research-article

Stateful dataflow multigraphs: a data-centric model for performance portability on heterogeneous architectures

Authors:

Johannes de Fine Licht,

Alexandros N. Ziogas,

Timo Schneider,

Torsten HoeflerAuthors Info & Claims

SC '19: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

Article No.: 81, Pages 1 - 14

https://doi.org/10.1145/3295500.3356173

Published: 17 November 2019 Publication History

Abstract

The ubiquity of accelerators in high-performance computing has driven programming complexity beyond the skill-set of the average domain scientist. To maintain performance portability in the future, it is imperative to decouple architecture-specific programming paradigms from the underlying scientific computations. We present the Stateful DataFlow multiGraph (SDFG), a data-centric intermediate representation that enables separating program definition from its optimization. By combining fine-grained data dependencies with high-level control-flow, SDFGs are both expressive and amenable to program transformations, such as tiling and double-buffering. These transformations are applied to the SDFG in an interactive process, using extensible pattern matching, graph rewriting, and a graphical user interface. We demonstrate SDFGs on CPUs, GPUs, and FPGAs over various motifs --- from fundamental computational kernels to graph analytics. We show that SDFGs deliver competitive performance, allowing domain scientists to develop applications naturally and port them to approach peak hardware performance without modifying the original scientific code.

References

[1]

Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2016. TensorFlow: A System for Large-scale Machine Learning. In Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation (OSDI'16). USENIX Association, Berkeley, CA, USA, 265--283.

Digital Library

[2]

Ping An, Alin Jula, Silvius Rus, Steven Saunders, Tim Smith, Gabriel Tanase, Nathan Thomas, Nancy Amato, and Lawrence Rauchwerger. 2003. STAPL: An Adaptive, Generic Parallel C++ Library. Springer Berlin Heidelberg, Berlin, Heidelberg, 193--208.

[3]

Riyadh Baghdadi, Ulysse Beaugnon, Albert Cohen, Tobias Grosser, Michael Kruse, Chandan Reddy, Sven Verdoolaege, Adam Betts, Alastair F. Donaldson, Jeroen Ketema, Javed Absar, Sven van Haastregt, Alexey Kravets, Anton Lokhmotov, Robert David, and Elnar Hajiyev. 2015. PENCIL: A Platform-Neutral Compute Intermediate Language for Accelerator Programming. In Proceedings of the 2015 International Conference on Parallel Architecture and Compilation (PACT) (PACT '15). IEEE Computer Society, Washington, DC, USA, 138--149.

Digital Library

[4]

Riyadh Baghdadi, Jessica Ray, Malek Ben Romdhane, Emanuele Del Sozzo, Patricia Suriana, Shoaib Kamil, and Saman P. Amarasinghe. 2018. Tiramisu: A Code Optimization Framework for High Performance Systems. CoRR abs/1804.10694 (2018). arXiv:1804.10694 http://arxiv.org/abs/1804.10694

[5]

Michael Bauer, Sean Treichler, Elliott Slaughter, and Alex Aiken. 2012. Legion: Expressing Locality and Independence with Logical Regions. In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis (SC '12). IEEE Computer Society Press, Los Alamitos, CA, USA, Article 66, 11 pages.

Digital Library

[6]

Tobias Becker, Oskar Mencer, and Georgi Gaydadjiev. 2016. Spatial Programming with OpenSPL. Springer International Publishing, Cham, 81--95.

[7]

Tal Ben-Nun, Ely Levy, Amnon Barak, and Eri Rubin. 2015. Memory Access Patterns: The Missing Piece of the Multi-GPU Puzzle. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '15). ACM, Article 19, 12 pages.

Digital Library

[8]

Maciej Besta, Michał Podstawski, Linus Groner, Edgar Solomonik, and Torsten Hoefler. 2017. To Push or To Pull: On Reducing Communication and Synchronization in Graph Computations. In Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing (HPDC '17). ACM, New York, NY, USA, 93--104.

Digital Library

[9]

Uday Bondhugula, Albert Hartono, J. Ramanujam, and P. Sadayappan. 2008. A Practical Automatic Polyhedral Program Optimization System. In ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI).

[10]

Zoran Budimlić, Michael Burke, Vincent Cavé, Kathleen Knobe, Geoff Lowney, Ryan Newton, Jens Palsberg, David Peixotto, Vivek Sarkar, Frank Schlimbach, and Sağnak Tasirlar. 2010. Concurrent Collections. Sci. Program. 18, 3--4 (Aug. 2010), 203--217.

Digital Library

[11]

B.L. Chamberlain, D. Callahan, and H.P. Zima. 2007. Parallel Programmability and the Chapel Language. The International Journal of High Performance Computing Applications 21, 3 (2007), 291--312.

Digital Library

[12]

Chun Chen, Jacqueline Chame, and Mary Hall. 2008. CHiLL: A framework for composing high-level loop transformations. Technical Report. University of Southern California.

[13]

Luigi P. Cordella, Pasquale Foggia, Carlo Sansone, and Mario Vento. 2004. A (sub)graph isomorphism algorithm for matching large graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 10 (Oct 2004), 1367--1372.

Digital Library

[14]

CUB 2019. CUB Library Documentation. http://nvlabs.github.io/cub/.

[15]

T. S. Czajkowski, U. Aydonat, D. Denisenko, J. Freeman, M. Kinsner, D. Neto, J. Wong, P. Yiannacouras, and D. P. Singh. 2012. From OpenCL to high-performance hardware on FPGAs. In 22nd International Conference on Field Programmable Logic and Applications (FPL). 531--534.

[16]

Leonardo Dagum and Ramesh Menon. 1998. OpenMP: An Industry-Standard API for Shared-Memory Programming. IEEE Comput. Sci. Eng. 5, 1 (Jan. 1998), 46--55.

Digital Library

[17]

Roshan Dathathri, Gurbinder Gill, Loc Hoang, Hoang-Vu Dang, Alex Brooks, Nikoli Dryden, Marc Snir, and Keshav Pingali. 2018. Gluon: A Communication-optimizing Substrate for Distributed Heterogeneous Graph Analytics. In Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2018). ACM, New York, NY, USA, 752--768.

Digital Library

[18]

NumPy Developers. 2019. NumPy Scientific Computing Package. http://www.numpy.org

[19]

H. Carter Edwards, Christian R. Trott, and Daniel Sunderland. 2014. Kokkos: Enabling manycore performance portability through polymorphic memory access patterns. J. Parallel and Distrib. Comput. 74, 12 (2014), 3202--3216. Domain-Specific Languages and High-Level Frameworks for High-Performance Computing.

Digital Library

[20]

Kayvon Fatahalian, Daniel Reiter Horn, Timothy J. Knight, Larkhoon Leem, Mike Houston, Ji Young Park, Mattan Erez, Manman Ren, Alex Aiken, William J. Dally, and Pat Hanrahan. 2006. Sequoia: Programming the Memory Hierarchy. In Proceedings of the 2006 ACM/IEEE Conference on Supercomputing (SC '06). ACM, New York, NY, USA, Article 83.

[21]

Jeanne Ferrante, Karl J. Ottenstein, and Joe D. Warren. 1987. The Program Dependence Graph and Its Use in Optimization. ACM Trans. Program. Lang. Syst. 9, 3 (July 1987), 319--349.

Digital Library

[22]

Python Software Foundation. 2019. The Python Programming Language. https://www.python.org

[23]

Franz Franchetti, Tze-Meng Low, Thom Popovici, Richard Veras, Daniele G. Spampinato, Jeremy Johnson, Markus Püschel, James C. Hoe, and José M. F. Moura. 2018. SPIRAL: Extreme Performance Portability. Proceedings of the IEEE, special issue on "From High Level Specification to High Performance Code" 106, 11 (2018).

[24]

V. Gajinov, S. Stipic, O. S. Unsal, T. Harris, E. Ayguadé, and A. Cristal. 2012. Supporting stateful tasks in a dataflow graph. In 2012 21st International Conference on Parallel Architectures and Compilation Techniques (PACT). 435--436.

[25]

Kazushige Goto and Robert A. van de Geijn. 2008. Anatomy of High-performance Matrix Multiplication. ACM Trans. Math. Softw. 34, 3, Article 12 (May 2008), 25 pages.

Digital Library

[26]

Tobias Grosser, Armin Groesslinger, and Christian Lengauer. 2012. Polly --- Performing Polyhedral Optimizations on a Low-Level Intermediate Representation. Parallel Processing Letters 22, 04 (2012), 1250010.

[27]

Khronos Group. 2019. OpenCL. https://www.khronos.org/opencl

[28]

Khronos Group. 2019. OpenVX. https://www.khronos.org/openvx

[29]

Khronos Group. 2019. SYCL. https://www.khronos.org/sycl

[30]

Intel. 2019. Math Kernel Library (MKL). https://software.intel.com/en-us/mkl

[31]

Michael Isard, Mihai Budiu, Yuan Yu, Andrew Birrell, and Dennis Fetterly. 2007. Dryad: Distributed Data-parallel Programs from Sequential Building Blocks. In Proceedings of the 2Nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007 (EuroSys '07). ACM, New York, NY, USA, 59--72.

Digital Library

[32]

Wesley M. Johnston, J. R. Paul Hanna, and Richard J. Millar. 2004. Advances in Dataflow Programming Languages. ACM Comput. Surv. 36, 1 (March 2004), 1--34.

Digital Library

[33]

Hartmut Kaiser, Thomas Heller, Bryce Adelstein-Lelbach, Adrian Serio, and Dietmar Fey. 2014. HPX: A Task Based Programming Model in a Global Address Space. In Proceedings of the 8th International Conference on Partitioned Global Address Space Programming Models (PGAS '14). ACM, New York, NY, USA, Article 6, 11 pages.

Digital Library

[34]

Laxmikant V. Kale and Sanjeev Krishnan. 1993. CHARM++: A Portable Concurrent Object Oriented System Based on C++. In Proceedings of the Eighth Annual Conference on Object-oriented Programming Systems, Languages, and Applications (OOPSLA '93). ACM, New York, NY, USA, 91--108.

Digital Library

[35]

David Koeplinger, Matthew Feldman, Raghu Prabhakar, Yaqi Zhang, Stefan Hadjis, Ruben Fiszel, Tian Zhao, Luigi Nardi, Ardavan Pedram, Christos Kozyrakis, and Kunle Olukotun. 2018. Spatial: A Language and Compiler for Application Accelerators. In Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2018). ACM, New York, NY, USA, 296--311.

Digital Library

[36]

M. Kong, L. N. Pouchet, P. Sadayappan, and V. Sarkar. 2016. PIPES: A Language and Compiler for Task-Based Programming on Distributed-Memory Clusters. In SC16: International Conference for High Performance Computing, Networking, Storage and Analysis. 456--467.

[37]

Maria Kotsifakou, Prakalp Srivastava, Matthew D. Sinclair, Rakesh Komuravelli, Vikram Adve, and Sarita Adve. 2018. HPVM: Heterogeneous Parallel Virtual Machine. In Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP '18). ACM, New York, NY, USA, 68--80.

Digital Library

[38]

Chris Lattner and Vikram Adve. 2004. LLVM: A Compilation Framework for Lifelong Program Analysis and Transformation. In CGO. San Jose, CA, USA, 75--88.

Digital Library

[39]

S. Lee, J. Kim, and J. S. Vetter. 2016. OpenACC to FPGA: A Framework for Directive-Based High-Performance Reconfigurable Computing. In 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS). 544--554.

[40]

LLNL. 2019. RAJA Performance Portability Layer. https://github.com/LLNL/RAJA

[41]

Mathieu Luisier, Andreas Schenk, Wolfgang Fichtner, and Gerhard Klimeck. 2006. Atomistic simulation of nanowires in the sp³d⁵s^* tight-binding formalism: From boundary conditions to strain calculations. Phys. Rev. B 74 (Nov 2006), 205323. Issue 20.

[42]

Ravi Teja Mullapudi, Vinay Vasista, and Uday Bondhugula. 2015. PolyMage: Automatic Optimization for Image Processing Pipelines. SIGARCH Comput. Archit. News 43, 1 (March 2015), 429--443.

Digital Library

[43]

Derek G. Murray, Frank McSherry, Rebecca Isaacs, Michael Isard, Paul Barham, and Martín Abadi. 2013. Naiad: A Timely Dataflow System. In Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles (SOSP '13). ACM, New York, NY, USA, 439--455.

Digital Library

[44]

Donald Nguyen, Andrew Lenharth, and Keshav Pingali. 2013. A Lightweight Infrastructure for Graph Analytics. In Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles (SOSP '13). ACM, New York, NY, USA, 456--471.

Digital Library

[45]

NVIDIA. 2019. CUBLAS Library Documentation. http://docs.nvidia.com/cuda/cublas.

[46]

NVIDIA. 2019. CUSPARSE Library Documentation. http://docs.nvidia.com/cuda/cusparse.

[47]

Patrick McCormick. 2019. Yin & Yang: Hardware Heterogeneity & Software Productivity. Talk at SOS23 meeting, Asheville, NC.

[48]

L. N. Pouchet. 2016. PolyBench: The Polyhedral Benchmark suite. https://sourceforge.net/projects/polybench

[49]

Jonathan Ragan-Kelley, Connelly Barnes, Andrew Adams, Sylvain Paris, Frédo Durand, and Saman Amarasinghe. 2013. Halide: A Language and Compiler for Optimizing Parallelism, Locality, and Recomputation in Image Processing Pipelines. In Proceedings of the 34th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI '13). ACM, New York, NY, USA, 519--530.

Digital Library

[50]

Gabe Rudy, Malik Murtaza Khan, Mary Hall, Chun Chen, and Jacqueline Chame. 2011. A Programming Language Interface to Describe Transformations and Code Generation. In Languages and Compilers for Parallel Computing, Keith Cooper, John Mellor-Crummey, and Vivek Sarkar (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 136--150.

[51]

Alina Sbîrlea, Jun Shirako, Louis-Noël Pouchet, and Vivek Sarkar. 2016. Polyhedral Optimizations for a Data-Flow Graph Language. In Revised Selected Papers of the 28th International Workshop on Languages and Compilers for Parallel Computing - Volume 9519 (LCPC 2015). Springer-Verlag, Berlin, Heidelberg, 57--72.

Digital Library

[52]

Emanuele Del Sozzo, Riyadh Baghdadi, Saman P. Amarasinghe, and Marco D. Santambrogio. 2018. A Unified Backend for Targeting FPGAs from DSLs. 2018 IEEE 29th International Conference on Application-specific Systems, Architectures and Processors (ASAP) (2018), 1--8.

[53]

Michel Steuwer, Christian Fensch, Sam Lindley, and Christophe Dubach. 2015. Generating Performance Portable Code Using Rewrite Rules: From High-level Functional Expressions to High-performance OpenCL Code. In Proceedings of the 20th ACM SIGPLAN International Conference on Functional Programming (ICFP 2015). ACM, New York, NY, USA, 205--217.

Digital Library

[54]

SymPy Development Team. 2019. SymPy Symbolic Math Library. http://www.sympy.org

[55]

William Thies, Michal Karczmarek, and Saman P. Amarasinghe. 2002. StreamIt: A Language for Streaming Applications. In Proceedings of the 11th International Conference on Compiler Construction (CC '02). Springer-Verlag, London, UK, UK, 179--196.

Digital Library

[56]

D. Unat, A. Dubey, T. Hoefler, J. Shalf, M. Abraham, M. Bianco, B. L. Chamberlain, R. Cledat, H. C. Edwards, H. Finkel, K. Fuerlinger, F. Hannig, E. Jeannot, A. Kamil, J. Keasler, P. H. J. Kelly, V. Leung, H. Ltaief, N. Maruyama, C. J. Newburn, and M. Pericàs. 2017. Trends in Data Locality Abstractions for HPC Systems. IEEE Transactions on Parallel and Distributed Systems 28, 10 (Oct 2017), 3007--3020.

[57]

US Department of Energy. 2019. Definition - Performance Portability. https://performanceportability.org/perfport/definition/.

[58]

Sven Verdoolaege, Juan Carlos Juega, Albert Cohen, José Ignacio Gómez, Christian Tenllado, and Francky Catthoor. 2013. Polyhedral parallel code generation for CUDA. ACM Trans. Archit. Code Optim. 9, 4 (Jan. 2013), 54:1--54:23.

Digital Library

[59]

Jeffrey S. Vetter, Ron Brightwell, Maya Gokhale, Pat McCormick, Rob Ross, John Shalf, Katie Antypas, David Donofrio, Travis Humble, Catherine Schuman, Brian Van Essen, Shinjae Yoo, Alex Aiken, David Bernholdt, Suren Byna, Kirk Cameron, Frank Cappello, Barbara Chapman, Andrew Chien, Mary Hall, Rebecca Hartman-Baker, Zhiling Lan, Michael Lang, John Leidel, Sherry Li, Robert Lucas, John Mellor-Crummey, Paul Peltz Jr., Thomas Peterka, Michelle Strout, and Jeremiah Wilke. 2018. Extreme Heterogeneity 2018 - Productive Computational Science in the Era of Extreme Heterogeneity: Report for DOE ASCR Workshop on Extreme Heterogeneity. (12 2018).

[60]

Mohamed Wahib and Naoya Maruyama. 2014. Scalable Kernel Fusion for Memory-bound GPU Applications. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '14). IEEE Press, Piscataway, NJ, USA, 191--202.

Digital Library

[61]

Xilinx. 2019. SDAccel. https://www.xilinx.com/products/design-tools/software-zone/sdaccel.html

[62]

Xilinx. 2019. Vivado HLS. https://www.xilinx.com/products/design-tools/vivado

[63]

Jin Zhou and Brian Demsky. 2010. Bamboo: A Data-centric, Object-oriented Approach to Many-core Software. SIGPLAN Not. 45, 6 (June 2010), 388--399.

Digital Library

[64]

A. N. Ziogas, T. Ben-Nun, G. Indalecio Fernández, T. Schneider, M. Luisier, and T. Hoefler. 2019. A Data-Centric Approach to Extreme-Scale Ab initio Dissipative Quantum Transport Simulations. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '19).

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Rudi JLee YChadha AWahib MWeide KO’Neal JDubey A(2025)CG-Kit: Code Generation Toolkit for performant and maintainable variants of source code applied to Flash-X hydrodynamics simulationsFuture Generation Computer Systems10.1016/j.future.2024.107511163(107511)Online publication date: Feb-2025
https://doi.org/10.1016/j.future.2024.107511
Brown N(2024)Domain Specific Abstractions for the Development of Fast-by-Construction Dataflow Codes on FPGAsChips10.3390/chips30400173:4(334-360)Online publication date: 4-Oct-2024
https://doi.org/10.3390/chips3040017
Andersson MKarp MMarkidis S(2024)Towards Performance Portable Kernels for Computational Fluid Dynamics Using DaCeWorkshop Proceedings of the 53rd International Conference on Parallel Processing10.1145/3677333.3678270(110-111)Online publication date: 12-Aug-2024
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Index Terms

Stateful dataflow multigraphs: a data-centric model for performance portability on heterogeneous architectures
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interactive systems and tools
2. Software and its engineering
  1. Software notations and tools
    1. Compilers
      1. Just-in-time compilers
    2. General programming languages
      1. Language types
        Data flow languages
        Parallel programming languages

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cover image ACM Conferences

SC '19: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

November 2019

1921 pages

ISBN:9781450362290

DOI:10.1145/3295500

General Chair:
Michela Taufer,
Program Chairs:
Pavan Balaji,
Antonio J. Peña

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Published: 17 November 2019

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https://doi.org/10.1016/j.future.2024.107511
Brown N(2024)Domain Specific Abstractions for the Development of Fast-by-Construction Dataflow Codes on FPGAsChips10.3390/chips30400173:4(334-360)Online publication date: 4-Oct-2024
https://doi.org/10.3390/chips3040017
Andersson MKarp MMarkidis S(2024)Towards Performance Portable Kernels for Computational Fluid Dynamics Using DaCeWorkshop Proceedings of the 53rd International Conference on Parallel Processing10.1145/3677333.3678270(110-111)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3677333.3678270
Zhu Q(2024)FreeStencil: A Fine-Grained Solver Compiler with Graph and Kernel Optimizations on Structured Meshes for Modern GPUsProceedings of the 53rd International Conference on Parallel Processing10.1145/3673038.3673076(1022-1031)Online publication date: 12-Aug-2024
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