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How ECP Software Technologies and Math Libraries are Working Toward Performance Portability at Exascale [article]

Lois Curfman McInnes
2021 figshare.com  
runtimes through advanced libraries for scalable mathematics, visualization, and analytics, as well as tools for performance analysis and tuning.  ...  Achieving good performance on both current architectures and emerging extreme-scale machines is critical, as these software technologies underpin a wide range of scientific application codes, both within  ...  learning, graph analytics, mesh refinement, PDE discretization, particles, online data analytics ECP's holistic approach uses co-design and integration to achieve exascale computing Performant  ... 
doi:10.6084/m9.figshare.14156903.v1 fatcat:t34u2qaltng65dr6eb5trdbavm

How ECP Software Technologies and Math Libraries are Working Toward Performance Portability at Exascale [article]

Lois Curfman McInnes
2021 figshare.com  
runtimes through advanced libraries for scalable mathematics, visualization, and analytics, as well as tools for performance analysis and tuning.  ...  Achieving good performance on both current architectures and emerging extreme-scale machines is critical, as these software technologies underpin a wide range of scientific application codes, both within  ...  learning, graph analytics, mesh refinement, PDE discretization, particles, online data analytics ECP's holistic approach uses co-design and integration to achieve exascale computing Performant  ... 
doi:10.6084/m9.figshare.14156903.v2 fatcat:6l4gtrlvnjdoxesj5wedcephlu

Big Data Analytics in Bioinformatics: A Machine Learning Perspective [article]

Hirak Kashyap, Hasin Afzal Ahmed, Nazrul Hoque, Swarup Roy, Dhruba Kumar Bhattacharyya
2015 arXiv   pre-print
Bioinformatics research is characterized by voluminous and incremental datasets and complex data analytics methods. The machine learning methods used in bioinformatics are iterative and parallel.  ...  However, there lack standard big data architectures and tools for many important bioinformatics problems, such as fast construction of co-expression and regulatory networks and salient module identification  ...  ACKNOWLEDGMENTS The authors would like to thank the Ministry of HRD, Govt. of India for funding as a Centre of Excellence with thrust area in Machine Learning Research and Big Data Analytics for the period  ... 
arXiv:1506.05101v1 fatcat:oix7d5hecbfgthzhepznwyi6fm

Front Matter: Volume 10148

Luigi Capodieci, Jason P. Cain
2017 Design-Process-Technology Co-optimization for Manufacturability XI  
applications from design to manufacturing 1014 1G Enhancing manufacturability of standard cells by using DTCO methodology vi  ...  Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  for IC physical designs using graph search, data analytics, and machine learning (Invited Paper) [10148-7] SESSION 3 DESIGN INTERACTIONS WITH LITHOGRAPHY: JOINT SESSION WITH CONFERENCES 10147 AND  ... 
doi:10.1117/12.2277800 fatcat:g7yjgbcblvh5nmidzvoe6avmjm

Software defined architectures for data analytics

Vito Giovanni Castellana, Marco Minutoli, Antonino Tumeo, Marco Lattuada, Pietro Fezzardi, Fabrizio Ferrandi
2019 Proceedings of the 24th Asia and South Pacific Design Automation Conference on - ASPDAC '19  
Data analytics applications increasingly are complex workflows composed of phases with very different program behaviors (e.g., graph algorithms and machine learning, algorithms operating on sparse and  ...  Field Programmable Gate Arrays are more and more used for accelerating various workloads and, in particular, inferencing in machine learning, providing higher efficiency than other solutions.  ...  INTRODUCTION Data analytics applications increasingly employ complex workflows that couple graph and machine learning methods.  ... 
doi:10.1145/3287624.3288754 dblp:conf/aspdac/CastellanaMTLFF19 fatcat:ip4n6z5ghzdubmzs7g6vsq3jmu

Big data analytics on Apache Spark

Salman Salloum, Ruslan Dautov, Xiaojun Chen, Patrick Xiaogang Peng, Joshua Zhexue Huang
2016 International Journal of Data Science and Analytics  
Apache Spark has emerged as the de facto framework for big data analytics with its advanced in-memory programming model and upper-level libraries for scalable machine learning, graph analysis, streaming  ...  More specifically, it shows what Apache Spark has for designing and implementing big data algorithms and pipelines for machine learning, graph analysis and stream processing.  ...  Research highlights Advanced analytics, such as machine learning, is essential for getting valuable insights from large-scale datasets.  ... 
doi:10.1007/s41060-016-0027-9 dblp:journals/ijdsa/SalloumD0PH16 fatcat:gtzw3aqupnhxvcjbefovrnfhne

AIPerf: Automated machine learning as an AI-HPC benchmark [article]

Zhixiang Ren, Yongheng Liu, Tianhui Shi, Lei Xie, Yue Zhou, Jidong Zhai, Youhui Zhang, Yunquan Zhang, Wenguang Chen
2021 arXiv   pre-print
scales of machines.  ...  Consequently, the need for cross-stack performance benchmarking of AI-HPC systems emerges rapidly.  ...  Though we can not use the existing HPC benchmarks for AI-HPC, they still inspire us in the benchmark design.  ... 
arXiv:2008.07141v7 fatcat:ighqmwdqtvg7blw5xtsxzto3pq

Analyzing Analytics

Rajesh Bordawekar, Bob Blainey, Ruchir Puri
2015 Synthesis Lectures on Computer Architecture  
These techniques, are known collectively as analytics, and draw upon multiple disciplines, including statistics, quantitative analysis, data mining, and machine learning.  ...  In this survey paper, we identify some of the key techniques employed in analytics both to serve as an introduction for the non-specialist and to explore the opportunity for greater optimizations for parallelization  ...  A key lesson learned from the design of Netezza has been the huge value of specializing system design for analytics.  ... 
doi:10.2200/s00678ed1v01y201511cac035 fatcat:jkjywe5rzzaupjwq5rjyavqxi4

Visual Analytics for Explainable Deep Learning [article]

Jaegul Choo, Shixia Liu
2018 arXiv   pre-print
In this paper, we review visual analytics, information visualization, and machine learning perspectives relevant to this aim, and discuss potential challenges and future research directions.  ...  However, even with such unprecedented advancements, the lack of explanation regarding the decisions made by deep learning models and absence of control over their internal processes act as major drawbacks  ...  Visual analytics for advanced deep learning architectures So far, researchers have mostly developed visual analytic approaches for basic deep learning architectures, such as CNNs and RNNs.  ... 
arXiv:1804.02527v1 fatcat:efwpg3ot5nfgfnbhnlt6rfkm44

Comprehensive Analysis of IoT Malware Evasion Techniques

A. Al-Marghilani
2021 Engineering, Technology & Applied Science Research  
Malware detection is critical for a system's security. Many security researchers have studied the IoT malware detection domain.  ...  This paper presents a survey of IoT malware evasion techniques, reviewing and discussing various researches.  ...  A graph-based lightweight detection method is yet to be designed and developed. [32] Advanced evasive techniques.  ... 
doi:10.48084/etasr.4296 fatcat:hyfkdspwizce3cyeu6erygpqai

Big Data Analytics: A Perspective View

Suman Pandey
2017 International Journal of Advanced Research in Computer Science and Software Engineering  
This work will be quite useful for the future researchers in this domain and facilitate the development of optimal techniques to address Big data.  ...  The process of diving into large amounts of data to discover patterns and disguised correlations is named as big data analytics.  ...  It provides many advance algorithms for machine learning.  ... 
doi:10.23956/ijarcsse/sv7i5/0237 fatcat:mq75vo3n4rbihnc43mtpktzrru

Visual Steering for One-Shot Deep Neural Network Synthesis [article]

Anjul Tyagi, Cong Xie, Klaus Mueller
2020 arXiv   pre-print
Recent advancements in the area of deep learning have shown the effectiveness of very large neural networks in several applications.  ...  In this approach, a super-graph of all candidate architectures is trained in one-shot and the optimal neural network is identified as a sub-graph.  ...  Explainable Machine Learning Explainable machine learning plays an important role when it comes to diagnosing the performance of a neural network, which in turn helps in designing the neural network architecture  ... 
arXiv:2009.13008v1 fatcat:sg6xvganvrcjlfypo2vzx4qsya

2020 Index IEEE Transactions on Computers Vol. 69

2020 IEEE transactions on computers  
Awad, A., +, TC Nov. 2020 1556-1557 Guest Editors' Introduction to the Special Issue on Machine Learning Archi-TTADF: Power Efficient Dataflow-Based Multicore Co-Design Flow.  ...  Salamat, S., +, TC Aug. 2020 1159-1171 Accurate Cost Estimation of Memory Systems Utilizing Machine Learning and Solutions from Computer Vision for Design Automation.  ... 
doi:10.1109/tc.2020.3042405 fatcat:htwgwc6gtbcfdkcpj6dcfbuwhq

6G: Connectivity in the Era of Distributed Intelligence [article]

Shilpa Talwar, Nageen Himayat, Hosein Nikopour, Feng Xue, Geng Wu, Vida Ilderem
2021 arXiv   pre-print
Future 6G networks will need to deliver quality of experience through seamless integration of communication, computation and AI.  ...  The confluence of 5G and AI is transforming wireless networks to deliver diverse services at the Edge, driving towards a vision of pervasive distributed intelligence.  ...  Acknowledgements: Many fruitful discussions with NSF, academia, and US partners have helped shape our view on 6G. We also acknowledge Intel-NSF collaborations in funding US research [4, 10, 15] .  ... 
arXiv:2110.07052v2 fatcat:j53bpyrmdbe7nfkhkrnxwhxjxm

Chapter 3 Big Data Outlook, Tools, and Architectures [chapter]

Hajira Jabeen
2020 Lecture Notes in Computer Science  
knowledge graphs.  ...  At the end, the chapter reviews knowledge graphs that address the challenges (e.g. heterogeneity, interoperability, variety) of big data through their specialised representation.  ...  Machine Learning: FlinkML is a machine learning library aimed to provide a list of machine learning algorithms.  ... 
doi:10.1007/978-3-030-53199-7_3 fatcat:vy7ac2ccszcenmxl7kxdiapfvq
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