“Ebb: A DSL for Physical Simulation on CPUs and GPUs” by Bernstein, Shah, Lemire, DeVito, Fisher, et al. …

  • ©Gilbert Bernstein, Chinmayee Shah, Crystal Lemire, Zachary DeVito, Matthew Fisher, Philip Levis, and Patrick (Pat) Hanrahan

Conference:


Type(s):


Title:

    Ebb: A DSL for Physical Simulation on CPUs and GPUs

Session/Category Title:   RENDERING & SIMULATION WITH GPUS


Presenter(s)/Author(s):


Moderator(s):



Abstract:


    Designing programming environments for physical simulation is challenging because simulations rely on diverse algorithms and geometric domains. These challenges are compounded when we try to run efficiently on heterogeneous parallel architectures. We present Ebb, a Domain-Specific Language (DSL) for simulation, that runs efficiently on both CPUs and GPUs. Unlike previous DSLs, Ebb uses a three-layer architecture to separate (1) simulation code, (2) definition of data structures for geometric domains, and (3) runtimes supporting parallel architectures. Different geometric domains are implemented as libraries that use a common, unified, relational data model. By structuring the simulation framework in this way, programmers implementing simulations can focus on the physics and algorithms for each simulation without worrying about their implementation on parallel computers. Because the geometric domain libraries are all implemented using a common runtime based on relations, new geometric domains can be added as needed, without specifying the details of memory management, mapping to different parallel architectures, or having to expand the runtime’s interface.

    We evaluate Ebb by comparing it to several widely used simulations, demonstrating comparable performance to handwritten GPU code where available, and surpassing existing CPU performance optimizations by up to 9 × when no GPU code exists.

References:


    1. Zachary DeVito, James Hegarty, Alex Aiken, Pat Hanrahan, and Jan Vitek. 2013. Terra: A multi-stage language for high-performance computing. In ACM SIGPLAN Notices, Vol. 48. ACM, 105–116. Google ScholarDigital Library
    2. Zachary DeVito, Niels Joubert, Francisco Palacios, Stephen Oakley, Montserrat Medina, Mike Barrientos, et al. 2011. Liszt: A domain specific language for building portable mesh-based PDE solvers. In Proceedings of the 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC’11). ACM, New York, NY, Article 9, 12 pages. DOI:http://dx.doi.org/10.1145/2063384.2063396 Google ScholarDigital Library
    3. Pradeep Dubey, Pat Hanrahan, Ronald Fedkiw, Michael Lentine, and Craig Schroeder. 2011. PhysBAM: Physically based simulation. In ACM SIGGRAPH 2011 Courses. ACM, 10. Google ScholarDigital Library
    4. Tim Foley and Pat Hanrahan. 2011. Spark: Modular, composable shaders for graphics hardware. In ACM SIGGRAPH 2011 Papers (SIGGRAPH’11). ACM, New York, NY, Article 107, 12 pages. Google ScholarDigital Library
    5. Nolan Goodnight. 2007. CUDA/OpenGL fluid simulation. NVIDIA Corporation (2007).Google Scholar
    6. Pat Hanrahan and Jim Lawson. 1990. A language for shading and lighting calculations. In Proceedings of the 17th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH’90). ACM, New York, NY, 289–298. Google ScholarDigital Library
    7. Peter Hawkins, Alex Aiken, Kathleen Fisher, Martin Rinard, and Mooly Sagiv. 2011. Data Representation Synthesis. Vol. 46. ACM. Google ScholarDigital Library
    8. Frédéric Hecht. 2012. New development in FreeFem++. J. Numer. Math. 20, 3–4 (2012), 251–265.Google ScholarCross Ref
    9. James Hegarty, John Brunhaver, Zachary DeVito, Jonathan Ragan-Kelley, Noy Cohen, Steven Bell, Artem Vasilyev, Mark Horowitz, and Pat Hanrahan. 2014. Darkroom: Compiling high-level image processing code into hardware pipelines. ACM Trans. Graph. 33, 4, Article 144 (July 2014), 11 pages. Google ScholarDigital Library
    10. Roberto Ierusalimschy, Luiz Henrique De Figueiredo, and Waldemar Celes. 2011. Passing a language through the eye of a needle. Commun. ACM 54, 7 (2011), 38–43. Google ScholarDigital Library
    11. Ian Karlin, Abhinav Bhatele, Bradford L. Chamberlain, Jonathan Cohen, Zachary Devito, Maya Gokhale, et al. 2012. LULESH Programming Model and Performance Ports Overview. Technical Report LLNL-TR-608824. 1–17.Google Scholar
    12. Ian Karlin, Abhinav Bhatele, Jeff Keasler, Bradford L. Chamberlain, Jonathan Cohen, Zachary DeVito, et al. 2013. Exploring traditional and emerging parallel programming models using a proxy application. In 27th IEEE International Parallel & Distributed Processing Symposium (IEEE IPDPS 2013). Google ScholarDigital Library
    13. Andrey Kuzmin, Mathieu Luisier, and Olaf Schenk. 2013. Fast methods for computing selected elements of the Greens function in massively parallel nanoelectronic device simulations. In Euro-Par 2013 Parallel Processing, F. Wolf, B. Mohr, and D. Mey (Eds.). Lecture Notes in Computer Science, Vol. 8097. Springer, Berlin, 533–544. DOI:http://dx.doi.org/10.1007/978-3-642-40047-6_54 Google ScholarDigital Library
    14. Edward A. Luke. 1999. Loci: A deductive framework for graph-based algorithms. In Computing in Object-Oriented Parallel Environments. Springer, 142–153. Google ScholarDigital Library
    15. Edward A. Luke and Thomas George. 2005. Loci: A rule-based framework for parallel multi-disciplinary simulation synthesis. J. Funct. Program. 15, 3 (May 2005), 477–502. Google ScholarDigital Library
    16. LUL. 2012. Hydrodynamics Challenge Problem, Lawrence Livermore National Laboratory. Technical Report LLNL-TR-490254. 1–17.Google Scholar
    17. Miles Macklin, Matthias Müller, Nuttapong Chentanez, and Tae-Yong Kim. 2014. Unified particle physics for real-time applications. ACM Trans. Graph. 33, 4 (2014), 104. Google ScholarDigital Library
    18. Gihan R. Mudalige, Mike B. Giles, Jeyarajan Thiyagalingam, István Z. Reguly, Carlo Bertolli, Paul H. J. Kelly, and Anne E. Trefethen. 2013. Design and initial performance of a high-level unstructured mesh framework on heterogeneous parallel systems. Parallel Comput. 39, 11 (2013), 669–692. DOI:http://dx.doi.org/10.1016/j.parco.2013.09.004 Google ScholarDigital Library
    19. Kekoa Proudfoot, William R. Mark, Svetoslav Tzvetkov, and Pat Hanrahan. 2001. A real-time procedural shading system for programmable graphics hardware. In Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH’01). ACM, New York, NY, 159–170. Google ScholarDigital Library
    20. Jonathan Ragan-Kelley, Andrew Adams, Sylvain Paris, Marc Levoy, Saman P. Amarasinghe, and Frédo Durand. 2012. Decoupling algorithms from schedules for easy optimization of image processing pipelines. ACM Trans. Graph. 31, 4 (2012), 32. Google ScholarDigital Library
    21. Fun Shing Sin, Daniel Schroeder, and Jernej Barbič. 2013. Vega: Non-linear FEM deformable object simulator. Comput. Graph. Forum 32, 1 (2013), 36–48. DOI:http://dx.doi.org/10.1111/j.1467-8659.2012.03230.xGoogle ScholarCross Ref
    22. Jos Stam. 1999. Stable fluids. In Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques. ACM Press/Addison-Wesley Publishing Co., 121–128. Google ScholarDigital Library
    23. Jos Stam. 2009. Nucleus: Towards a unified dynamics solver for computer graphics. In Proceedings of the 11th IEEE International Conference on Computer-Aided Design and Computer Graphics (CAD/Graphics’09). IEEE, 1–11.Google ScholarCross Ref
    24. Nicholas Wilt. 2013. The Cuda Handbook: A Comprehensive Guide to GPU Programming. Pearson Education.Google Scholar

ACM Digital Library Publication:



Overview Page: