11 research outputs found
Parallel algorithms for normalization
Given a reduced affine algebra A over a perfect field K, we present parallel
algorithms to compute the normalization \bar{A} of A. Our starting point is the
algorithm of Greuel, Laplagne, and Seelisch, which is an improvement of de
Jong's algorithm. First, we propose to stratify the singular locus Sing(A) in a
way which is compatible with normalization, apply a local version of the
normalization algorithm at each stratum, and find \bar{A} by putting the local
results together. Second, in the case where K = Q is the field of rationals, we
propose modular versions of the global and local-to-global algorithms. We have
implemented our algorithms in the computer algebra system SINGULAR and compare
their performance with that of the algorithm of Greuel, Laplagne, and Seelisch.
In the case where K = Q, we also discuss the use of modular computations of
Groebner bases, radicals, and primary decompositions. We point out that in most
examples, the new algorithms outperform the algorithm of Greuel, Laplagne, and
Seelisch by far, even if we do not run them in parallel.Comment: 19 page
Towards Massively Parallel Computations in Algebraic Geometry
Introducing parallelism and exploring its use is still a fundamental challenge for the computer algebra community. In high-performance numerical simulation, on the other hand, transparent environments for distributed computing which follow the principle of separating coordination and computation have been a success story for many years. In this paper, we explore the potential of using this principle in the context of computer algebra. More precisely, we combine two well-established systems: The mathematics we are interested in is implemented in the computer algebra system Singular, whose focus is on polynomial computations, while the coordination is left to the workflow management system GPI-Space, which relies on Petri nets as its mathematical modeling language and has been successfully used for coordinating the parallel execution (autoparallelization) of academic codes as well as for commercial software in application areas such as seismic data processing. The result of our efforts is a major step towards a framework for massively parallel computations in the application areas of Singular, specifically in commutative algebra and algebraic geometry. As a first test case for this framework, we have modeled and implemented a hybrid smoothness test for algebraic varieties which combines ideas from Hironakaâs celebrated desingularization proof with the classical Jacobian criterion. Applying our implementation to two examples originating from current research in algebraic geometry, one of which cannot be handled by other means, we illustrate the behavior of the smoothness test within our framework and investigate how the computations scale up to 256 cores
IBP reduction coefficients made simple
We present an efficient method to shorten the analytic integration-by-parts
(IBP) reduction coefficients of multi-loop Feynman integrals. For our approach,
we develop an improved version of Leinartas' multivariate partial fraction
algorithm, and provide a modern implementation based on the computer algebra
system Singular. Furthermore, We observe that for an integral basis with
uniform transcendental (UT) weights, the denominators of IBP reduction
coefficients with respect to the UT basis are either symbol letters or
polynomials purely in the spacetime dimension . With a UT basis, the partial
fraction algorithm is more efficient both with respect to its performance and
the size reduction. We show that in complicated examples with existence of a UT
basis, the IBP reduction coefficients size can be reduced by a factor of as
large as . We observe that our algorithm also works well for settings
without a UT basis.Comment: minor changes, typos correcte