Affiliation:
1. Simon Fraser University, Burnaby B.C., Canada
Abstract
We demonstrate how a new data structure for sparse distributed polynomials in the Maple kernel significantly accelerates a large subset of Maple library routines. The POLY data structure and its associated kernel operations (degree, coeff, subs, has, diff, eval, ...) are programmed for high scalability, allowing polynomials to have hundreds of millions of terms, and very low overhead, increasing parallel speedup in existing routines and improving the performance of high level Maple library routines.
Publisher
Association for Computing Machinery (ACM)
Cited by
4 articles.
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1. New Bounds on Quotient Polynomials with Applications to Exact Division and Divisibility Testing of Sparse Polynomials;Proceedings of the 2024 International Symposium on Symbolic and Algebraic Computation;2024-07-16
2. What Can (and Can't) we Do with Sparse Polynomials?;Proceedings of the 2018 ACM International Symposium on Symbolic and Algebraic Computation;2018-07-11
3. Output-Sensitive Algorithms for Sumset and Sparse Polynomial Multiplication;Proceedings of the 2015 ACM on International Symposium on Symbolic and Algebraic Computation;2015-06-24
4. The Design of Maple's Sum-of-Products and POLY Data Structures for Representing Mathematical Objects;ACM Communications in Computer Algebra;2015-02-05