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Can Parallel Programming Revolutionize EDA Tools?

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Advanced Logic Synthesis

Abstract

Recent advances in parallel programming hold the promise of transforming the EDA tools area and changing the way we design, synthesize, and simulate circuits.

Traditional parallel programming technology was developed for computational science applications, which operate on dense and sparse matrices, and it is not useful for most EDA applications. However, in the past decade, there has been substantial progress in developing parallelization technology for graph applications. These advances were motivated primarily by the needs of large-scale parallel graph analytics, but this technology is useful for EDA applications since large graphs such as netlists are ubiquitous in this domain.

In this paper, we describe the Galois system, developed by our group at the University of Texas at Austin to make it easier to program large-scale graph applications and execute them in parallel. Algorithms from many problem domains including graph analytics, high-performance computing, and FPGA tools have been implemented successfully in Galois. These successes suggest that by working together, the EDA and parallel programming research communities can bring state-of-the-art parallel programming technology to bear on EDA tools, transforming this area.

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Notes

  1. 1.

    Sparse direct methods are an exception, but even in these algorithms, a dependence graph, known as the elimination tree, is built before the algorithm is executed in parallel [5].

  2. 2.

    A more detailed description of the implementation of the Galois system can be found in our previous papers such as [31].

References

  1. D.A. Bader, K. Madduri, Design and implementation of the HPCS graph analysis benchmark on symmetric multiprocessors, in High Performance Computing, HiPC’05 (2005)

    Google Scholar 

  2. U. Brandes, T. Erlebach (eds.), Network Analysis: Methodological Foundations (Springer, Heidelberg, 2005)

    MATH  Google Scholar 

  3. G. Bronevetsky, D. Marques, K. Pingali, P. Stodghill, C3: a system for automating application-level checkpointing of MPI programs. Languages and Compilers for Parallel Computing (Springer, New York, 2004), pp. 357–373

    Google Scholar 

  4. K.M. Chandy, J. Misra, The drinking philosophers problem. ACM Trans. Program. Lang. Syst. 6(4), 632–646 (1984)

    Article  Google Scholar 

  5. I.S. Duff, A.M. Erisman, J.K. Reid, Direct Methods for Sparse Matrices (Oxford University Press, New York, 1986). ISBN 0-198-53408-6

    MATH  Google Scholar 

  6. P. Feautrier, Some efficient solutions to the affine scheduling problem: one dimensional time. Int. J. Parallel Prog. 21(5), 313–347 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  7. J.E. Gonzalez, Y. Low, H. Gu, D. Bickson, C. Guestrin, Powergraph: distributed graph-parallel computation on natural graphs, in Proceedings of the 10th USENIX Conference on Operating Systems Design and Implementation, OSDI’12, Berkeley, CA, 2012, pp. 17–30. USENIX Association. ISBN 978-1-931971-96-6. http://dl.acm.org/citation.cfm?id=2387880.2387883

  8. M. Gort, J. Anderson, Deterministic multicore parallel routing for FPGAs, in International Conference on Field Programmable Technology (ICFPT’10) (2010)

    Google Scholar 

  9. M.A. Hassaan, M. Burtscher, K. Pingali, Ordered vs unordered: a comparison of parallelism and work-efficiency in irregular algorithms, in Proceedings of the 16th ACM symposium on Principles and Practice of Parallel Programming, PPoPP ’11 (ACM, New York, 2011), pp. 3–12. ISBN 978-1-4503-0119-0. doi:http://doi.acm.org/10.1145/1941553.1941557. http://iss.ices.utexas.edu/Publications/Papers/ppopp016s-hassaan.pdf

  10. IWLS, IWLS 2005 benchmarks. http://iwls.org/iwls2005/benchmarks.html (2005)

  11. J. JaJa, An Introduction to Parallel Algorithms (Addison-Wesley, Boston, 1992)

    MATH  Google Scholar 

  12. R.M. Karp, V. Ramachandran, A survey of parallel algorithms for shared-memory machines. Technical Report UCB/CSD-88-408, EECS Department, University of California, Berkeley (1988). http://www.eecs.berkeley.edu/Pubs/TechRpts/1988/5865.html

  13. K. Kennedy, J. Allen (eds.) Optimizing Compilers for Modern Architectures: A Dependence-Based Approach (Morgan Kaufmann, San Francisco, 2001)

    Google Scholar 

  14. I. Kodukula, N. Ahmed, K. Pingali, Data-centric multi-level blocking, in PLDI ’97: Proceedings of the ACM SIGPLAN 1997 Conference on Programming Language Design and Implementation (ACM, New York, 1997), pp. 346–357. ISBN 0-89791-907-6. doi:http://doi.acm.org/10.1145/258915.258946. http://iss.ices.utexas.edu/Publications/Papers/PLDI1997.pdf

  15. V. Kotlyar, K. Pingali, P. Stodghill, A relational approach to the compilation of sparse matrix programs, in Euro-Par ’97: Proceedings of the Third International Euro-Par Conference on Parallel Processing (Springer, London, 1997), pp. 318–327. ISBN 3-540-63440-1. http://iss.ices.utexas.edu/Publications/Papers/EUROPAR1997.pdf

    Book  MATH  Google Scholar 

  16. M. Kulkarni, D. Nguyen, D. Prountzos, X. Sui, K. Pingali, Exploiting the commutativity lattice, in PLDI (2011)

    Google Scholar 

  17. J. Lee, W.-S. Han, R. Kasperovics, J.-H. Lee, An in-depth comparison of subgraph isomorphism algorithms in graph databases. Proc. VLDB Endow. 6(2), 133–144 (2012). ISSN 2150-8097. doi:10.14778/2535568.2448946. http://dx.doi.org/10.14778/2535568.2448946

  18. A. Lenharth, K. Pingali, Scaling runtimes for irregular algorithms to large-scale NUMA systems. Computer 48(8), 35–44 (2015)

    Article  Google Scholar 

  19. A. Lenharth, D. Nguyen, K. Pingali, Priority queues are not good concurrent priority schedulers, in European Conference on Parallel Processing (Springer, Berlin/Heidelberg, 2015), pp. 209–221

    Google Scholar 

  20. A. Lenharth, D. Nguyen, K. Pingali, Parallel graph analytics. Commun. ACM 59(5), 78–87 (2016). ISSN 0001-0782. doi:10.1145/2901919. http://doi.acm.org/10.1145/2901919

  21. D.B. Loveman, High performance fortran. IEEE Parallel Distrib. Technol. Syst. Appl. 1(1), 25–42 (1993)

    Article  Google Scholar 

  22. Y. Low, J. Gonzalez, A. Kyrola, D. Bickson, C. Guestrin, J.M. Hellerstein, Graphlab: a new parallel framework for machine learning, in Conference on Uncertainty in Artificial Intelligence (UAI), July 2010

    Google Scholar 

  23. D. Mackay, Information Theory, Inference and Learning Algorithms (Cambridge University Press, Cambridge, 2003)

    MATH  Google Scholar 

  24. G. Malewicz, M.H. Austern, A.J. Bik, J.C. Dehnert, I. Horn, N. Leiser, G. Czajkowski, Pregel: a system for large-scale graph processing - “abstract”, in Proceedings of the 28th ACM Symposium on Principles of Distributed Computing, PODC ’09 (ACM, New York, 2009), pp. 6–6 ISBN 978-1-60558-396-9. doi:10.1145/1582716.1582723. http://doi.acm.org/10.1145/1582716.1582723

  25. M. Mendez-Lojo, A. Mathew, K. Pingali, Parallel inclusion-based points-to analysis, in Proceedings of the 24th Annual ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA’10), October 2010. http://iss.ices.utexas.edu/Publications/Papers/oopsla10-mendezlojo.pdf

  26. U. Meyer, P. Sanders, Delta-stepping: a parallel single source shortest path algorithm, in Proceedings of the 6th Annual European Symposium on Algorithms, ESA ’98 (Springer, London, 1998), pp. 393–404. ISBN 3-540-64848-8. http://dl.acm.org/citation.cfm?id=647908.740136

    Google Scholar 

  27. J. Misra, Distributed discrete-event simulation. ACM Comput. Surv. 18(1), 39–65 (1986), ISSN 0360-0300. doi:http://doi.acm.org/10.1145/6462.6485

    Google Scholar 

  28. Y.O.M. Moctar, P. Brisk, Parallel FPGA routing based on the operator formulation, in Proceedings of the 51st Annual Design Automation Conference, DAC ’14 (2014).

    Google Scholar 

  29. R. Nasre, M. Burtscher, K. Pingali, Data-driven versus topology-driven irregular computations on GPUs, in Proceedings of the 27th IEEE International Parallel and Distributed Processing Symposium, IPDPS ’13 (Springer, London, 2013)

    Google Scholar 

  30. D. Nguyen, K. Pingali, Synthesizing concurrent schedulers for irregular algorithms, in Proceedings of International Conference Architectural Support for Programming Languages and Operating Systems, ASPLOS ’11, pp. 333–344 (2011). ISBN 978-1-4503-0266-1. doi:10.1145/1950365.1950404. http://doi.acm.org/10.1145/1950365.1950404

  31. D. Nguyen, A. Lenharth, K. Pingali, A lightweight infrastructure for graph analytics, in Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles, SOSP ’13 (ACM, New York, 2013), pp. 456–471. ISBN 978-1-4503-2388-8. doi:10.1145/2517349.2522739. http://doi.acm.org/10.1145/2517349.2522739

    Book  Google Scholar 

  32. R.W. Numrich, J. Reid, Co-array fortran for parallel programming, in ACM Sigplan Fortran Forum, vol. 17 (ACM, New York, 1998), pp. 1–31

    Google Scholar 

  33. K. Pingali, D. Nguyen, M. Kulkarni, M. Burtscher, M.A. Hassaan, R. Kaleem, T.-H. Lee, A. Lenharth, R. Manevich, M. Méndez-Lojo, D. Prountzos, X. Sui, The TAO of parallelism in algorithms, in Proceedings of ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI ’11, pp. 12–25 (2011). ISBN 978-1-4503-0663-8. doi:10.1145/1993498.1993501. http://doi.acm.org/10.1145/1993498.1993501

  34. D. Prountzos, R. Manevich, K. Pingali, K.S. McKinley, A shape analysis for optimizing parallel graph programs, in Proceedings of the 38th Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, POPL ’11 (ACM, New York, 2011), pp. 159–172. ISBN 978-1-4503-0490-0. doi:http://doi.acm.org/10.1145/1926385.1926405. http://www.cs.utexas.edu/users/dprountz/popl2011.pdf

  35. J.R. Shewchuk, Triangle: engineering a 2D quality mesh generator and delaunay triangulator, in Applied Computational Geometry: Towards Geometric Engineering, ed. by M.C. Lin, D. Manocha. Lecture Notes in Computer Science, vol. 1148 (Springer, Berlin, 1996), pp. 203–222. From the First ACM Workshop on Applied Computational Geometry

    Google Scholar 

  36. J. Shun, G.E. Blelloch, Ligra: a lightweight graph processing framework for shared memory, in Proceedings of the 18th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP, pp 135–146 (2013)

    Book  Google Scholar 

  37. P.-N. Tan, M. Steinbach, V. Kumar (eds.), Introduction to Data Mining (Pearson Addison Wesley, Boston, 2005)

    Google Scholar 

  38. J.R. Ullmann, Bit-vector algorithms for binary constraint satisfaction and subgraph isomorphism. J. Exp. Algorithm. 15, 1.6:1.1–1.6:1.64 (2011) ISSN 1084-6654. doi:10.1145/1671970.1921702. http://doi.acm.org/10.1145/1671970.1921702

  39. L.G. Valiant, A bridging model for parallel computation. Commun. ACM 33(8), 103–111 (1990). ISSN 0001-0782. doi:http://doi.acm.org/10.1145/79173.79181

    Google Scholar 

  40. S.C. Woo, M. Ohara, E. Torrie, J.P. Singh, A. Gupta, The splash-2 programs: characterization and methodological considerations, in Proceedings of the 22nd Annual International Symposium on Computer Architecture, ISCA ’95 (ACM, New York, 1995), pp. 24–36. ISBN 0-89791-698-0. doi:10.1145/223982.223990. http://doi.acm.org/10.1145/223982.223990

    Google Scholar 

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Acknowledgements

This research was supported by NSF grants 1218568, 1337281, 1406355, and 1618425, and by DARPA contracts FA8750-16-2-0004 and FA8650-15-C-7563.

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  1. The University of Texas at Austin, Austin, TX, USA

    Yi-Shan Lu & Keshav Pingali

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  1. Yi-Shan Lu
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Correspondence to Yi-Shan Lu .

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  1. PPGC/PGMICRO, Institute of Informatics, UFRGS, Porto Alegre, Rio Grande do Sul, Brazil

    André Inácio Reis

  2. Group for Computer Architecture, University of Bremen, Bremen, Germany

    Rolf Drechsler

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Lu, YS., Pingali, K. (2018). Can Parallel Programming Revolutionize EDA Tools?. In: Reis, A., Drechsler, R. (eds) Advanced Logic Synthesis. Springer, Cham. https://doi.org/10.1007/978-3-319-67295-3_2

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  • DOI: https://doi.org/10.1007/978-3-319-67295-3_2

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