Smart Containers and Skeleton Programming for GPU-Based Systems

In this paper, we discuss the role, design and implementation of smart containers in the SkePU skeleton library for GPU-based systems. These containers provide an interface similar to C++ STL containers but internally perform runtime optimization of data transfers and runtime memory management for t...

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Vydané v:	International journal of parallel programming Ročník 44; číslo 3; s. 506 - 530
Hlavní autori:	Dastgeer, Usman, Kessler, Christoph
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.06.2016 Springer Nature B.V
Predmet:	Analysis C plus plus Communication Computer memory Computer programming Computer Science Consistency Containers Design engineering Interfaces Libraries Memory management Optimization Processor Architectures Programming Run time (computers) Software Engineering/Programming and Operating Systems Studies Synchronization Theory of Computation SkePU Skeleton programming Memory management Smart containers Runtime optimizations GPU-based systems
ISSN:	0885-7458, 1573-7640, 1573-7640
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Abstract	In this paper, we discuss the role, design and implementation of smart containers in the SkePU skeleton library for GPU-based systems. These containers provide an interface similar to C++ STL containers but internally perform runtime optimization of data transfers and runtime memory management for their operand data on the different memory units. We discuss how these containers can help in achieving asynchronous execution for skeleton calls while providing implicit synchronization capabilities in a data consistent manner. Furthermore, we discuss the limitations of the original, already optimizing memory management mechanism implemented in SkePU containers, and propose and implement a new mechanism that provides stronger data consistency and improves performance by reducing communication and memory allocations. With several applications, we show that our new mechanism can achieve significantly (up to 33.4 times) better performance than the initial mechanism for page-locked memory on a multi-GPU based system.
AbstractList	In this paper, we discuss the role, design and implementation of smart containers in the SkePU skeleton library for GPU-based systems. These containers provide an interface similar to C++ STL containers but internally perform runtime optimization of data transfers and runtime memory management for their operand data on the different memory units. We discuss how these containers can help in achieving asynchronous execution for skeleton calls while providing implicit synchronization capabilities in a data consistent manner. Furthermore, we discuss the limitations of the original, already optimizing memory management mechanism implemented in SkePU containers, and propose and implement a new mechanism that provides stronger data consistency and improves performance by reducing communication and memory allocations. With several applications, we show that our new mechanism can achieve significantly (up to 33.4 times) better performance than the initial mechanism for page-locked memory on a multi-GPU based system. In this paper, we discuss the role, design and implementation of smart containers in the SkePU skeleton library for GPU-based systems. These containers provide an interface similar to C++ STL containers but internally perform runtime optimization of data transfers and runtime memory management for their operand data on the different memory units. We discuss how these containers can help in achieving asynchronous execution for skeleton calls while providing implicit synchronization capabilities in a data consistent manner. Furthermore, we discuss the limitations of the original, already optimizing memory management mechanism implemented in SkePU containers, and propose and implement a new mechanism that provides stronger data consistency and improves performance by reducing communication and memory allocations. With several applications, we show that our new mechanism can achieve significantly (up to 33.4 times) better performance than the initial mechanism for page-locked memory on a multi-GPU based system. Issue Title: Special Issue on High-Level Parallel Programming and Applications In this paper, we discuss the role, design and implementation of smart containers in the SkePU skeleton library for GPU-based systems. These containers provide an interface similar to C++ STL containers but internally perform runtime optimization of data transfers and runtime memory management for their operand data on the different memory units. We discuss how these containers can help in achieving asynchronous execution for skeleton calls while providing implicit synchronization capabilities in a data consistent manner. Furthermore, we discuss the limitations of the original, already optimizing memory management mechanism implemented in SkePU containers, and propose and implement a new mechanism that provides stronger data consistency and improves performance by reducing communication and memory allocations. With several applications, we show that our new mechanism can achieve significantly (up to 33.4 times) better performance than the initial mechanism for page-locked memory on a multi-GPU based system.
Author	Kessler, Christoph Dastgeer, Usman
Author_xml	– sequence: 1 givenname: Usman surname: Dastgeer fullname: Dastgeer, Usman organization: PELAB, Department of Computer and Information Science, Linköping University – sequence: 2 givenname: Christoph surname: Kessler fullname: Kessler, Christoph email: christoph.kessler@liu.se organization: PELAB, Department of Computer and Information Science, Linköping University
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Cites_doi	10.1145/1863482.1863487 10.1007/978-3-642-40447-4_18 10.1109/MM.2011.89 10.1109/HPEC.2014.7040988 10.1109/PDP.2013.29 10.1145/1944862.1944883 10.1109/IPDPS.2011.269 10.1007/s10766-006-0018-x 10.1145/2086696.2086721 10.1504/IJHPCN.2012.046370 10.1007/s00450-011-0157-1 10.1007/978-3-642-40047-6_86 10.1017/CBO9781139051224
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References_xml	– reference: KecklerSWDallyWJKhailanyBGarlandMGlascoDGPUs and the future of parallel computingIEEE Micro.201131571710.1109/MM.2011.89 – reference: Dastgeer, U.: Skeleton programming for heterogeneous GPU-based systems. Licentiate thesis. Thesis No. 1504. Department of Computer and Information Science, Linköping University (2011). URL http://liu.diva-portal.org/smash/record.jsf?pid=diva2:437140 – reference: Marques, R., Paulino, H., Alexandre, F., Medeiros, P.D.: Algorithmic skeleton framework for the orchestration of GPU computations. In: Euro-Par 2013 Parallel Processing. Lecture Notes in Computer Science, vol. 8097, pp. 874–885. Springer, Berlin Heidelberg (2013) – reference: NVIDIA Corporation: NVIDIA CUDA C Programming Guide (2013). http://docs.nvidia.com/cuda/cuda-c-programming-guide – reference: GrelckCScholzSSAC-A functional array language for efficient multi-threaded executionInt. J. Parallel Program.200634438342710.1007/s10766-006-0018-x1102.68438 – reference: ShainerGThe development of Mellanox/NVIDIA GPUDirect over InfiniBand—a new model for GPU to GPU communicationsComput. Sci.-Res. Dev.2011263410.1007/s00450-011-0157-1 – reference: ColeMIAlgorithmic Skeletons: Structured Management of Parallel Computation1989CambridgeAddison-Wesley0681.68041 – reference: Goli, M., Gonzalez-Velez, H.: Heterogeneous algorithmic skeletons for FastFlow with seamless coordination over hybrid architectures. In: 21st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 148–156 (2013) – reference: Hoberock, J., Bell, N.: Thrust: C++ template library for CUDA (2011). http://code.google.com/p/thrust/ – reference: KichererMNowakFBuchtyRKarlWSeamlessly portable applications: managing the diversity of modern heterogeneous systemsACM Trans. Archit. Code Optim.20128442:142:2010.1145/2086696.2086721 – reference: Park, J.: Memory optimizations of embedded applications for energy efficiency. Ph.D. thesis, Dept. of Electrical Engineering. University of Stanford (2011) – reference: Kicherer, M., Buchty, R., Karl, W.: Cost-aware function migration in heterogeneous systems. In: 6th International Conference on High Performance and Embedded Architectures and Compilers. HiPEAC ’11, pp. 137–145. ACM, New York, NY, USA (2011) – reference: Diogo, M., Grelck, C.: Towards Heterogeneous Computing without Heterogeneous Programming. In: H.W. Loidl, R. Pena (eds.): 13th Int. Symposium on Trends in Functional Programming (TFP 2012), St. Andrews, UK, Lecture Notes in Computer Science 7829, pp. 279–294, Springer (2013) – reference: DuboisMAnnavaramMStenströmPParallel Computer Organization and Design2012CambridgeCambridge University Press10.1017/CBO9781139051224 – reference: Ciechanowicz, P., Poldner, M., Kuchen, H.: The Münster skeleton library Muesli—a comprehensive overview (2009). ERCIS Working Paper No. 7 – reference: AlexandrescuAModern C++ Design20011BostonAddison-Wesley Professional – reference: ErnstingSKuchenHAlgorithmic skeletons for multi-core, multi-GPU systems and clustersInt. J. High Perform. Comput. Netw.2012712913810.1504/IJHPCN.2012.046370 – reference: Landaverde, R., Zhang, T., Coskun, A., Herbordt, M.: An investigation of Unified Memory access performance in CUDA. In: IEEE High Performance Extreme Computing Conference, Waltham, USA (2014) – reference: Steuwer, M., Kegel, P., Gorlatch, S.: SkelCL—A portable skeleton library for high-level GPU programming. In: 16th International Workshop on High-Level Parallel Programming Models and Supportive Environments, HIPS ’11 (2011) – reference: Aufmann, R., Barker, V., Lockwood, J.: Intermediate Algebra with Applications, Multimedia Edition. Cengage Learning (2008). URL http://books.google.se/books?id=QYfJAxqwDE8C – reference: Dastgeer, U., Kessler, C., Thibault, S.: Flexible runtime support for efficient skeleton programming. In: Advances in Parallel Computing, vol. 22, pp. 159–166. IOS Press (2012). Proc. ParCo conference, Ghent, Belgium (Sep . 2011) – reference: Harris, M.: CUDA Unfied Memory in CUDA 6. Nvidia, http://devblogs.nvidia.com/parallelforall/unified-memory-in-cuda-6 (2013) – reference: Dastgeer, U.: Performance-aware component composition for GPU-based systems. Ph.D. thesis, Linköping University (2014). URL http://www.diva-portal.org/smash/record.jsf?pid=diva2:712422 – reference: Enmyren, J., Kessler, C.: SkePU: A Multi-Backend Skeleton Programming Library for Multi-GPU Systems. In: Proceedings of 4th International Workshop on High-Level Parallel Programming and Applications (HLPP-2010), Baltimore, USA, ACM (Sep. 2010) – ident: 357_CR10 doi: 10.1145/1863482.1863487 – ident: 357_CR8 doi: 10.1007/978-3-642-40447-4_18 – ident: 357_CR7 – volume: 31 start-page: 7 issue: 5 year: 2011 ident: 357_CR16 publication-title: IEEE Micro. doi: 10.1109/MM.2011.89 – ident: 357_CR19 doi: 10.1109/HPEC.2014.7040988 – ident: 357_CR3 – ident: 357_CR5 – ident: 357_CR6 – ident: 357_CR12 doi: 10.1109/PDP.2013.29 – ident: 357_CR22 – volume-title: Modern C++ Design year: 2001 ident: 357_CR1 – ident: 357_CR21 – ident: 357_CR2 – ident: 357_CR17 doi: 10.1145/1944862.1944883 – ident: 357_CR24 doi: 10.1109/IPDPS.2011.269 – volume-title: Algorithmic Skeletons: Structured Management of Parallel Computation year: 1989 ident: 357_CR4 – volume: 34 start-page: 383 issue: 4 year: 2006 ident: 357_CR13 publication-title: Int. J. 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Snippet	In this paper, we discuss the role, design and implementation of smart containers in the SkePU skeleton library for GPU-based systems. These containers provide... Issue Title: Special Issue on High-Level Parallel Programming and Applications In this paper, we discuss the role, design and implementation of smart... In this paper, we discuss the role, design and implementation of smart containers in the SkePU skeleton library for GPU-based systems. These containers provide...
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SubjectTerms	Analysis C plus plus Communication Computer memory Computer programming Computer Science Consistency Containers Design engineering Interfaces Libraries Memory management Optimization Processor Architectures Programming Run time (computers) Software Engineering/Programming and Operating Systems Studies Synchronization Theory of Computation
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Title	Smart Containers and Skeleton Programming for GPU-Based Systems
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