ISTCS 1992: Theory of Computing and Systems pp 147-153

Bounds on parallel computation of multivariate polynomials

• Amir Averbuch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 601)

Abstract

We consider the problem of fast parallel evaluation of multivariate polynomials over a field F. We define “maximal-degree” (maxdeg) of a multivariate polynomial f as maxii=1,..., n.

The first lower bound result states that if a circut G evaluates a multivariate polynomial f, where its nodes are capable of performing (+,*), then the depth(G) is not less than log2[maxdeg(f)]. This result is a generalization of Kung's[K] results for a univariate polynomial which is log2 [deg f].

In the second part, we consider the circuit G which evaluates an arbitrary polynomial f in n variables with maxdeg(f)≜dp>1.

We present two algorithms that achieve better performance than the classical results of Hyafil[H] and Valiant et al. [VSBR] for most classes of multivariate polynomials. For the class of ” dense ” polynomials the results are closed to the theorethical bound log C, where C is the sequential complexity.

The algorithms generalize Munro-Paterson[MP] method for the univariate case. It should be noticed that the bound obtained by Hyafil[H] and Valiant, Skyum, Berkowitz and Rackoff[VSBR] is not sufficiently tight for the worst-sequential case (dense multivariate polynomials) and their bound can be reduced by the factor of log d while the number of required processors is only O(C). The best improvement is achieved in a case of a ”dense” multivariate polynomial. A polynomial is dense if the computation necessitates Ω(d p n ) sequential steps.

The simple algorithm requires only n log dp+O(n√log dp) parallel steps The second algorithm has parallel complexity, measured by the depth of the circuit, depth(G)n(log dp+β)+log n+√log dp where β ≤ √log dp. If C=Ω(d p n ) then it is less than log C+√n · log C where C is the number of sequential steps, and it requires only O(C) processors.

The second algorithm is slightly better than the ” simple” one. The improvement is achieved when β is small. The improvement of both algorithms in the parallel complexity and the number of processors with respect to Valiant is significant for most classes of multivariate polynomials.

Key words

Complexity of parallel computation Multivariate polynomials Design and analysis of parallel algorithms Maximal-degree Dense polynomial

References

1. [BM]
Borodin, A., Munro, I.: The Computational Complexity of Algebraic and Numeric problems, American Elsevier Publishing Company, 1975.Google Scholar
2. [K]
Kung, H.: New Algorithm and Lower Bounds for the Parallel Evaluation of Certain Rational Expressions and Recurrences, J. ACM, vol. 23, no. 2 (1976) 252–261.Google Scholar
3. [H]
Hyafil, K.: On the Parallel Evaluation of Multivariate Polynomials, Proc., 10th ACM Symposium on Theory of Computing, (1978) pp. 193–195.Google Scholar
4. [VSBR]
Valiant, L.G., Skyum, S., Berkowitz, S., Rackoff, C.: Fast Parallel Computation of Polynomials using Few Processors, SIAM J. Comput., 12, No. 4, (1983) 641–644.Google Scholar
5. [MP]
Munro, I., Paterson, M.: Optimal Algorithms for Parallel Polynomial Evaluation, J. of Computer and System Science 7(1973) 189–198.Google Scholar