March 2016 - OpenStack-HPC - lists.openstack.org

Re: [openstack-hpc] What's the state of openstack-hpc now?
by Blair Bethwaite 06 Oct '16

06 Oct '16

Hi, Apologies for top-posting but I don't intend to answer all the historical project points you've raised. Regarding old things floating around on github, your mileage may vary, but I doubt at this point you want to be looking at any of that in great detail. You haven't really explained what you mean by or want from HPC in this context, so I'm guessing a little based on your other questions... OpenStack is many things to different people and organisations, but at the software core is a very flexible infrastructure provisioning framework. HPC requires infrastructure (compute, network, storage), and OpenStack can certainly deliver it - make your deployment choices to suit your use-cases. A major choice would be whether you will use full system virtualisation or bare-metal or containers or <insert next trend> - that choice largely depends on your typical workloads and what style of cluster you want. Beyond that, compared to "typical" cloud hardware - faster CPUs, faster memory, faster network (probably with much greater east-west capacity), integration of a suitable parallel file-system. However, OpenStack is not a HPC management / scheduling / queuing / middleware system - there are lots of those already and you should pick one that fits your requirements and then (if it helps) run it atop an OpenStack cloud (it might help, e.g., if you want to run multiple logical clusters on the same physical infrastructure, if you want to mix other more traditional cloud workloads in, if you're just doing everything with OpenStack like the other cool kids). There are lots of nuances here, e.g., where one scheduler might lend itself better to more dynamic infrastructure (adding/removing instances), another might be lighter-weight for use with a Cluster-as-a-Service deployment model, whilst another suits a multi-user managed service style cluster. I'm sure there is good experience and opinion hidden on this list if you want to interrogate those sorts of choices more specifically. Most of the relevant choices you need to make with respect to running HPC workloads on infrastructure that is provisioned through OpenStack will come down to your hypervisor choices. My preference for now is to stick with the OpenStack community's most popular free OS and hypervisor (Ubuntu and KVM+Libvirt) - when I facilitated the hypervisor-tuning ops session at the Vancouver summit (with a bunch of folks interested in HPC on OpenStack) there was no-one in the room running a different hypervisor, though several were using RHEL. With the right tuning KVM can get you to within a hair's breadth of bare-metal performance for a wide range of CPU, memory and inter-process comms benchmarks, plus you can easily make use of PCI passthrough for latency sensitive or "difficult" devices like NICs/HCAs and GPGPUs. And the "right tuning" is not really some arcane knowledge, it's mainly about exposing host CPU capabilities, pinning vCPUs to pCPUs, and tuning or pinning and exposing NUMA topology - most of this is supported directly through OpenStack-native features now. To answer the GPU question more explicitly - yes you can do this. Mainly you need to ensure you're getting compatible hardware (GPU and relevant motherboard components) - most of the typical GPGPU choices (e.g. K80, K40, M60) will work, and you should probably be wary of PCIe switches unless you know exactly what you're doing (recommend trying before buying). At the OpenStack level you just define the PCI devices you want OpenStack Nova to provision and you can then define custom instance-types/flavors that will get a GPU passed through. Similar things go for networking. Lastly, just because you can do this doesn't make it a good idea... OpenStack is complex, HPC systems are complex, layering one complicated thing on another is a good way to create tricky problems that hide in the interface between the two layers. So make sure you're gaining something from having OpenStack in the mix here. HTH, Blair On 15 March 2016 at 23:00, <openstack-hpc-request(a)lists.openstack.org> wrote: > Message: 1 > Date: Tue, 15 Mar 2016 19:05:38 +0800 > From: "me,apporc" <appleorchard2000(a)gmail.com> > To: openstack-hpc(a)lists.openstack.org > Subject: [openstack-hpc] What's the state of openstack-hpc now? > Message-ID: > <CAOBTi0sftGTG-fscM-C5wLu6bTgZMaLaM2eXBJpa0a=vkPDusg(a)mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > Hi, all > > I found this etherpad[1] which was created long time ago, inside which > there are some blueprints: support-heterogeneous-archs[2], > heterogeneous-instance-types[3] > and schedule-instances-on-heterogeneous-architectures[4] . > But those blueprints had been obselete since year 2014, and some of its > patches were abandoned. > There however is a forked branch github[5] or launchpad[6], which is > diverged far away from nova/trunk, and not updated since 2014 too. > > Is that we just abandoned those blueprints in openstack or else? > > Besides, there is a CaaS[7] project called Senlin[8], which refered to the > word "HPC" in its wiki. But it seems like not really related. "Cluster" can > mean many things, but hpc is some kind different. > > I can not get the status of GPU support in nova. As the case of network, > SR-IOV[9] seems ok. For storage, i don't know what the word "mi2" means in > etherpad[1]. > > According to what i got above, it seems we can not use hpc in openstack > now. But there are some videos here[10], here[11] and here[12].Since we can > not get GPU in nova instance, are they just building traditional hpcs > without GPU? > > > I need more information, thanks in advance. > > 1. https://etherpad.openstack.org/p/HVHsTqOQGc > 2. https://blueprints.launchpad.net/nova/+spec/support-heterogeneous-archs > 3. https://blueprints.launchpad.net/nova/+spec/heterogeneous-instance-types > 4. > https://blueprints.launchpad.net/nova/+spec/schedule-instances-on-heterogen… > 5. https://github.com/usc-isi/nova > 6. https://code.launchpad.net/~usc-isi/nova/hpc-trunk > 7. https://wiki.openstack.org/wiki/CaaS > 8. https://wiki.openstack.org/wiki/Senlin > 9. https://wiki.openstack.org/wiki/SR-IOV-Passthrough-For-Networking > 10. > https://www.openstack.org/summit/vancouver-2015/summit-videos/presentation/… > 11. > https://www.openstack.org/summit/tokyo-2015/videos/presentation/hpc-on-open… > 12. > https://www.openstack.org/summit/tokyo-2015/videos/presentation/canonical-h… > -- > Regards, > apporc >

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Extended Deadline (April 12) CFP: Big Data & Deep Learning in HPC (Springer LNCS)
by Carlos Ferreira 29 Mar '16

29 Mar '16

------------------------------------ First Workshop on Big Data & Deep Learning in High Performance Computing (http://bigdatadeeplearning2016.inesctec.pt/) in conjunction with VECPAR 2016 - High Performance Computing for Computational Science (http://vecpar.fe.up.pt/2016/) Porto, Portugal June 30, 2016 Porto has been crowned the BEST EUROPEAN DESTINATION of 2014 for its postcard-perfect views, friendly locals and gastronomy. Paper New SUBMISSION DEADLINE: *** April 12, 2016 **** ------------------------------------ ------------------------------------ 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. ------------------------------------ SUBMISSION ------------------------------------ Papers submitted to this workshop should not exceed 12 pages in length. The papers should be formatted according to the rules of the Springer Series Lecture Notes in Computer Science (LNCS). All accepted papers will be published by Springer in the series entitled Lecture Notes in Computer Science. The proceedings will be distributed in electronic format to participants of VECPAR 2016. You should make your submissions through the EasyChair system: https://easychair.org/conferences/?conf=bdl2016 ------------------------------------ REGISTRATION ------------------------------------ All participants must attend the VECPAR 2016 conference. http://vecpar.fe.up.pt/2016/registration.html ------------------------------------ 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) Vítor Santos Costa (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(a)isep.ipp.pt | www.isep.ipp.pt

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Final CFP: Big Data & Deep Learning in HPC (Springer LNCS)
by Carlos Ferreira 25 Mar '16

25 Mar '16

------------------------------------------------------------------------------------- Porto, Portugal June 30, 2016 Workshop on Big Data & Deep Learning in High Performance Computing (http://bigdatadeeplearning2016.inesctec.pt/) in conjunction with VECPAR 2016 - High Performance Computing for Computational Science (http://vecpar.fe.up.pt/2016/) Paper Submission Deadline: 27 March 2016 --------------------------------------------------------------------------------------- ------------------------------------ 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. ------------------------------------ SUBMISSION ------------------------------------ Papers submitted to this workshop should not exceed 12 pages in length. The papers should be formatted according to the rules of the Springer Series Lecture Notes in Computer Science (LNCS). All accepted papers will be published by Springer in the series entitled Lecture Notes in Computer Science. The proceedings will be distributed in electronic format to participants of VECPAR 2016. You should make your submissions through the EasyChair system: https://easychair.org/conferences/?conf=bdl2016 ------------------------------------ REGISTRATION ------------------------------------ All participants must attend the VECPAR 2016 conference. http://vecpar.fe.up.pt/2016/registration.html ------------------------------------ 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) Vítor Santos Costa (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(a)isep.ipp.pt | www.isep.ipp.pt

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CfP 11th Workshop on Virtualization in High-Performance Cloud Computing (VHPC '16)
by VHPC 16 19 Mar '16

19 Mar '16

==================================================================== CALL FOR PAPERS 11th Workshop on Virtualization in High-Performance Cloud Computing (VHPC '16) held in conjunction with the International Supercomputing Conference - High Performance, June 19-23, 2016, Frankfurt, Germany. ==================================================================== Date: June 23, 2016 Workshop URL: http://vhpc.org Paper Submission Deadline: April 25, 2016 Call for Papers Virtualization technologies constitute a key enabling factor for flexible resource management in modern data centers, and particularly in cloud environments. Cloud providers need to manage complex infrastructures in a seamless fashion to support the highly dynamic and heterogeneous workloads and hosted applications customers deploy. Similarly, HPC environments have been increasingly adopting techniques that enable flexible management of vast computing and networking resources, close to marginal provisioning cost, which is unprecedented in the history of scientific and commercial computing. Various virtualization technologies contribute to the overall picture in different ways: machine virtualization, with its capability to enable consolidation of multiple underutilized servers with heterogeneous software and operating systems (OSes), and its capability to live-migrate a fully operating virtual machine (VM) with a very short downtime, enables novel and dynamic ways to manage physical servers; OS-level virtualization (i.e., containerization), with its capability to isolate multiple user-space environments and to allow for their coexistence within the same OS kernel, promises to provide many of the advantages of machine virtualization with high levels of responsiveness and performance; I/O Virtualization allows physical NICs/HBAs to take traffic from multiple VMs or containers; network virtualization, with its capability to create logical network overlays that are independent of the underlying physical topology and IP addressing, provides the fundamental ground on top of which evolved network services can be realized with an unprecedented level of dynamicity and flexibility; the increasingly adopted paradigm of Software-Defined Networking (SDN) promises to extend this flexibility to the control and data planes of network paths. Topics of Interest The VHPC program committee solicits original, high-quality submissions related to virtualization across the entire software stack with a special focus on the intersection of HPC and the cloud. Topics include, but are not limited to: - Virtualization in supercomputing environments, HPC clusters, cloud HPC and grids - OS-level virtualization including container runtimes (Docker, rkt et al.) - Lightweight compute node operating systems/VMMs - Optimizations of virtual machine monitor platforms, hypervisors - QoS and SLA in hypervisors and network virtualization - Cloud based network and system management for SDN and NFV - Management, deployment and monitoring of virtualized environments - Virtual per job / on-demand clusters and cloud bursting - Performance measurement, modelling and monitoring of virtualized/cloud workloads - Programming models for virtualized environments - Virtualization in data intensive computing and Big Data processing - Cloud reliability, fault-tolerance, high-availability and security - Heterogeneous virtualized environments, virtualized accelerators, GPUs and co-processors - Optimized communication libraries/protocols in the cloud and for HPC in the cloud - Topology management and optimization for distributed virtualized applications - Adaptation of emerging HPC technologies (high performance networks, RDMA, etc..) - I/O and storage virtualization, virtualization aware file systems - Job scheduling/control/policy in virtualized environments - Checkpointing and migration of VM-based large compute jobs - Cloud frameworks and APIs - Energy-efficient / power-aware virtualization The Workshop on Virtualization in High-Performance Cloud Computing (VHPC) aims to bring together researchers and industrial practitioners facing the challenges posed by virtualization in order to foster discussion, collaboration, mutual exchange of knowledge and experience, enabling research to ultimately provide novel solutions for virtualized computing systems of tomorrow. The workshop will be one day in length, composed of 20 min paper presentations, each followed by 10 min discussion sections, plus lightning talks that are limited to 5 minutes. Presentations may be accompanied by interactive demonstrations. Important Dates April 25, 2016 - Paper submission deadline May 30, 2016 Acceptance notification June 23, 2016 - Workshop Day July 25, 2016 - Camera-ready version due Chair Michael Alexander (chair), TU Wien, Austria Anastassios Nanos (co-chair), NTUA, Greece Balazs Gerofi (co-chair), RIKEN Advanced Institute for Computational Science, Japan Program committee Stergios Anastasiadis, University of Ioannina, Greece Costas Bekas, IBM Research, Switzerland Jakob Blomer, CERN Ron Brightwell, Sandia National Laboratories, USA Roberto Canonico, University of Napoli Federico II, Italy Julian Chesterfield, OnApp, UK Stephen Crago, USC ISI, USA Christoffer Dall, Columbia University, USA Patrick Dreher, MIT, USA Robert Futrick, Cycle Computing, USA Robert Gardner, University of Chicago, USA William Gardner, University of Guelph, Canada Wolfgang Gentzsch, UberCloud, USA Kyle Hale, Northwestern University, USA Marcus Hardt, Karlsruhe Institute of Technology, Germany Krishna Kant, Templte University, USA Romeo Kinzler, IBM, Switzerland Brian Kocoloski, University of Pittsburgh, USA Kornilios Kourtis, IBM Research, Switzerland Nectarios Koziris, National Technical University of Athens, Greece John Lange, University of Pittsburgh, USA Nikos Parlavantzas, IRISA, France Kevin Pendretti, Sandia National Laboratories, USA Che-Rung Roger Lee, National Tsing Hua University, Taiwan Giuseppe Lettieri, University of Pisa, Italy Qing Liu, Oak Ridge National Laboratory, USA Paul Mundt, Adaptant, Germany Amer Qouneh, University of Florida, USA Carlos Reaño, Technical University of Valencia, Spain Seetharami Seelam, IBM Research, USA Josh Simons, VMWare, USA Borja Sotomayor, University of Chicago, USA Dieter Suess, TU Wien, Austria Craig Stewart, Indiana University, USA Anata Tiwari, San Diego Supercomputer Center, USA Kurt Tutschku, Blekinge Institute of Technology, Sweden Amit Vadudevan, Carnegie Mellon University, USA Yasuhiro Watashiba, Osaka University, Japan Nicholas Wright, Lawrence Berkeley National Laboratory, USA Chao-Tung Yang, Tunghai University, Taiwan Gianluigi Zanetti, CRS4, Italy Paper Submission-Publication Papers submitted to the workshop will be reviewed by at least two members of the program committee and external reviewers. Submissions should include abstract, key words, the e-mail address of the corresponding author, and must not exceed 10 pages, including tables and figures at a main font size no smaller than 11 point. Submission of a paper should be regarded as a commitment that, should the paper be accepted, at least one of the authors will register and attend the conference to present the work. The format must be according to the Springer LNCS Style. Initial submissions are in PDF; authors of accepted papers will be requested to provide source files. Format Guidelines: ftp://ftp.springer.de/pub/tex/latex/llncs/latex2e/llncs2e.zip Abstract, Paper Submission Link: https://edas.info/newPaper.php?c=21801 Lightning Talks Lightning Talks are non-paper track, synoptical in nature and are strictly limited to 5 minutes. They can be used to gain early feedback on ongoing research, for demonstrations, to present research results, early research ideas, perspectives and positions of interest to the community. Submit abstract via the main submission link. General Information The workshop is one day in length and will be held in conjunction with the International Supercomputing Conference - High Performance (ISC) 2016, June 19-23, Frankfurt, Germany.

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What's the state of openstack-hpc now?
by me,apporc 16 Mar '16

16 Mar '16

Hi, all I found this etherpad[1] which was created long time ago, inside which there are some blueprints: support-heterogeneous-archs[2], heterogeneous-instance-types[3] and schedule-instances-on-heterogeneous-architectures[4] . But those blueprints had been obselete since year 2014, and some of its patches were abandoned. There however is a forked branch github[5] or launchpad[6], which is diverged far away from nova/trunk, and not updated since 2014 too. Is that we just abandoned those blueprints in openstack or else? Besides, there is a CaaS[7] project called Senlin[8], which refered to the word "HPC" in its wiki. But it seems like not really related. "Cluster" can mean many things, but hpc is some kind different. I can not get the status of GPU support in nova. As the case of network, SR-IOV[9] seems ok. For storage, i don't know what the word "mi2" means in etherpad[1]. According to what i got above, it seems we can not use hpc in openstack now. But there are some videos here[10], here[11] and here[12].Since we can not get GPU in nova instance, are they just building traditional hpcs without GPU? I need more information, thanks in advance. 1. https://etherpad.openstack.org/p/HVHsTqOQGc 2. https://blueprints.launchpad.net/nova/+spec/support-heterogeneous-archs 3. https://blueprints.launchpad.net/nova/+spec/heterogeneous-instance-types 4. https://blueprints.launchpad.net/nova/+spec/schedule-instances-on-heterogen… 5. https://github.com/usc-isi/nova 6. https://code.launchpad.net/~usc-isi/nova/hpc-trunk 7. https://wiki.openstack.org/wiki/CaaS 8. https://wiki.openstack.org/wiki/Senlin 9. https://wiki.openstack.org/wiki/SR-IOV-Passthrough-For-Networking 10. https://www.openstack.org/summit/vancouver-2015/summit-videos/presentation/… 11. https://www.openstack.org/summit/tokyo-2015/videos/presentation/hpc-on-open… 12. https://www.openstack.org/summit/tokyo-2015/videos/presentation/canonical-h… -- Regards, apporc

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CFP [Springer LNCS]: Workshop on Big Data & Deep Learning in HPC
by Carlos Ferreira 02 Mar '16

02 Mar '16

------------------------------------------------------------------------------------- Porto, Portugal June 30, 2016 First Workshop on Big Data & Deep Learning in High Performance Computing (http://bigdatadeeplearning2016.inesctec.pt/) in conjunction with VECPAR 2016 - High Performance Computing for Computational Science (http://vecpar.fe.up.pt/2016/) Paper Submission Deadline: 27 March 2016 --------------------------------------------------------------------------------------- ------------------------------------ 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. ------------------------------------ SUBMISSION ------------------------------------ Papers submitted to this workshop should not exceed 12 pages in length. The papers should be formatted according to the rules of the Springer Series Lecture Notes in Computer Science (LNCS). All accepted papers will be published by Springer in the series entitled Lecture Notes in Computer Science. The proceedings will be distributed in electronic format to participants of VECPAR 2016. You should make your submissions through the EasyChair system: https://easychair.org/conferences/?conf=bdl2016 ------------------------------------ REGISTRATION ------------------------------------ All participants must attend the VECPAR 2016 conference. http://vecpar.fe.up.pt/2016/registration.html ------------------------------------ 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) Vítor Santos Costa (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(a)isep.ipp.pt | www.isep.ipp.pt

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