GPU Support for Component-based Development of Embedded Systems

One pressing challenge of many modern embedded systems is to successfully deal with the considerable amount of data that originates from the interaction with the environment. A recent solution come ...

[1]  Jukka Mäki-Turja,et al.  The Rubus component model for resource constrained real-time systems , 2008, 2008 International Symposium on Industrial Embedded Systems.

[2]  Séverine Sentilles,et al.  Component Allocation Optimization for Heterogeneous CPU-GPU Embedded Systems , 2014, 2014 40th EUROMICRO Conference on Software Engineering and Advanced Applications.

[3]  M. Leeser,et al.  Heterogeneous tasks and conduits framework for rapid application portability and deployment , 2012, 2012 Innovative Parallel Computing (InPar).

[4]  J. Ramanujam,et al.  Automatic C-to-CUDA Code Generation for Affine Programs , 2010, CC.

[5]  Song Huang,et al.  On the energy efficiency of graphics processing units for scientific computing , 2009, 2009 IEEE International Symposium on Parallel & Distributed Processing.

[6]  Wolfgang Paul,et al.  GPU accelerated Monte Carlo simulation of the 2D and 3D Ising model , 2009, J. Comput. Phys..

[7]  Wen-mei W. Hwu,et al.  CUDA-Lite: Reducing GPU Programming Complexity , 2008, LCPC.

[8]  Pat Hanrahan,et al.  Brook for GPUs: stream computing on graphics hardware , 2004, SIGGRAPH 2004.

[9]  Kang G. Shin,et al.  Component allocation with multiple resource constraints for large embedded real-time software design , 2004, Proceedings. RTAS 2004. 10th IEEE Real-Time and Embedded Technology and Applications Symposium, 2004..

[10]  Santonu Sarkar,et al.  Reuse and Refactoring of GPU Kernels to Design Complex Applications , 2012, 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications.

[11]  Daniela Cruzes,et al.  Research synthesis in software engineering: A tertiary study , 2011, Inf. Softw. Technol..

[12]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[13]  Klaus Schulten,et al.  Accelerating Molecular Modeling Applications with GPU Computing , 2009 .

[14]  Christo Angelov,et al.  COMDES-II: A Component-Based Framework for Generative Development of Distributed Real-Time Control Systems , 2007, 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2007).

[15]  Peter J. Ashenden,et al.  Programming models for hybrid FPGA-cpu computational components: a missing link , 2004, IEEE Micro.

[16]  Steve Mann,et al.  OpenVIDIA: parallel GPU computer vision , 2005, ACM Multimedia.

[17]  Séverine Sentilles,et al.  A Classification Framework for Software Component Models , 2011, IEEE Transactions on Software Engineering.

[18]  Jean-Luc Dekeyser,et al.  A Model-Driven Design Framework for Massively Parallel Embedded Systems , 2011, TECS.

[19]  Pearl Brereton,et al.  A systematic review of systematic review process research in software engineering , 2013, Inf. Softw. Technol..

[20]  T. Greenhalgh,et al.  Effectiveness and efficiency of search methods in systematic reviews of complex evidence: audit of primary sources , 2005, BMJ : British Medical Journal.

[21]  Rudolf Eigenmann,et al.  OpenMP to GPGPU: a compiler framework for automatic translation and optimization , 2009, PPoPP '09.

[22]  Steven G. Parker,et al.  The CCA component model for high-performance scientific computing , 2006 .

[23]  Christoph W. Kessler,et al.  A Framework for Performance-Aware Composition of Applications for GPU-Based Systems , 2013, 2013 42nd International Conference on Parallel Processing.

[24]  Victor R. Basili,et al.  The Experimental Paradigm in Software Engineering , 1992, Experimental Software Engineering Issues.

[25]  Pradip Bose,et al.  Rugged Embedded Systems: Computing in Harsh Environments , 2016 .

[26]  Vijayan Sugumaran,et al.  A Platform-based Design Approach for Flexible Software Components , 2017, J. Inf. Technol. Theory Appl..

[27]  Geoff V. Merrett,et al.  Energy-Efficient Run-Time Mapping and Thread Partitioning of Concurrent OpenCL Applications on CPU-GPU MPSoCs , 2017, ACM Trans. Embed. Comput. Syst..

[28]  Séverine Sentilles,et al.  ProCom - the Progress Component Model Reference Manual, version 1.0 , 2008 .

[29]  Marilyn Wolf.  High-Performance Embedded Computing: Applications in Cyber-Physical Systems and Mobile Computing , 2014 .

[30]  Jan Carlson,et al.  Adding Support for Hardware Devices to Component Models for Embedded Systems , 2011, ICSEA 2011.

[31]  Gabriel Campeanu,et al.  Run-time component allocation in CPU-GPU embedded systems , 2017, SAC.

[32]  Michael Tiegelkamp,et al.  IEC 61131-3: Programming Industrial Automation Systems: Concepts and Programming Languages, Requirements for Programming Systems, Decision-Making Aids , 2001 .

[33]  Michael Barr,et al.  Programming embedded systems - with C and GNU development tools: thinking inside the box: includes real-time and Linux examples (2. ed.) , 2006 .

[34]  Zhisong Fu,et al.  MapGraph: A High Level API for Fast Development of High Performance Graph Analytics on GPUs , 2014, GRADES.

[35]  Wenguang Chen,et al.  MapCG: Writing parallel program portable between CPU and GPU , 2010, 2010 19th International Conference on Parallel Architectures and Compilation Techniques (PACT).

[36]  Feng Zhao,et al.  Energy-optimal software partitioning in heterogeneous multiprocessor embedded systems , 2008, 2008 45th ACM/IEEE Design Automation Conference.

[37]  Séverine Sentilles,et al.  Flexible Semantic-Preserving Flattening of Hierarchical Component Models , 2011, 2011 37th EUROMICRO Conference on Software Engineering and Advanced Applications.

[38]  Steve Bernier,et al.  Using OpenCL to Increase SCA Application Portability , 2017, J. Signal Process. Syst..

[39]  Peter Liggesmeyer,et al.  Trends in Embedded Software Engineering , 2009, IEEE Software.

[40]  Jennie Popay,et al.  Testing Methodological Guidance on the Conduct of Narrative Synthesis in Systematic Reviews , 2009 .

[41]  Vivek Sarkar,et al.  JCUDA: A Programmer-Friendly Interface for Accelerating Java Programs with CUDA , 2009, Euro-Par.

[42]  Clemens Grelck,et al.  Towards Heterogeneous Computing without Heterogeneous Programming , 2012, Trends in Functional Programming.

[43]  Christoph W. Kessler,et al.  The PEPPHER Composition Tool: Performance-Aware Dynamic Composition of Applications for GPU-Based Systems , 2012, 2012 SC Companion: High Performance Computing, Networking Storage and Analysis.

[44]  Sébastien Gérard,et al.  Split of composite components for distributed applications , 2013, Proceedings of the 2013 Forum on specification and Design Languages (FDL).

[45]  Kai Petersen,et al.  Systematic Mapping Studies in Software Engineering , 2008, EASE.

[46]  Greg Stitt,et al.  Elastic computing: A portable optimization framework for hybrid computers , 2012, Parallel Comput..

[47]  Jakob Axelsson,et al.  An analysis of a layered system architecture for autonomous construction vehicles , 2015, 2015 Annual IEEE Systems Conference (SysCon) Proceedings.

[48]  Frédéric Guyomarc'h,et al.  A Modeling Approach based on UML/MARTE for GPU Architecture , 2011, ArXiv.

[49]  Jacob Cohen,et al.  Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. , 1968 .

[50]  Narayanan Vijaykrishnan,et al.  Reliability concerns in embedded system designs , 2006, Computer.

[51]  Siegfried Benkner,et al.  Improving programmability of heterogeneous many-core systems via explicit platform descriptions , 2011, IWMSE '11.

[52]  Manish Parashar,et al.  Object-oriented stream programming using aspects , 2010, 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS).

[53]  Ivica Crnkovic,et al.  Building Reliable Component-Based Software Systems , 2002 .

[54]  Sam Malek,et al.  An energy consumption framework for distributed java-based systems , 2007, ASE.

[55]  Juval Lowy.  Programming NET Components : Design and Build NET Applications Using Component-Oriented Programming , 2009 .

[56]  Lars Grunske,et al.  Software Architecture Optimization Methods: A Systematic Literature Review , 2013, IEEE Transactions on Software Engineering.

[57]  Benoît Meister,et al.  A mapping path for multi-GPGPU accelerated computers from a portable high level programming abstraction , 2010, GPGPU-3.

[58]  David I. August,et al.  Automatic CPU-GPU communication management and optimization , 2011, PLDI '11.

[59]  Pearl Brereton,et al.  Performing systematic literature reviews in software engineering , 2006, ICSE.

[60]  Bálint Joó,et al.  A Framework for Lattice QCD Calculations on GPUs , 2014, 2014 IEEE 28th International Parallel and Distributed Processing Symposium.