检索结果-内蒙古大学图书馆

32nd International Conference on Machine Learning, ICML 2015

作者： Snoek, Jasper Ripped, Oren Swersky, Kevin Kiros, Ryan Satish, Nadathur Sundaram, Narayanan Patwary, Md. Mostofa Ali Prabhat Adams, Ryan P. Harvard University School of Engineering and Applied Sciences United States Massachusetts Institute of Technology Department of Mathematics United States University of Toronto Department of Computer Science Canada Intel Labs Parallel Computing Lab Switzerland NERSC Lawrence Berkeley National Laboratory United States

ISBN: (纸本)9781510810587

Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations. It relies on querying a distribution over functions defined by a relatively cheap surrogate model. An accurate model for this distribution over functions is critical to the effectiveness of the approach, and is typically fit using Gaussian processes (GPs). However, since GPs scale cubically with the number of observations, it has been challenging to handle objectives whose optimization requires many evaluations, and as such, massively parallelizing the optimization. In this work, we explore the use of neural networks as an alternative to GPs to model distributions over functions. We show that performing adaptive basis function regression with a neural network as the parametric form performs competitively with state-of-the-art GP-based approaches, but scales linearly with the number of data rather than cubically. This allows us to achieve a previously intractable degree of parallelism, which we apply to large scale hyperparameter optimization, rapidly finding competitive models on benchmark object recognition tasks using convolutional networks, and image caption generation using neural language models. © Copyright 2015 by International Machine Learning Society (IMLS). All rights reserved.

关键词： Global optimization

来源：评论

学校读者我要写书评

暂无评论

Introducing the Quantum Research Kernels: Lessons from Classical parallel computing

arXiv

引用

arXiv 2022年

作者： Matsuura, A.Y. Mattson, Timothy G. Applications and Quantum Architecture Lab Intel Corp HillsboroOR United States Parallel Computing Lab Intel Corp. IlwacoWA United States

Quantum computing represents a paradigm shift for computation requiring an entirely new computer architecture. However, there is much that can be learned from traditional classical computer engineering. In this paper, we describe the parallel Research Kernels (PRK), a tool that was very useful for designing classical parallel computing systems. The PRK are simple kernels written to expose bottlenecks that limit classical parallel computing performance. We hypothesize that an analogous tool for quantum computing, Quantum Research Kernels (QRK), may similarly aid the codesign of software and hardware for quantum computing systems, and we give a few examples of representative QRKs. Copyright © 2022, The Authors. All rights reserved.

关键词： Hardware-software codesign

来源：评论

学校读者我要写书评

暂无评论

Multi-label Classification with Clustering for Image and Text Categorization

Multi-label Classification with Clustering for Image and Tex...

引用

International Congress on Image and Signal Processing

作者： Gulisong Nasierding Atul Sajjanhar School of Computer Science and Technology Xinjiang Normal University Parallel and Distributed Computing Lab School of Information Technology Deakin University

ISBN: (纸本)9781479927654

This paper explores effective multi-label classification methods for multi-semantic image and text categorization. We perform an experimental study of clustering based multi-label classification (CBMLC) for the target problem. Experimental evaluation is conducted for identifying the impact of different clustering algorithms and base classifiers on the predictive performance and efficiency of CBMLC. In the experimental setting, three widely used clustering algorithms and six popular multi-label classification algorithms are used and evaluated on multi-label image and text datasets. A multi-label classification evaluation metrics, micro F1-measure, is used for presenting predictive performances of the classifications. Experimental evaluation results reveal that clustering based multi-label learning algorithms are more effective compared to their non-clustering counterparts.

关键词： Multi-label classification Clustering Image and text categorization

来源：评论

学校读者我要写书评

暂无评论

A semi-supervised method for multi-subject FMRI functional alignment

A semi-supervised method for multi-subject FMRI functional a...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Javier S. Turek Theodore L. Willke Po-Hsuan Chen Peter J. Ramadge Parallel Computing Lab Intel Labs Hillsboro Oregon USA Department of Electrical Engineering Princeton University New Jersey USA

ISBN: (纸本)9781509041183

Practical limitations on the duration of individual fMRI scans have led neuroscientist to consider the aggregation of data from multiple subjects. Differences in anatomical structures and functional topographies of brains require aligning data across subjects. Existing functional alignment methods serve as a preprocessing step that allows subsequent statistical methods to learn from the aggregated multi-subject data. Despite their success, current alignment methods do not leverage the labeled data used in the subsequent methods. In this work we propose a semi-supervised scheme that simultaneously learns the alignment and performs the analysis. We derive a specific instance of the scheme using the Shared Response Model for alignment and Multinomial Logistic Regression for classification. In our experiments this method improves the average classification accuracy from 65.5% to 68.5%, and from 5.3% to 6.1% over the independently-trained methods. Furthermore, our method achieves similar prediction with almost half the samples used for alignment.

关键词： fMRI functional alignment shared response model semi-supervised method

来源：评论

学校读者我要写书评

暂无评论

A quick introduction to functional verification of array-intensive programs

arXiv

引用

arXiv 2019年

作者： Banerjee, Kunal Karfa, Chandan Parallel Computing Lab Intel Corporation Bangalore India Dept. of Computer Sc. & Engg IIT Guwahati Guwahati India

Array-intensive programs are often amenable to parallelization across many cores on a single machine as well as scaling across multiple machines and hence are well explored, especially in the domain of high-performance computing. These programs typically undergo loop transformations and arithmetic transformations in addition to parallelizing transformations. Although a lot of effort has been invested in improving parallelizing compilers, experienced programmers still resort to hand-optimized transformations which is typically followed by careful tuning of the transformed program to finally obtain the optimized program. Therefore, it is critical to verify that the functional correctness of an original sequential program is not sacrificed during the process of optimization. In this paper, we cover important literature on functional verification of array-intensive programs which we believe can be a good starting point for one interested in this field. Copyright © 2019, The Authors. All rights reserved.

关键词： Formal verification

来源：评论

学校读者我要写书评

暂无评论

A location information-based route discovery protocol for mobile ad hoc networks

A location information-based route discovery protocol for mo...

引用

IEEE International Conference on Performance, computing and Communications (IPCCC)

作者： H. Ammari H. El-Rewini Mobile Computing and Parallel Processing Lab Computer Science and Engineering Department School of Engineering Southern Methodist University Dallas TX USA

A mobile ad hoc network (MANET) is a set of mobile nodes that agreed upon forming a temporary, spontaneous network in spite of the lack of any infrastructure or centralized administration. MANET topology changes frequently and arbitrarily due to node mobility. Therefore, an efficient routing protocol is required to cope with the dynamic change of MANET topology, which requires discovering and maintaining new routes, in this paper we propose a route discovery protocol based on location information, which could be provided to MANET nodes by a Global Positioning System (GPS). Our route discovery protocol utilizes the essential features of Voronoi diagrams in order to reduce the search space of routes by allowing only a small subset of MANET nodes, called authorized forwarders, to forward a route request. This results in a restrictive, selective route discovery protocol, which significantly reduces the routing overhead compared to other location-based routing protocol, such as LAR.

关键词： Mobile ad hoc networks Routing protocols Mobile computing Global Positioning System Concurrent computing parallel processing Computer science Network topology Costs Bandwidth

来源：评论

学校读者我要写书评

暂无评论

A study of BFLOAT16 for deep learning training

arXiv

引用

arXiv 2019年

作者： Kalamkar, Dhiraj Mudigere, Dheevatsa Mellempudi, Naveen Das, Dipankar Banerjee, Kunal Avancha, Sasikanth Vooturi, Dharma Teja Jammalamadaka, Nataraj Huang, Jianyu Yuen, Hector Yang, Jiyan Park, Jongsoo Heinecke, Alexander Georganas, Evangelos Srinivasan, Sudarshan Kundu, Abhisek Smelyanskiy, Misha Kaul, Bharat Dubey, Pradeep Parallel Computing Lab Intel Labs Facebook 1 Hacker Way Menlo ParkCA United States IIIT Hyderabad Lab126 Amazon

This paper presents the first comprehensive empirical study demonstrating the efficacy of the Brain Floating Point (BFLOAT16) half-precision format for Deep Learning training across image classification, speech recognition, language modeling, generative networks and industrial recommendation systems. BFLOAT16 is attractive for Deep Learning training for two reasons: the range of values it can represent is the same as that of IEEE 754 floating-point format (FP32) and conversion to/from FP32 is simple. Maintaining the same range as FP32 is important to ensure that no hyper-parameter tuning is required for convergence;e.g., IEEE 754 compliant half-precision floating point (FP16) requires hyper-parameter tuning. In this paper, we discuss the flow of tensors and various key operations in mixed precision training, and delve into details of operations, such as the rounding modes for converting FP32 tensors to BFLOAT16. We have implemented a method to emulate BFLOAT16 operations in Tensorflow, Caffe2, IntelCaffe, and Neon for our experiments. Our results show that deep learning training using BFLOAT16 tensors achieves the same state-of-the-art (SOTA) results across domains as FP32 tensors in the same number of iterations and with no changes to hyper-parameters. Copyright © 2019, The Authors. All rights reserved.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

Memory Access Complexity Analysis of SpMV in RAM (h) Model

Memory Access Complexity Analysis of SpMV in RAM (h) Model

引用

IEEE International Conference on High Performance computing and Communications (HPCC)

作者： E Yuan Yun-quan Zhang Xiangzheng Sun ISCAS Lab. of Parallel Computing China Graduate University of Chinese Academy of Sciences China CAS State Key Lab. of Computer Science China

Sparse matrix-vector multiplication is an important computational kernel in scientific applications, and CSR storage algorithm often performs poorly on modern computer systems. But the register-level blocking algorithm can optimize memory hierarchy access, reduce memory access time, and then improve the performance. RAM (h) is a computation model that has h-level memory hierarchies. It indicates that different implementation forms of the same algorithm can have different memory access complexity. In this paper, we try to analyze Memory access complexity of two implementation forms of SpMV(which are CSR storage algorithm and register- level blocking algorithm) and to predict the performance of SpMV through combining the memory access complexity analysis and the data movement/floating point operation ratio analysis. The performance data of two forms and Statistical results of miss number of II, 12 and TIB on Pentium IV platform are listed. Model analytical results matched well with experimental results.

关键词： Complexity theory Random access memory Sparse matrices Algorithm design and analysis Signal processing algorithms Registers Prediction algorithms

来源：评论

学校读者我要写书评

暂无评论

An insightful program performance tuning chain for GPU computing

An insightful program performance tuning chain for GPU compu...

引用

12th International Conference on Algorithms and Architectures for parallel Processing, ICA3PP 2012

作者： Jia, Haipeng Zhang, Yunquan Long, Guoping Yan, Shengen Lab. of Parallel Software and Computational Science Institute of Software Chinese Academy of Sciences China College of Information Science and Engineering Ocean University of China China State Key Laboratory of Computing Science Chinese Academy of Sciences China Graduate University of Chinese Academy of Sciences China

ISBN: (纸本)9783642330773

It is challenging to optimize GPU kernels because this progress requires deep technical knowledge of the underlying hardware. Modern GPU architectures are becoming more and more diversified, which further exacerbates the already difficult problem of performance optimization. This paper presents an insightful performance tuning chain for GPUs. The goal is to help non-expert programmers with limited knowledge of GPU architectures implement high performance GPU kernels directly. We achieve it by providing performance information to identify GPU program performance bottlenecks and decide which optimization methods should be adopted, so as to facilitate the best match between algorithm features and underlying hardware characteristics. To demonstrate the usage of tuning chain, we optimize three representative GPU kernels with different compute intensity: Matrix Transpose, Laplace Transform and Integral on both NVIDIA and AMD GPUs. Experimental results demonstrate that under the guidance of our tuning chain, performance of those kernels achieves 7.8~42.4 times speedup compared to their naïve implementations on both NVIDIA and AMD GPU platforms. © 2012 Springer-Verlag.

关键词： Chains

来源：评论

学校读者我要写书评

暂无评论

Improving Rating Estimation in Recommender Using Demographic Data and Expert Opinions

Improving Rating Estimation in Recommender Using Demographic...

引用

2011 2nd IEEE International Conference on Software Engineering and Service Science(2011年第二届 IEEE 软件工程与服务科学国际会议 ICSESS 2011)

作者： Long Yun Yan Yang Jing Wang Ge Zhu Dept. of Computer Science and Technology Heilongjiang University Key Lab. of Database and Parallel Computing Heilongjiang Province Harbin China

Memory-based collaborative filtering algorithms have been widely adopted in many popular recommender systems, however the rating data are very sparse, which affects prediction accuracy greatly. To solve this problem, we use expert opinions to improve prediction accuracy. Firstly, we propose a novel similarity measure in order to highlight users' background. Then, combining users' ratings with expert opinions, the prediction get a right balance in both expert professional opinions and similar users. Finally, since SVD-based collaborative filtering algorithms shows good performance on the prevention of noise, we use it to smooth the prediction. Experiments of MovieLens have shown that our proposed method improves recommendation quality obviously.

关键词： Recommendation Collaborative Filtering Data Sparsity Expert Demography

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：