Abstract
In this paper we prove lower bounds on the computational complexity of symmetric functions on parallel machines. The parallel models considered are PRIORITY and ARBITRARY, both widely used versions of the Parallel Random Access Machine (PRAM). We first consider PRIORITY. We prove that a PRIORITY PRAM with one shared memory cell and O(n) processors requires Ω(g(n)) time in order to decide if a binary vector of length n has at least g(n) 1's, for the case g(n)=o(n 1/4). This is the decision problem for the threshold language L g. Our lower bound is tight. For PRIORITY with m shared memory cells we prove an Ω(g(n)/m) lower bound. The limitation on the number of processors is essential, since with enough processors L g can be decided by the same model in O(√g(n)) time. The limitation on g is important: for instance, for g(n)=n/2 L g can be decided in time O(√g(n)), which is optimal [VW].
Our new technique for threshold languages enables us to show that for a large range of number of processors, PRIORITY and ARBITRARY with one shared memory cell require the same time complexity for computing large classes of symmetric functions. These results are of interest since PRIORITY is known to be more powerful than ARBITRARY (see [FMRW], for instance).
Next we show that a probabilistic PRIORITY with m≤n ∈,(0≤∈<1) shared memory cells and any finite number of processors requires expected time more than 1−ε / 4 logn to compute PARITY of n bits. (Note that with enough processors and shared memory PRIORITY can compute PARITY in constant time even deterministically). For probabilistic PRIORITY without ROM we prove a tight Ω(n/m) lower bound. The probabilistic case is much more complicated than the deterministic case, hence we develop new techniques for probabilistic lower bounds.
This work was supported in part by the National Science Foundation under Grant No. DCR-8606366, and by the Office of Research and Graduate Studies of the Ohio State University. First Author's current address: Aiken Computation Lab., Harvard University, Cambridge, MA 02138.
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6. References
L. Adleman, Two theorems on random polynomial time, Proc. 19th IEEE Symposium on Foundations of Computer Science (1978), pp. 75–83.
B. Awerbuch and Y. Shiloach, New connectivity and MSF algorithms for ultracomputers and PRAM, Proc. IEEE conf. on parallel proc., 1983, 175–179.
P. Beame, Limits on the power of concurrent-write parallel machines, 18th ACM Symp. on Theory of Computing, Berkeley, 1986, pp. 169–176.
S.A Cook and C. Dwork, Bounds on the time for parallel RAM's to compute simple functions, Proc. 14th ACM Symp. on Theory of Computing, 1982, pp 231–233.
F. Fich, F. Meyer auf der Heide, P. Ragde, and A Wigderson, One, two, three, ..., infinity: lower bounds for parallel computation, 24th ACM Symp. on Theory of Computing, 1985, pp 48–58.
F. Fich, P. Ragde, and A. Wigderson, Relations between concurrent-write models of parallel computation, Proc. 3rd ACM Symp. on Principles of Distributed computing, 1984, pp 179–184.
Z. Galil, Optimal parallel algorithms for string matching, STOC'84, p.240.
A. Gottlieb, R. Grishman, C. Kruskal, K. McAuliffe, L. Rudolf and M. Snir, The NYU Ultracomputer — Designing a MIMD shared memory parallel machine, IEEE Trans. Comput. C-32 (1983), pp 175–189.
D. Hirschberg, A. Chandra, and D. Sarwate, Computing connected components on parallel computers, CACM 22,8, 1979, pp. 461–464.
D. Kuck, A survey of parallel machine organization and programming, Computing Surveys, 9 (1977) pp29–52.
R.M. Karp, E. Upfal and A. Wigderson, Constructing a perfect matching is in random NC, Proc. 17th ACM STOC, 1985.
M. Li and Y. Yesha, Separation and lower bounds for ROM and nondeterministic models of parallel computation, Ohio State University, CISRC-TR-86-7, (To appear in Information and Control) 1985.
M. Li and Y. Yesha, New lower bounds for parallel computation, 18th ACM Ann. Symp. on Theory of Computing, Berkeley, 1986, pp. 177–187.
F. Meyer auf der Heide and A. Wigderson, The complexity of parallel sorting, 17th ACM Symp. on Theory of computing, 1985, pp. 532–540.
F.P. Preparata, New parallel-sorting schemes, IEEE Trans. Comp. c-27. p.669
Y. Shiloach and U. Vishkin, Finding the maximum, merging and sorting on parallel models of computation, J. of Algorithms 2, 1981, pp. 88–102.
Y. Shiloach and U. Vishkin, An O(logn) parallel connectivity algorithm, J. of Algorithms 3, 1982, pp. 57–67.
U. Vishkin, Parallel-design space distributed — implementation space (PDDI) general purpose computer, RC 9541, IBM T.J. Watson Research Center, Yorktown Heights, NY.
U. Vishkin and A Wigderson, Trade-offs between depth and width in parallel computation, SIAM J. Computing, Vol. 14, No 2, May 1985, pp 303–314.
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Li, M., Yesha, Y. (1987). The probabilistic and deterministic parallel complexity of symmetric functions. In: Ottmann, T. (eds) Automata, Languages and Programming. ICALP 1987. Lecture Notes in Computer Science, vol 267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-18088-5_27
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DOI: https://doi.org/10.1007/3-540-18088-5_27
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