performance and scalability is affected by important parameters such as number of nodes, number of workers, threads per node, batch size;(2) how computational phases are interleaved with all-reduce communication phases at fine granularity and what consequences this interleaving has in terms of potential bottle-necks. Our findings show that pipelining of back-propagation, gradient reduction and weight updates mitigate the effects of stragglers during all-reduce only partially. Furthermore, there can be significant delays between weight updates, which can be leveraged to mask the overhead of additional background operations that are coupled with the training.

关键词： deep learning data-parallel training behavior analysis

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Metaoptimization on a Distributed System for Deep Reinforcement learning 5

Metaoptimization on a Distributed System for Deep Reinforcem...

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5th ieee/acm workshop on machine learning in High Performance Computing environments (mlhpc)

作者： Heinrich, Greg Frosio, Iuri NVIDIA Nice France NVIDIA Santa Clara CA USA

ISBN: (纸本)9781728159850

Training intelligent agents through reinforcement learning (RL) is a notoriously unstable procedure. Massive parallelization on GPUs and distributed systems has been exploited to generate a large amount of training experiences and consequently reduce instabilities, but the success of training remains strongly influenced by the choice of the hyperparameters. To overcome this issue, we introduce HyperTrick, a new metaoptimization algorithm, and show its effective application to tune hyperparameters in the case of deep RL, while learning to play different Atari games on a distributed system. Our analysis provides evidence of the interaction between the identification of the optimal hyperparameters and the learned policy, that is peculiar of the case of metaoptimization for deep RL. When compared with state-of-the-art metaoptimization algorithms, HyperTrick is characterized by a simpler implementation and it allows learning similar policies, while making a more effective

关键词： Reinforcement learning distributed systems metaoptimization

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Scheduling optimization of parallel linear algebra algorithms using Supervised learning 5

Scheduling optimization of parallel linear algebra algorithm...

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5th ieee/acm workshop on machine learning in High Performance Computing environments (mlhpc)

作者： Laberge, Gabriel Shirzad, Shahrzad Diehl, Patrick Kaiser, Hartmut Prudhomme, Serge Lemoine, Adrian S. Polytech Montreal Dept Math & Ind Engn Montreal PQ Canada Louisiana State Univ Ctr Computat & Technol Baton Rouge LA 70803 USA

ISBN: (纸本)9781728159850

Linear algebra algorithms are used widely in a variety of domains, e.g machine learning, numerical physics and video games graphics. For all these applications, loop-level parallelism is required to achieve high performance. However, finding the optimal way to schedule the workload between threads is a non-trivial problem because it depends on the structure of the algorithm being parallelized and the hardware the executable is run on. In the realm of Asynchronous Many Task runtime systems, a key aspect of the scheduling problem is predicting the proper chunk-size, where the chunk-size is defined as the number of iterations of a for-loop assigned to a thread as one task. In this paper, we study the applications of supervised learning models to predict the chunk-size which yields maximum performance on multiple parallel linear algebra operations using the HPX backend of Blaze's linear algebra library. More precisely, we generate our training and tests sets by measuring performance of the application with different chunk-sizes for multiple linear algebra operations;vector-addition, matrix-vector-multiplication, matrix-matrix addition and matrix-matrix-multiplication. We compare the use of logistic regression, neural networks and decision trees with a newly developed decision tree based model in order to predict the optimal value for chunk-size. Our results show that classical decision trees and our custom decision tree model are able to forecast a chunk-size which results in good performance for the linear algebra operations.

关键词： hpc linear algebra loop-level parallelism supervised learning HPX Blaze AMT

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GradVis: Visualization and Second Order Analysis of Optimization Surfaces during the Training of Deep Neural Networks 5

GradVis: Visualization and Second Order Analysis of Optimiza...

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5th ieee/acm workshop on machine learning in High Performance Computing environments (mlhpc)

作者： Chatzimichailidis, Avraam Pfreundt, Franz-Josef Gauger, Nicolas R. Keuper, Janis Fraunhofer ITWM Competence Ctr High Performance Comp Kaiserslautern Germany Fraunhofer Ctr Machine Learning Berlin Germany TU Kaiserslautern Chair Sci Comp Kaiserslautern Germany Offenburg Univ Inst Machine Learning & Analyt Offenburg Germany

ISBN: (纸本)9781728159850

Current training methods for deep neural networks boil down to very high dimensional and non-convex optimization problems which are usually solved by a wide range of stochastic gradient descent methods. While these approaches tend to work in practice, there are still many gaps in the theoretical understanding of key aspects like convergence and generalization guarantees, which are induced by the properties of the optimization surface (loss landscape). In order to gain deeper insights, a number of recent publications proposed methods to visualize and analyze the optimization surfaces. However, the computational cost of these methods are very high, making it hardly possible to use them on larger networks. In this paper, we present the GradVis Toolbox, an open source library for efficient and scalable visualization and analysis of deep neural network loss landscapes in Tensorflow and PyTorch. Introducing more efficient mathematical formulations and a novel parallelization scheme, GradVis allows to plot 2d and 3d projections of optimization surfaces and trajectories, as well as high resolution second order gradient information for large networks.

关键词： visualization eigenvalues parallelization deeplearning second-order

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Scalable Hyperparameter Optimization with Lazy Gaussian Processes 5

Scalable Hyperparameter Optimization with Lazy Gaussian Proc...

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5th ieee/acm workshop on machine learning in High Performance Computing environments (mlhpc)

作者： Ram, Raju Mueller, Sabine Pfreundt, Franz-Josef Gauger, Nicolas R. Keuper, Janis Fraunhofer ITWM Competence Ctr High Performance Comp Kaiserslautern Germany Fraunhofer Ctr Machine Learning Berlin Germany TU Kaiserslautern Sci Comp Grp Kaiserslautern Germany Offenburg Univ Inst Machine Learning & Analyt Offenburg Germany

ISBN: (纸本)9781728159850

Most machine learning methods require careful selection of hyper-parameters in order to train a high performing model with good generalization abilities. Hence, several automatic selection algorithms have been introduced to overcome tedious manual (try and error) tuning of these parameters. Due to its very high sample efficiency, Bayesian Optimization over a Gaussian Processes modeling of the parameter space has become the method of choice. Unfortunately, this approach suffers from a cubic compute complexity due to underlying Cholesky factorization, which makes it very hard to be scaled beyond a small number of sampling steps. In this paper, we present a novel, highly accurate approximation of the underlying Gaussian Process. Reducing its computational complexity from cubic to quadratic allows an efficient strong scaling of Bayesian Optimization while outperforming the previous approach regarding optimization accuracy. First experiments show speedups of a factor of 162 in single node and further speed up by a factor of 5 in a parallel environment.

关键词： Hyper-Parameter Optimization Neural Architecture Search Bayesian Optimization Gaussian Processes Scalability

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Parallel Data-Local Training for Optimizing Word2Vec Embeddings for Word and Graph Embeddings 5

Parallel Data-Local Training for Optimizing Word2Vec Embeddi...

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5th ieee/acm workshop on machine learning in High Performance Computing environments (mlhpc)

作者： Moon, Gordon E. Newman-Griffis, Denis Kim, Jinsung Sukumaran-Rajam, Aravind Fosler-Lussier, Eric Sadayappan, P. Ohio State Univ Comp Sci & Engn Columbus OH 43210 USA Univ Utah Sch Comp Salt Lake City UT USA

ISBN: (纸本)9781728159850

the Word2Vec model is a neural network-based unsupervised word embedding technique widely used in applications such as natural language processing, bioinformatics and graph mining. As Word2Vec repeatedly performs Stochastic Gradient Descent (SGD) to minimize the objective function, it is very compute-intensive. However, existing methods for parallelizing Word2Vec are not optimized enough for data locality to achieve high performance. In this paper, we develop a parallel data-locality-enhanced Word2Vec algorithm based on Skip-gram with a novel negative sampling method that decouples loss calculation with positive and negative samples;this allows us to efficiently reformulate matrix-matrix operations for the negative samples over the sentence. Experimental results demonstrate our parallel implementations on multi-core CPUs and GPUs achieve significant performance improvement over the existing state-of-the-art parallel Word2Vec implementations while maintaining evaluation quality. We also show the utility of ourWord2Vec implementation within the Node2Vec algorithm which accelerates embedding learning for large graphs.

关键词： Parallel machine learning Unsupervised learning learning Latent Representations Parallel Word2Vec Node2Vec Word Embedding Graph Embedding

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Message from the workshop chair

Proceedings of MLHPC 2019: 5th Workshop on Machine Learning ...

引用

Proceedings of mlhpc 2019: 5th workshop on machine learning in hpc environments - Held in conjunction with SC 2019: the International Conference for High Performance Computing, Networking, Storage and Analysis 2019年 V页

作者： Lim, Seung-Hwan Oak Ridge National Laboratory United States

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PAVE: An In Situ Framework for Scientific Visualization and machine learning Coupling 5

PAVE: An In Situ Framework for Scientific Visualization and ...

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5th ieee/acm International workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD)

作者： Leventhal, Samuel Kim, Mark Pugmire, David Univ Utah Sch Comp Sci Comp & Imaging Inst Salt Lake City UT 84112 USA Oak Ridge Natl Lab Oak Ridge TN USA

ISBN: (纸本)9781728160177

machine learning (ML) has emerged as a tool for understanding data at scale. However, this new methodology comes at a cost because even more hpc resources are required to generate ML algorithms. In addition to the compute resources required to develop ML algorithms, ML does not sidestep one of the biggest challenges on leading-edge hpc systems: the increasing gap between compute performance and I/O bandwidth. this has led to a strong push towards in situ designs by processing data as it is generated and developing strategies to mitigate the I/O bottleneck. Unfortunately, there are no in situ frameworks dedicated to coupling scientific visualization and ML at scale to develop ML algorithms for scientific visualization. To address the ML and in situ visualization gap, we introduce PAVE. PAVE is an in situ framework which addresses the data management needs between visualisation and machine learning tasks. We demonstrate our framework with a case study that accelerates physically-based light rendering, path-tracing, through the use of a conditional Generative Adversarial neural Network (cGAN). PAVE couples the training over path-traced images resulting in a generative model able to produce scene renderings with accurate light transport and global illumination of a quality comparable to offline approaches in a more efficient manner.

关键词： VTKm neural networks generative adversarial network in situ PyTorch path-tracing

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Using machine learning to reduce ensembles of geological models for oil and gas exploration 5

Using machine learning to reduce ensembles of geological mod...

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5th ieee/acm International workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD)

作者： Roubickova, Anna Brown, Nick Brown, Oliver thomson MacGregor, Lucy Stewart, Mike Univ Edinburgh EPCC Edinburgh Midlothian Scotland Cognit Geol Edinburgh Midlothian Scotland

ISBN: (纸本)9781728160177

Exploration using borehole drilling is a key activity in determining the most appropriate locations for the petroleum industry to develop oil fields. However, estimating the amount of Oil In Place (OIP) relies on computing with a very significant number of geological models, which, due to the ever increasing capability to capture and refine data, is becoming infeasible. As such, data reduction techniques are required to reduce this set down to a smaller, yet still fully representative ensemble. In this paper we explore different approaches to identifying the key grouping of models, based on their most important features, and then using this information select a reduced set which we can be confident fully represent the overall model space. the result of this work is an approach which enables us to describe the entire state space using only 0.5% of the models, along with a series of lessons learnt. the techniques that we describe are not only applicable to oil and gas exploration, but also more generally to the hpc community as we are forced to work with reduced data-sets due to the rapid increase in data collection capability.

关键词： Data reduction machine learning Geological models Oil and gas exploration

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