This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Exploiting Domain-Specific Properties: Compiling Parallel Dynamic Neural Network Algorithms into Efficient Code
November 1999 (vol. 10 no. 11)
pp. 1105-1117

Abstract—Domain-specific constraints can be exploited to implement compiler optimizations that are not otherwise feasible. Compilers for neural network learning algorithms can achieve near-optimal colocality of data and processes and near-optimal balancing of load over processors, even for dynamically irregular problems. This is impossible for general programs, but restricting programs to the neural algorithm domain allows for the exploitation of domain-specific properties. The operations performed by neural algorithms are broadcasts, reductions, and object-local operations only; the load distribution is regular with respect to the (perhaps irregular) network topology; changes of network topology occur only from time to time. A language, compilation techniques, and a compiler implementation on the MasPar MP-1 are described and quantitative results for the effects of various optimizations used in the compiler are shown. Conservative experiments with weight pruning algorithms yield performance improvements of 27 percent due to load balancing and 195 percent improvement is achieved due to data locality, both compared to unoptimized versions. Two other optimizations—connection allocation and selecting the number of replicates—speed programs up by about 50 percent and 100 percent, respectively. This work can be viewed as a case study in exploiting domain-specific information; some of the principles presented here may apply to other domains as well.

[1] Neurocomputing: Foundations of Research, J.A. Anderson and E. Rosenfeld, eds. Cambridge, Mass.: MIT Press, 1988.
[2] G.E. Blelloch, S. Chatterjee, J. Hardwick, J. Sipelstein, and M. Zagha, “Implementation of a Portable Nested Data-Parallel Language,” Proc. Fourth ACM SIGPLAN Symp. Principles and Practice of Parallel Programming, 1993.
[3] S. Chatterjee, J.R. Gilbert, R. Schreiber, and S.H. Teng, "Automatic Array Alignment in Data-Parallel Programs," Proc. ACM SIGACT/ SIGPLAN Symp. Principles of Programming Languages,Charleston, S.C., Jan. 1993.
[4] W. Finnoff, F. Hergert, and H.G. Zimmermann, “Improving Model Selection by Nonconvergent Methods,” Neural Networks, vol. 6, pp. 771–783, 1993.
[5] S.F. Hummel, E. Schonberg, and L.E. Flynn, “Factoring: A Method for Scheduling Parallel Loops,” Comm. ACM, vol. 35, no. 8, pp. 90-101, Aug. 1992.
[6] B. Gomes, “A Framework for Mapping Connectionist Networks onto Parallel Machines,” PhD thesis, Electrical Engineering and Computer Science Dept., Univ. of California, Berkeley, May 1997.
[7] K.A. Grajski, “Neurocomputing Using the MasPar MP-1,” Technical Report 90-010, MasPar Computers, Sunnyvale, Calif., 1990.
[8] R.W. Gray, V.P. Heuring, S.P. Levi, A.M. Sloane, and W.M. Waite, “Eli: A Complete, Flexible Compiler Construction System,” Comm. ACM, vol. 35, no. 2, pp. 121–131, Feb. 1992.
[9] D. Hammerstrom, The CNAPS Architecture, Adaptive Solutions, Beaverton, Ore., Jan. 1993.
[10] B. Hendrickson and R. Leland, The Chaco User's Guide, version 1.0. UC-405 SAND93-2339, Sandia National Laboratories, Albuquerque, N.M., Oct. 1993.
[11] H. Hopp and L. Prechelt, “CuPit-2: A Portable Parallel Programming Language for Artificial Neural Networks,” Proc. 15th IMACS World Congress Scientific Computation, Modelling, and Applied Math., A. Sydow, ed., vol. 6, pp. 493–498, Berlin: Wissenschaft and Technik Verlag, Aug. 1997.
[12] C. Jacob and P. Wilke, “A Distributed Network Simulation Environment for Multi-Processing Systems,” Proc. Int'l Joint Conf. Neural Networks (IJCNN), pp. 1,178–1,183, Singapore, 1991.
[13] G. Kock and T. Becher, “Mind: An Environment for the Development, Integration, and Acceleration of Connectionist Systems,” Proc. 15th IMACS World Congress Scientific Computation, Modelling, and Applied Math., pp. 499–504, 1997.
[14] G. Kock and N.B. Serbedzija, “Artificial Neural Networks: From Compact Descriptions to C++,” Proc. Int'l Conf. Artificial Neural Networks, 1994.
[15] D. Koll, M. Riedmiller, and H. Braun, “Massively Parallel Training of Multi Layer Perceptrons with Irregular Topologies,” Proc. Int'l Conf. Artificial Neural Networks and Genetic Algorithms (ICANNGA), Ales, France, Springer Verlag, 1995.
[16] X. Liu and G.L. Wilcox, “Benchmarking of the CM-5 and the Cray Machines with a Very Large Backpropagation Neural Network, Technical Report 93/38, Univ. of Minnesota Supercomputer Inst., Apr. 1993.
[17] MPL Language Reference Manual. Sunnyvale, Calif.: MasPar Computers, 1990.
[18] W. McCulloch and W. Pitts, “A Logical Calculus of Ideas Immanent in Nervous Activity,” Bull. Math. Biophysics, vol. 5, pp. 115–133, 1943.
[19] M. Misra, “Parallel Environments for Implementing Neural Networks,” Neural Computing Surveys vol. 1, pp. 48–60, 1997.
[20] S. Müller and B. Gomes, “A Performance Analysis of CNS-1 on Sparse Connectionist Networks,” Technical Report TR-94-009, Int'l Computer Science Inst., Berkeley, Calif., Feb. 1994.
[21] M. Philippsen, “Automatic Alignment of Array Data and Processes to Reduce Communication Time on DMPPs,” Proc. Fifth ACM SIGPLAN Symp. Principles and Practice of Parallel Programming (PPoPP), pp. 156-165, July 1995.
[22] M. Philippsen, E.A. Heinz, and P. Lukowicz, “Compiling Machine-Independent Parallel Programs,” ACM SIGPLAN Notices, vol. 28, no. 8 pp. 99–108, Aug. 1993.
[23] L. Prechelt, “CuPit—A Parallel Language for Neural Algorithms: Language Reference and Tutorial,” Technical Report 4/94, Fakultät für Informatik, Univ., Karlsruhe, Germany, Jan. 1994, ftp://ftp.ira.uka.de/pub/papers/techreports/ 1994 1994-04.ps.gz.
[24] L. Prechelt, “PROBEN1—A Set of Benchmarks and Benchmarking Rules for Neural Network Training Algorithms,” Technical Report 21/94, Fakultät für Informatik, Univ. Karlsruhe, Germany, Sept. 1994, ftp://ftp.ira.uka.de/pub/papers/techreports/ 19941994-21.ps.gz.
[25] L. Prechelt, “The CuPitCompiler for the MasPar—A Literate Programming Document,” Technical Report 1/95, Fakultät für Informatik, Univ. Karlsruhe, Germany, Jan. 1995, ftp://ftp.ira.uka.de/pub/papers/techreports/ 19951995-01.ps.gz.
[26] L. Prechelt, “A Parallel Programming Model for Irregular Dynamic Neural Networks,” W.K. Giloi, S. Jähnichen, and B.D. Shriver, eds., Proc. Programming Models for Massively Parallel Computers, Berlin, Oct. 1995. GMD First, IEEE CS Press. By accident, the article wasnotprinted in the proceedings volume, but see.
[27] U. Ramacher, W. Raab, J. Anlauf, U. Hachmann, J. Beichter, N. Brüls, M. Weßeling, E. Sicheneder, J. Gläß, A. Wurz, and R. Männer, “Synapse-1: A High-Speed General Purpose Parallel Neurocomputer System,” Proc. Ninth Int'l Symp. Parallel Processing (IPPS '95), pp. 774–781, IEEE/CS Press, Los Alamitos, Calif., Apr. 1995.
[28] H. Braun and M. Riedmiller, "Direct Adaptive Method for Faster Backpropagation Learning: The RPropAlgorithm," Proc. IEEE Int'l Conf. Neural Networks (ICNN '93), IEEE, Piscataway, N.J., 1993, pp. 586-591
[29] “Application in Modelling and Simulation,” Proc. 15th IMACS World Congress on Scientific Computation, Modelling, and Applied Math., vol. 6.A. Sydow, ed., Berlin: Wissenschaft und Technik Verlag, Aug. 1997.
[30] T. Tollenaere and G.A. Orban, “Decomposition and Mapping of Locally Connected Layered Neural Networks on Message-Passing Multiprocessors,” Parallel Algorithms and Applications, vol. 1, pp. 43–56, 1993.
[31] X. Zhang, M. McKenna, J.P. Mesirov, and D. Waltz, “An Efficient Implementation of the Backpropagation Algorithm on the Connection Machine CM-2,” Advances in Neural Information Processing Systems 2, D. Touretzky, ed., pp. 801–809. San Mateo, Calif.: Morgan Kaufmann, 1989.

Index Terms:
Compiler optimizations, high-level parallel language, irregular problems, dynamic data structures, communication optimization.
Citation:
Lutz Prechelt, "Exploiting Domain-Specific Properties: Compiling Parallel Dynamic Neural Network Algorithms into Efficient Code," IEEE Transactions on Parallel and Distributed Systems, vol. 10, no. 11, pp. 1105-1117, Nov. 1999, doi:10.1109/71.809571
Usage of this product signifies your acceptance of the Terms of Use.