From cgf at isep.ipp.pt Tue Mar 31 15:59:25 2020 From: cgf at isep.ipp.pt (Carlos Ferreira) Date: Tue, 31 Mar 2020 16:59:25 +0100 Subject: [openstack-hpc] CFP: SBAC - PAD 2020 - IEEE International Symposium on Computer Architecture and High Performance Computing Message-ID: SBAC - PAD 2020 - IEEE International Symposium on Computer Architecture and High Performance Computing Department of Computer Science, School of Sciences, University of Porto Porto, Portugal Web page: https://sbac2020.dcc.fc.up.pt E-mail: sbac2020 at dcc.fc.up.pt Call for Papers ------------------------------------------------------------------------------------------------------- SBAC-PAD is an international symposium, started in 1987, which has continuously presented an overview of new developments, applications, and trends in parallel and distributed computing technologies. SBAC-PAD is open for faculty members, researchers, specialists and graduate students around the world. In this edition, the symposium will be held at the University of Porto, Porto, Portugal. The city of Porto is famous for its Port wine and beautiful scenery, architecture and cultural events. Authors are invited to submit manuscripts on a wide range of high-performance and distributed computing areas. Topics of interest include (but are not limited to): * Application-specific systems * Architecture and programming support for emerging domains (Big Data, Deep Learning, Machine learning, Cognitive Systems) * Benchmarking, performance measurements, and analysis * Cloud, cluster, and edge/fog computing systems * Embedded and pervasive systems * GPUs, FPGAs and accelerator architectures * Languages, compilers, and tools for parallel and distributed programming * Modeling and simulation methodology * Operating systems and virtualization * Parallel and distributed systems, algorithms, and applications * Power and energy-efficient systems * Processor, cache, memory, storage, and network architecture * Real-world applications and case studies * Reconfigurable, resilient and fault-tolerant systems Paper Submission ------------------------------------------------------------------------------------------------------- Submissions must be in English, 8 pages maximum, following the IEEE conference formatting guidelines. To be published in the conference proceedings and to be eligible for publication at the IEEE Xplore, at least one of the authors must register at the full rate and present her/his work. Authors may not use a single registration for multiple papers. Authors of accepted papers will be invited to submit extended versions of their papers for publication on the Journal of Parallel and Distributed Computing. The call will be open and will follow the normal journal submission procedure. Important Dates ------------------------------------------------------------------------------------------------------- Abstract deadline: May 15th, 2020 Paper deadline: May 22nd, 2020 Reviewing period: May 24-Jun 21, 2020 Author notification: June 26th, 2020 Camera-ready submission: July 3rd, 2020 Organizing Committee ------------------------------------------------------------------------------------------------------- General Chairs * InêsDutra.ines at dcc.fc.up.pt (University of Porto, Portugal) * Jorge Barbosa,jbarbosa at fe.up.pt (University of Porto, Portugal) * Miguel Areias,miguel-areias at dcc.fc.up.pt (University of Porto, Portugal) Program Co-chairs * Jorge Barbosa (University of Porto, Portugal) * Laurent Lefèvre (Inria, ENS Lyon, University of Lyon, France) * Lucia Drummond (Universidade Federal Fluminense, Brazil) Track Chairs * Computer Architecture o Chair: José Moreira (IBM Thomas J. Watson Research Center, USA) * Networking and Distributed Systems o Chair: Jesús Carretero (University Carlos III of Madrid, Spain) * Parallel Applications and Algorithms o Chair: Alba Melo (Universidade de Brasília, Brazil) * Performance Evaluation o Chair: Ariel Oleksiak (Poznań Supercomputing and Networking Cente, Poland) * System Software o Chair: Jidong Zhai (Tsinghua University, China) Publicity Chairs * Carlos Ferreira, Instituto Superior de Engenharia, Portugal * Miguel Areias, University of Porto, Portugal * Feng Zhang, Renmin University of China Workshop Chairs * Miguel Areias, University of Porto, Portugal * Iván Carrera, Escuela Politécnica Nacional, Quito, Equador and University of Porto, Portugal Publications Chairs * IEEE - Proceedings o Rui Camacho, University of Porto, Portugal o Iván Carrera, Escuela Politécnica Nacional, Quito, Equador and University of Porto, Portugal * JPDC - Special Issue o Jorge Barbosa, University of Porto, Portugal Carlos Ferreira ISEP | Instituto Superior de Engenharia do Porto Rua Dr. António Bernardino de Almeida, 431 4249-015 Porto - PORTUGAL tel. +351 228 340 500 | fax +351 228 321 159 mail at isep.ipp.pt | www.isep.ipp.pt From cgf at isep.ipp.pt Tue Mar 31 16:46:05 2020 From: cgf at isep.ipp.pt (Carlos Ferreira) Date: Tue, 31 Mar 2020 17:46:05 +0100 Subject: [openstack-hpc] CFP: Big Data & Deep Learning in HPC (IEEE Xplore) @Porto, Portugal Message-ID: <7c0be9ed-224e-f1b7-6bb3-4534f4abd0ba@isep.ipp.pt> Workshop on BIG DATA & DEEP LEARNING in HIGH PERFORMANCE COMPUTING (https://sbac2020.dcc.fc.up.pt/bdl2020/) in conjunction with the IEEE 32nd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD 2020) (https://sbac2020.dcc.fc.up.pt/) September 9, 2020, Porto, Portugal The city of Porto is famous for its Port wine and beautiful scenery, architecture and cultural events. Portugal has again been awarded the best European Tourist Destination by the World Travel Awards, the Oscars equivalent in the field of tourism. ------------------------------------ WORKSHOP ON BIG DATA & DEEP LEARNING IN HIGH PERFORMANCE COMPUTING ------------------------------------ The number of very large data repositories (big data) is increasing in a rapid pace. Analysis of such repositories using the "traditional" sequential implementations of ML and emerging techniques, like deep learning, that model high-level abstractions in data by using multiple processing layers, requires expensive computational resources and long running times. Parallel or distributed computing are possible approaches that can make analysis of very large repositories and exploration of high-level representations feasible. Taking advantage of a parallel or a distributed execution of a ML/statistical system may: i) increase its speed; ii) learn hidden representations; iii) search a larger space and reach a better solution or; iv) increase the range of applications where it can be used (because it can process more data, for example). Parallel and distributed computing is therefore of high importance to extract knowledge from massive amounts of data and learn hidden representations. The workshop will be concerned with the exchange of experience among academics, researchers and the industry whose work in big data and deep learning require high performance computing to achieve goals. Participants will present recently developed algorithms/systems, on going work and applications taking advantage of such parallel or distributed environments. ------------------------------------ LIST OF TOPICS ------------------------------------ All novel data-intensive computing techniques, data storage and integration schemes, and algorithms for cutting-edge high performance computing architectures which targets Big Data and Deep Learning are of interest to the workshop. Examples of topics include but not limited to: - parallel algorithms for data-intensive applications; - scalable data and text mining and information retrieval; - using Hadoop, MapReduce, Spark, Storm, Streaming to analyze Big Data; - energy-efficient data-intensive computing; - deep-learning with massive-scale datasets; - querying and visualization of large network datasets; - processing large-scale datasets on clusters of multicore and manycore processors, and accelerators; - heterogeneous computing for Big Data architectures; - Big Data in the Cloud; - processing and analyzing high-resolution images using high-performance computing; - using hybrid infrastructures for Big Data analysis. - New algorithms for parallel/distributed execution of ML systems; - applications of big data and deep learning to real-life problems. ------------------------------------ KEY DATES ------------------------------------ Deadline for paper submission: May 25, 2020 Author notification: July 1, 2020 Camera-ready version of papers: July 25, 2020 ------------------------------------ SUBMISSION ------------------------------------ We invite authors to submit original work to BDL. All papers will be peer reviewed and accepted papers will be published in IEEE Xplore. Submissions must be in English, limited to 8 pages in the IEEE conference format (see https://www.ieee.org/conferences/publishing/templates.html) All submissions should be made electronically through the EasyChair system: https://easychair.org/conferences/?conf=bdl2020 ------------------------------------ REGISTRATION ------------------------------------ A full registration to the workshop and presentation are needed in order to have your paper included in the workshop proceedings. The Workshop fee is 300 euros. Registration system available in https://sbac2020.dcc.fc.up.pt/bdl2020/registration.html ------------------------------------ VENUE ------------------------------------ Department of Computer Science, Faculty of Sciences, University of Porto Rua do Campo Alegre 1021/1055 4169-007 Porto, Portugal The city of Porto is famous for its Port wine and beautiful scenery, architecture and cultural events. Portugal has again been awarded the best European Tourist Destination by the World Travel Awards, the Oscars equivalent in the field of tourism. ------------------------------------ ORGANIZATION ------------------------------------ Carlos Ferreira (LIAAD - INESC TEC LA and Polytechnic Institute of Porto) João Gama (LIAAD - INESC TEC LA and University of Porto) Albert Bifet (Telecom ParisTech) Miguel Areias (CRACS - INESC TEC LA and University of Porto) Rui Camacho (LIAAD -INESC TEC LA and University of Porto) Carlos Ferreira ISEP | Instituto Superior de Engenharia do Porto Rua Dr. António Bernardino de Almeida, 431 4249-015 Porto - PORTUGAL tel. +351 228 340 500 | fax +351 228 321 159 mail at isep.ipp.pt | www.isep.ipp.pt