Parallel Programming Models for Big-Data Modelling and Simulation
Chair: Prof. Marco Aldinucci, Italy
Vice-Chairs: Prof. Christoph Kessler, Sweden; Dr. Peter Kilpatrick, UK
A core challenge in Modelling and Simulation is the need to combine software expertise and domain expertise. Even starting from well-defined mathematical models, they still have to be manually coded. When parallel or distributed computation is required, the coding becomes much harder. This may impair time-to-solution, performance, and performance portability across different platforms. These problems have been traditionally addressed by trying to lift software design and development to a higher level of abstraction. In the Domain Specific Languages (DSL) approach, abstractions aim to provide domain experts with programming primitives that match specific concepts in their domain, whereas performance and portability issues are ideally moved (with various degrees of effectiveness) to development tools. Examples include Verilog and VHDL hardware description languages, MATLAB and GNU Octave for matrix programming, Mathematica and Maxima for symbolic mathematics, etc. In the general-purpose approaches, such as Model-Driven Engineering (MDE), general-purpose programming concepts are abstracted into high-level constructs enforcing extra-functional features by design, e.g. compositionality, portability, parallelizability. In this regard, the number and the quality of programming models enabling the high-level management of parallelism have steadily increased and, in some cases, these approaches have become mainstream for a range of HPC, dataintensive and Big Data workloads: streaming (e.g. Storm, S4, Infosphere stream, FastFlow), structured parallel programming and MapReduce (e.g. Hadoop, Intel TBB, OpenMP, MPI), SIMD (e.g. OpenACC, SkePU).
Prof. Marco Aldinucci is the Principal Investigator at the research group on Parallel Computing at Computer Science Department of University of Torino (alpha) and at the NVidia CUDA research centre at University of Torino. He received his PhD from University of Pisa, He has authored over 110 papers and participated in over 25 national and international research projects concerning parallel computing, autonomic computing, grid and cloud topics, including 3 EU FP6 projects, 3 EU FP7 projects (HiPEAC, Paraphrase, REPARA), 3 currently on going H2020 projects (RePhrase, HyVar, Toreador). He also participated to 3 COST Actions (Betty, Nesus, Chipset). He has been the leader at the University of Torino in 6 EU projects and Work Package leader in 3 of them.
He is the recipient of a HPC Advisory Council award 2011 and the IEEE HPCC outstanding leadership award delivered over 15 invited talks in international venues. He has organized and he has edited several conference proceedings (such as IEEE PDP and IEEE ScalCom), and special issue of journals (such as Sage IJHPCA). He is a member of HPC Advisory Council and HPC500. At University of Torino, he has been ranked 1st (over 481 lecturers) in the evaluation exercise 2009-2012 and is currently part of the Steering Committee of the PhD school in Informatics.
He participated in the design of several frameworks for parallel programming including compilers, libraries and frameworks, both in industrial and academic teams. They include ASSIST, Muskel, and FastFlow programming environments, the VirtuaLinux cloud platform, and the ETSI standard Grid Component Model (GCM).
Christoph W. Kessler is a professor for Computer Science at Linköping University, Sweden, where he leads the Programming Environment Laboratory’s research group on compiler technology and parallel computing.
Christoph Kessler received a PhD degree in Computer Science in 1994 from the University of Saarbrücken, Germany, and a Habilitation degree in 2001 from the University of Trier, Germany.
In 2001 he joined Linköping university, Sweden, as associate professor at the programming environments lab (PELAB) of the computer science department (IDA).
In 2007 he was appointed full professor at Linköping university.
His research interests include parallel programming, compiler technology, code generation, optimization algorithms, and software composition.
He has published two books, several book chapters and more than 100 scientific papers in international journals and conferences. His contributions include e.g. the OPTIMIST retargetable optimizing integrated code generator for VLIW and DSP processors, the PARAMAT approach to pattern-based automatic parallelization, the concept of multi-variant performance-aware parallel components for optimized composition, the PEPPHER component model and composition tool for heterogeneous multicore/manycore based systems, the SkePU library of tunable generic components for GPU-based systems, and the parallel programming languages Fork and NestStep.
Peter Kilpatrick is a Senior Lecturer in the School of Electronics, Electrical Engineering and Computer Science of Queen’s University Belfast, UK. He has published more than 110 peer-reviewed papers, mostly in the fields of parallel and distributed programming, programming transformation and formal modelling. He has been involved in a number of UK government, EU and industry-supported research projects, most recently the EU ParaPhrase and CACTOS projects. His current research interest centres on pattern-based parallel computing and autonomic management of non-functional concerns in parallel systems. He has held visiting positions at the University of Queensland, the Max-Planck Institute for Astrophysics (Munich) and the University of Pisa.
Name Contact Country Expertise
Apostolos N. PapadopoulosM firstname.lastname@example.org Greece data mining, databases, big data analytics, distributed processing
Ari VisaM Tampere University of Technology, Department of Signal Processing, email@example.com Finland big data, signal processing, machine learning, software engineering
Cevdet AykanatM Bilkent University, firstname.lastname@example.org Turkey HPC, parallel computing, parallel programming, parallel sparse matrix kernels for big data applications
Christoph KesslerM Linköping University, email@example.com Sweden parallel programming models; high-level parallel programming; adaptive program optimizations; mapping and scheduling
Ciprian-Octavian TruicăS University Politehnica of Bucharest, firstname.lastname@example.org Romania Distributed Databases; Data Aggregation; Cloud Computing; Parallel programming
Clemens GrelckM University of Amsterdam, email@example.com Netherlands programming languages, compilers, parallel computing, resource-aware computing
Corinne AncourtM MINES ParisTech, firstname.lastname@example.org France Compilation, Program optimization for parallel architectures, Integer linear programming
Daniela OrozovaM email@example.com Bulgaria Modelling tools, Big Data Applications, Intelligent Tutoring Systems, Network programming
Edgars CelmsM Institute of Mathematics and Computer Science, University of Latvia, firstname.lastname@example.org Latvia Model-Driven Software Development, Ontologies, Semantic technologies, Domain Specific Languages
Eleni Karatza Aristotle University of Thessaloniki, email@example.com Greece Simulation and Performance Analysis of Grids and Clouds, Mapping/Scheduling Techniques
Elisabeth LarssonM firstname.lastname@example.org Sweden High performance computing, task parallel programming frameworks
Francisco de SandeS Universidad de La Laguna, email@example.com Spain HPC, GPGPU, Compilers technology, Parallelization of Scientific Applications
GABER Jaafar Universite de Technologie de Belfort-Montbeliard,, firstname.lastname@example.org France multicore and parallel algorithms
George A. PapadopoulosM email@example.com Cyprus Parallel Programming Models, Model-Driven Development, Coordination Models, Context Aware Systems
George SuciuS R&D Department, BEIA Consult International, E-mail: firstname.lastname@example.org Romania Big data indexing, search based applications, cloud platforms, IoT data-mining
Georgios L. Stavrinides Affiliation: Department of Informatics, Aristotle University of Thessaloniki, Greece. Email: email@example.com Greece Modelling, simulation and performance evaluation of distributed systems; scheduling algorithms for complex workloads; cloud computing; real-time distributed systems.
Hans VangheluweM University of Antwerp, Hans.Vangheluwe@uantwerpen.be Belgium Model Based Systems Engineering, Modelling and Simulation Language Engineering, CPS, distributed simulators
Horacio Gonzalez-VelezVC NCI, firstname.lastname@example.org Ireland Parallel Programming; High Performance Computing; Algorithmic Skeletons;
Ilias Mavridis Aristotle University of Thessaloniki, email@example.com Greece cloud computing, distributed processing, modeling , simulation
Ivan Merelli firstname.lastname@example.org Italy bioinformatics, computational biology, high performance computing
Jamal RaiynM Computer Science Department, email@example.com Isreael cellular system, resource allocation strategies, ITS
Lalit GargM University of Malta, firstname.lastname@example.org Malta Modeling and Simulation, Operational research, Missing data handling, Data mining
Luís CorreiaM Luis.Correia@ciencias.ulisboa.pt Portugal Self-organised systems; Multi-agent systems
Luís Veiga INESC-ID Lisboa, IST, Univ. Lisboa Portugal virtual machines, cloud resource scheduling, middleware, Big-data processing
Marco AldinucciM University of Torino, email@example.com Italy parallel computing, distributed computing, HPC, cloud engineering
Massimo Torquati firstname.lastname@example.org Italy Parallel Programming Models; High-Performance Streaming Computations; Distributed Computing
Michel Steuwer University of Edinburgh, email@example.com UK High-level Parallel Programming; Algorithmic Skeletons; Compilation; Heterogeneous Systems
Miguel Goulão Universidade Nova de Lisboa, firstname.lastname@example.org Portugal Empirical Software Engineering, Systematic Literature Reviews
Natalija Stojanovic University of Nis, email@example.com Serbia High performance computing, parallel programming for GPU and multi/core architectures, distributed programming and systems
Paulo Carreira Instituto Superior Técnico, Universidade de Lisboa Portugal Real-time Data Processing; Models and languages for Big Data; Intelligent Buildings; Energy Management
Peter KilpatrickM Queen's University Belfast, firstname.lastname@example.org UK Programming Models, Parallel Patterns
Pierre KuonenM email@example.com Switzerland HPC, middleware for parallel and distributed applications, parallel and distributed programming, GPU programming
Pietro LioS firstname.lastname@example.org UK Big data
Qian Wang Software Research Institute, Athlone Institute of Technology, email@example.com Ireland IoT, Information Centric Technology, Data Computing
Sabri PllanaS Linnaeus University, firstname.lastname@example.org Sweden high-performance genomics; high-performance AI; performance-oriented software engineering;
Sanja BrdarS University Novi Sad, email@example.com Serbia Machine learning, Data Fusion, Distributed Computing
Siegfried BenknerM firstname.lastname@example.org Austria parallel programming models, compilers, runtime systems
Vasco Miguel Moreira do AmaralM email@example.com Portugal Domain Specific Modelling Languages, Model-Driven Software Development, Software Quality , Cyberphysical Systems Modelling
Vicente Blanco firstname.lastname@example.org Spain HPC, Parallel Programming, Performance Analysis, Heterogeneous Computing