7,975 research outputs found
Fast and compact self-stabilizing verification, computation, and fault detection of an MST
This paper demonstrates the usefulness of distributed local verification of
proofs, as a tool for the design of self-stabilizing algorithms.In particular,
it introduces a somewhat generalized notion of distributed local proofs, and
utilizes it for improving the time complexity significantly, while maintaining
space optimality. As a result, we show that optimizing the memory size carries
at most a small cost in terms of time, in the context of Minimum Spanning Tree
(MST). That is, we present algorithms that are both time and space efficient
for both constructing an MST and for verifying it.This involves several parts
that may be considered contributions in themselves.First, we generalize the
notion of local proofs, trading off the time complexity for memory efficiency.
This adds a dimension to the study of distributed local proofs, which has been
gaining attention recently. Specifically, we design a (self-stabilizing) proof
labeling scheme which is memory optimal (i.e., bits per node), and
whose time complexity is in synchronous networks, or time in asynchronous ones, where is the maximum degree of
nodes. This answers an open problem posed by Awerbuch and Varghese (FOCS 1991).
We also show that time is necessary, even in synchronous
networks. Another property is that if faults occurred, then, within the
requireddetection time above, they are detected by some node in the locality of each of the faults.Second, we show how to enhance a known
transformer that makes input/output algorithms self-stabilizing. It now takes
as input an efficient construction algorithm and an efficient self-stabilizing
proof labeling scheme, and produces an efficient self-stabilizing algorithm.
When used for MST, the transformer produces a memory optimal self-stabilizing
algorithm, whose time complexity, namely, , is significantly better even
than that of previous algorithms. (The time complexity of previous MST
algorithms that used memory bits per node was , and
the time for optimal space algorithms was .) Inherited from our proof
labelling scheme, our self-stabilising MST construction algorithm also has the
following two properties: (1) if faults occur after the construction ended,
then they are detected by some nodes within time in synchronous
networks, or within time in asynchronous ones, and (2) if
faults occurred, then, within the required detection time above, they are
detected within the locality of each of the faults. We also show
how to improve the above two properties, at the expense of some increase in the
memory
Finding 3-edge-connected components in parallel
A parallel algorithm for finding 3-edge-connected components of an undirected graph on a CRCW PRAM is presented. The time and work complexity of this algorithm is O(logn) and O((m+n)loglogn), respectively, where n is the number of vertices and m is the number of edges in the input graph. The algorithm is based on ear decomposition and reduction of 3-edge-connectivity to 1-vertex-connectivity. This is the first 3-edge-connected component algorithm of a parallel model
2-Edge-Connectivity and 2-Vertex-Connectivity with Fault Containment
Self-stabilization for non-masking fault-tolerant distributed system has received considerable research interest over the last decade. In this paper, we propose a self-stabilizing algorithm for 2-edge-connectivity and 2-vertex-connectivity of an asynchronous distributed computer network. It is based on a self-stabilizing depth-first search, and is not a composite algorithm in the sense that it is not composed of a number of self-stabilizing algorithms that run concurrently. The time and space complexities of the algorithm are the same as those of the underlying self-stabilizing depth-first search algorithm
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Complex macrocycle exploration: parallel, heuristic, and constraint-based conformer generation using ForceGen.
ForceGen is a template-free, non-stochastic approach for 2D to 3D structure generation and conformational elaboration for small molecules, including both non-macrocycles and macrocycles. For conformational search of non-macrocycles, ForceGen is both faster and more accurate than the best of all tested methods on a very large, independently curated benchmark of 2859 PDB ligands. In this study, the primary results are on macrocycles, including results for 431 unique examples from four separate benchmarks. These include complex peptide and peptide-like cases that can form networks of internal hydrogen bonds. By making use of new physical movements ("flips" of near-linear sub-cycles and explicit formation of hydrogen bonds), ForceGen exhibited statistically significantly better performance for overall RMS deviation from experimental coordinates than all other approaches. The algorithmic approach offers natural parallelization across multiple computing-cores. On a modest multi-core workstation, for all but the most complex macrocycles, median wall-clock times were generally under a minute in fast search mode and under 2 min using thorough search. On the most complex cases (roughly cyclic decapeptides and larger) explicit exploration of likely hydrogen bonding networks yielded marked improvements, but with calculation times increasing to several minutes and in some cases to roughly an hour for fast search. In complex cases, utilization of NMR data to constrain conformational search produces accurate conformational ensembles representative of solution state macrocycle behavior. On macrocycles of typical complexity (up to 21 rotatable macrocyclic and exocyclic bonds), design-focused macrocycle optimization can be practically supported by computational chemistry at interactive time-scales, with conformational ensemble accuracy equaling what is seen with non-macrocyclic ligands. For more complex macrocycles, inclusion of sparse biophysical data is a helpful adjunct to computation
Near Linear Performance in an Articulated Body Solver with Implicit Elasticity
The dynamics of articulated rigid bodies can be solved in O(n) time using a recursive method. When elasticity is added between the bodies, with linearly implicit integration, the stiffness matrix in the equations of motion breaks the tree topology of the system, making the recursive method inapplicable. The only alternative has been to form and solve the system matrix, which takes O(n^ 3) time.
A new approach that can solve the linearly implicit equations of motion in near linear time, coined REDMAX, is built using a combined reduced/maximal coordinate formulation. This hybrid model enables direct flexibility to apply arbitrary combinations of forces in both reduced and maximal coordinates, while maintaining near linear performance in the number of bodies
Near Linear Performance in an Articulated Body Solver with Implicit Elasticity
The dynamics of articulated rigid bodies can be solved in O(n) time using a recursive method. When elasticity is added between the bodies, with linearly implicit integration, the stiffness matrix in the equations of motion breaks the tree topology of the system, making the recursive method inapplicable. The only alternative has been to form and solve the system matrix, which takes O(n^ 3) time.
A new approach that can solve the linearly implicit equations of motion in near linear time, coined REDMAX, is built using a combined reduced/maximal coordinate formulation. This hybrid model enables direct flexibility to apply arbitrary combinations of forces in both reduced and maximal coordinates, while maintaining near linear performance in the number of bodies
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