2018 New York Scientific Data Summit (NYSDS)最新文献_第2页

Streaming Classical Multidimensional Scaling 流式经典多维缩放

2018 New York Scientific Data Summit (NYSDS) Pub Date : 2018-08-01 DOI: 10.1109/NYSDS.2018.8538942

Xi Zhang, Hao Huang, K. Mueller, Shinjae Yoo

引用次数: 2

Data Analysis on Multivariate Image Set 多元图像集的数据分析

2018 New York Scientific Data Summit (NYSDS) Pub Date : 2018-08-01 DOI: 10.1109/NYSDS.2018.8538941

Shruti K. Nair, S. Ha, Wei Xu

引用次数: 0

Assessing the Cryptographic Strength of RSA Moduli Using Algorithmic Entropy Reduction in Bivariate Polynomials 利用二元多项式的熵约算法评估RSA模的密码强度

2018 New York Scientific Data Summit (NYSDS) Pub Date : 2018-08-01 DOI: 10.1109/NYSDS.2018.8538957

Nicole Soder, C. Deluca, David Biersach, M. Dephillips

{"title":"Assessing the Cryptographic Strength of RSA Moduli Using Algorithmic Entropy Reduction in Bivariate Polynomials","authors":"Nicole Soder, C. Deluca, David Biersach, M. Dephillips","doi":"10.1109/NYSDS.2018.8538957","DOIUrl":"https://doi.org/10.1109/NYSDS.2018.8538957","url":null,"abstract":"We develop an innovative approach to factoring semiprimes, numbers that are the product of two large primes. These composite numbers form the foundation of the widely used RSA encryption method which protects vast amounts of digitally transmitted data. With the current rate of technological growth, experts believe that the best-known factoring algorithms would take thousands of years to break a single RSA composite. But with a more efficient method, government agencies could quickly decrypt big data sets to better confront national security concerns. Our technique, Algorithmic Entropy Reduction in Bivariate Polynomials, takes a different approach from traditional methods in that it addresses the binary expansion of the primes without relying on number theory. The method evaluates if a valid solution can exist for a given bivariate polynomial (BVP) representation of each prime at every bit position. In doing so, the method ascertains each binary digit from least significant bit on the right-hand side to most significant bit on the left-hand side (entropy reduction). We execute the algorithm by generating code in C++ and Python and reduce its computational work by a factor of two using multiprocessing.","PeriodicalId":366010,"journal":{"name":"2018 New York Scientific Data Summit (NYSDS)","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115534108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Forecasting Smart Grid Load on the Wire 预测线上智能电网负荷

2018 New York Scientific Data Summit (NYSDS) Pub Date : 2018-08-01 DOI: 10.1109/NYSDS.2018.8538948

Jin Xu, Shilpi Bhattacharyya, D. Katramatos, Shinjae Yoo, Meng Yue

引用次数: 1

Visualization and quantum computation of Moiré superconductivity in bilayer graphene, carbon nanocones and nanostrips 双层石墨烯、碳纳米锥和纳米带中moir<s:1>超导性的可视化和量子计算

2018 New York Scientific Data Summit (NYSDS) Pub Date : 2018-08-01 DOI: 10.1109/NYSDS.2018.8538949

S. Elghazoly, M. McGuigan

{"title":"Visualization and quantum computation of Moiré superconductivity in bilayer graphene, carbon nanocones and nanostrips","authors":"S. Elghazoly, M. McGuigan","doi":"10.1109/NYSDS.2018.8538949","DOIUrl":"https://doi.org/10.1109/NYSDS.2018.8538949","url":null,"abstract":"The purpose of this project is to visualize and study the electronic properties of carbon nanostructures. The principal structures studied are AA and AB (Bernal) stacked graphene bilayers, an 80C nanocone with pentagonal defects at its apex, and a 16C nanoring. Graphene and other based nanostructures have attracted much attention due to their unique electronic properties. For example, bilayer graphene shows superconductivity at certain magic angles of rotation. Open source molecular modeling and visualization programs like SAMSON, Avogadro, and VMD are utilized to generate the structures above. Using bond information in order to find nearest neighbors, an adjacency matrix can be generated from which the energy eigenvalues are solvable and are used to visualize the dispersion relation, free energy, persistent current, Green’s function, and entanglement entropy. The goal of this project is to study the effect of rotation on the electronic properties of bilayer graphene, particularly as it pertains to the magic angle where superconductivity occurs, as well as the electronic properties of the bilayer Moiré patterns which emerge due to the superposition of the identical lattices at different angles. An important goal is to incorporate phonon interactions into the tight-binding model for carbon structures, and to utilize quantum based computing as a tool to find the ground state properties of these structures, particularly in the terminating case where boundary conditions have an effect.","PeriodicalId":366010,"journal":{"name":"2018 New York Scientific Data Summit (NYSDS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126442612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Prescriptive provenance for streaming analysis of workflows at scale 大规模工作流流分析的规定性来源

2018 New York Scientific Data Summit (NYSDS) Pub Date : 2018-08-01 DOI: 10.1109/NYSDS.2018.8538951

Line C. Pouchard, K. Huck, Gyorgy Matyasfalvi, Dingwen Tao, L. Tang, H. V. Dam, Shinaje Yoo

引用次数: 6

Performance Evaluation for Tape Storage Data Recall with T10KD Drive T10KD驱动器磁带存储数据召回性能评价

2018 New York Scientific Data Summit (NYSDS) Pub Date : 2018-08-01 DOI: 10.1109/NYSDS.2018.8538947

Guangwei Che, D. Yu

{"title":"Performance Evaluation for Tape Storage Data Recall with T10KD Drive","authors":"Guangwei Che, D. Yu","doi":"10.1109/NYSDS.2018.8538947","DOIUrl":"https://doi.org/10.1109/NYSDS.2018.8538947","url":null,"abstract":"Tape library storage is the best solution for archiving big data due to high capacity, low cost and long retention. However, as the nature of sequential media, its main drawback versus disk storage is the slow performance for data recall in random access order. With emerging of the modern technology from the enterprise tape drives, Recommended Access Order (RAO) should improve the data recall performance considerably.The Scientific Data and Computing Center (SDCC) in Brookhaven National Lab (BNL) is operating a tape library storage system which contains more than 100 PB data. It is managed by High Performance Storage System (HPSS) software. Starting from HPSS 7.5.1 release, a new feature named Tape Ordered Recall (TOR) has offered a great advantage for large amount of data recall. It is designed to reduce the file access time from tape media. In our test with Oracle T10K enterprise drive, TOR’s tape recall rates for small files (1GB) is almost 4 times of the unordered recall. TOR supports both RAO and non-RAO drives. It will use the RAO feature if available. Otherwise, it makes a linear offset ordering to recall the data. Additionally, Quaid is a smart recall tool introduced in HPSS7.5.1 as well. It uses new batch stage API to stage files in the background, improves the tape data recall performance significantly. Efficient Retrieval and Access to Data Archived on Tape, aka ERADAT, is a tape storage data recall tool developed mainly in SDCC. It sorts the staging requests based on file’s logical position order before submitting jobs to HPSS. Over a decade of utilization and improvement, ERADAT has been proven as a great performer serving BNL’s worldwide scientific community. In this paper we will present the test results for each individual tool under certain test setting and configuration, analyze and evaluate their performance advantage and disadvantage, find the best solution for BNL’s tape storage production system for different data characteristics.","PeriodicalId":366010,"journal":{"name":"2018 New York Scientific Data Summit (NYSDS)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122648545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detailed Performance Analysis of Distributed Tensorflow on a GPU Cluster using Deep Learning Algorithms 使用深度学习算法的分布式Tensorflow在GPU集群上的详细性能分析

2018 New York Scientific Data Summit (NYSDS) Pub Date : 2018-08-01 DOI: 10.1109/NYSDS.2018.8538946

A. Malik, Micheal Lu, N. Wang, Yeiwei Lin, Shinjae Yoo

{"title":"Detailed Performance Analysis of Distributed Tensorflow on a GPU Cluster using Deep Learning Algorithms","authors":"A. Malik, Micheal Lu, N. Wang, Yeiwei Lin, Shinjae Yoo","doi":"10.1109/NYSDS.2018.8538946","DOIUrl":"https://doi.org/10.1109/NYSDS.2018.8538946","url":null,"abstract":"Long training times for building a high accuracy deep neural networks (DNNs) is impeding research for new DNN architectures. For example, time for training GoogleNet with the ImageNet dataset on a single Nvidia K20 GPU almost takes 25 days. Therefore, there is a great need in the AI community to speed up the training phase, especially when using a large dataset. For this, we need Distributed Deep Neural Networks (DDNNs) that can scale well with more computation resources. However, this involves two challenges.First, the deep learning framework or training library must support inter-node communication. Second, the user must modify the code to take advantage of the inter-node communication. The changes to the code can be minimal to significant depending upon the user expertize in the distributed systems. Current DNN frameworks support distributed learning using MPI. However, these frameworks come with poorly understood overheads associated with communication and data management. Tensorflow provides APIs for distributed learning using MPI programming model and gRPC. These APIs are not easy to use for a domain expert for designing an efficient distributed learning model. Recently, Uber Inc. provides the Horovod Framework which gives a fast and easy way to support distributed learning using Tensorflow, Pytorach, and Keras. In this paper we provide a detailed performance analysis of distributed Tensorflow using Horovod. We implemented distributed learning for AlexNet, GoogleNet, and ResNet50 using Horovod. We used Nvidia K 40,K80, and P100 GPUs for our experimentation. We used synthetic image data with different runtime variables (batch size and number of GPUs). Our results shows that the Horovod framework gives almost linear throughput (images/sec) scalability up to 256 GPUs.","PeriodicalId":366010,"journal":{"name":"2018 New York Scientific Data Summit (NYSDS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127665073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Application of Analysis on the Wire to Streaming NSLS-II Beamline Data 线上分析在NSLS-II波束线数据流中的应用

2018 New York Scientific Data Summit (NYSDS) Pub Date : 2018-08-01 DOI: 10.1109/NYSDS.2018.8538955

Renuka Diwan, Ayesha Rizvi, Shilpi Bhattacharyya, D. Katramatos, K. Yager

引用次数: 1

Visualizing effective potentials and using the IBM-Q to study quantum field theory models in 0+1 dimensions 可视化有效势并使用IBM-Q研究0+1维的量子场论模型

2018 New York Scientific Data Summit (NYSDS) Pub Date : 2018-08-01 DOI: 10.1109/NYSDS.2018.8538962

Christopher Kane, M. McGuigan

引用次数: 5