Charles M. Fiduccia

Bio: Charles M. Fiduccia is an academic researcher from General Electric. The author has contributed to research in topics: Router & Parallel processing (DSP implementation). The author has an hindex of 7, co-authored 8 publications receiving 3027 citations. Previous affiliations of Charles M. Fiduccia include Massachusetts Institute of Technology.

A Linear-Time Heuristic for Improving Network Partitions

Abstract: An iterative mincut heuristic for partitioning networks is presented whose worst case computation time, per pass, grows linearly with the size of the network. In practice, only a very small number of passes are typically needed, leading to a fast approximation algorithm for mincut partitioning. To deal with cells of various sizes, the algorithm progresses by moving one cell at a time between the blocks of the partition while maintaining a desired balance based on the size of the blocks rather than the number of cells per block. Efficient data structures are used to avoid unnecessary searching for the best cell to move and to minimize unnecessary updating of cells affected by each move.

A "Greedy" Channel Router

Abstract: We present a new, "greedy", channel-router that is quick, simple, and highly effective. It always succeeds, usually using no more than one track more than required by channel density. (It may be forced in rare cases to make a few connections "off the end" of the channel, in order to succeed.) It assumes that all pins and wiring lie on a common grid, and that vertical wires are on one layer, horizontal on another. The greedy router wires up the channel in a left-to-right, column-by-column manner, wiring each column completely before starting the next. Within each column the router tries to maximize the utility of the wiring produced, using simple, "greedy" heuristics. It may place a net on more than one track for a few columns, and "collapse" the net to a single track later on, using a vertical jog. It may also use a jog to move a net to a track closer to its pin in some future column. The router may occasionally add a new track to the channel, to avoid "getting stuck".

A “GGreedy” channel router

Abstract: We present a new, "greedy", channel-router that is quick, simple, and highly effective. It always succeeds, usually using no more than one track more than required by channel density. (It may be forced in rare cases to make a few connections "off the end" of the channel, in order to succeed.) It assumes that all pins and wiring lie on a common grid, and that vertical wires are on one layer, horizontal on another. The greedy router wires up the channel in a left-to-right, column-by-column manner, wiring each column completely before starting the next. Within each column the router tries to maximize the utility of the wiring produced, using simple, "greedy" heuristics. It may place a net on more than one track for a few columns, and "collapse" the net to a single track later on, using a vertical jog. It may also use a jog to move a net to a track closer to its pin in some future column. The router may occasionally add a new track to the channel, to avoid "getting stuck".

Local interconnection scheme for parallel processing architectures

Abstract: Method and apparatus for interconnecting the processing cells of a parallel processing machine configured in a two-dimensional rectangular array in which each cell includes a plurality of ports, each port having a unique port address, and a plurality of cells have similarly addressed ports. The cells are interconnected, via the cell ports, to form cell clusters having a central cell and eight neighboring cells such that a plurality of neighboring cells share a common connection to the central cell and further such that no two similarly addressed ports are coupled to one another. During a data transfer operation, in accordance with the single instruction multiple data (SIMD) format, each cell transmits data from one port and receives data from another port such that all cells transmit data from similarly addressed ports and receive data at similarly addressed ports to provide data transfer throughout the array in a uniform direction.

An efficient formula for linear recurrences

Abstract: The solutions to a scalar, homogeneous, constant-coefficient, linear recurrence are expressible in terms of the powers of a companion matrix. We show how to compute these powers efficiently via polynomial multiplication. The result is a simple expression for the solution, which does not involve the characteristic roots and which is valid for any module over any commutative ring. The formula yields the nth term of the solution to a kth order recurrence with $O(\mu (k) \cdot \log n)$ arithmetic operations, where $\mu (k)$ is the total number of arithmetic operations required to multiply two polynomials of degree $k - 1$. Thus if the ring supports a fast Fourier transform, then $O(k \cdot \log k \cdot \log n)$ operations are sufficient to compute the nth term.

A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs

Abstract: Recently, a number of researchers have investigated a class of graph partitioning algorithms that reduce the size of the graph by collapsing vertices and edges, partition the smaller graph, and then uncoarsen it to construct a partition for the original graph [Bui and Jones, Proc. of the 6th SIAM Conference on Parallel Processing for Scientific Computing, 1993, 445--452; Hendrickson and Leland, A Multilevel Algorithm for Partitioning Graphs, Tech. report SAND 93-1301, Sandia National Laboratories, Albuquerque, NM, 1993]. From the early work it was clear that multilevel techniques held great promise; however, it was not known if they can be made to consistently produce high quality partitions for graphs arising in a wide range of application domains. We investigate the effectiveness of many different choices for all three phases: coarsening, partition of the coarsest graph, and refinement. In particular, we present a new coarsening heuristic (called heavy-edge heuristic) for which the size of the partition of the coarse graph is within a small factor of the size of the final partition obtained after multilevel refinement. We also present a much faster variation of the Kernighan--Lin (KL) algorithm for refining during uncoarsening. We test our scheme on a large number of graphs arising in various domains including finite element methods, linear programming, VLSI, and transportation. Our experiments show that our scheme produces partitions that are consistently better than those produced by spectral partitioning schemes in substantially smaller time. Also, when our scheme is used to compute fill-reducing orderings for sparse matrices, it produces orderings that have substantially smaller fill than the widely used multiple minimum degree algorithm.

Co-clustering documents and words using bipartite spectral graph partitioning

Abstract: Both document clustering and word clustering are well studied problems. Most existing algorithms cluster documents and words separately but not simultaneously. In this paper we present the novel idea of modeling the document collection as a bipartite graph between documents and words, using which the simultaneous clustering problem can be posed as a bipartite graph partitioning problem. To solve the partitioning problem, we use a new spectral co-clustering algorithm that uses the second left and right singular vectors of an appropriately scaled word-document matrix to yield good bipartitionings. The spectral algorithm enjoys some optimality properties; it can be shown that the singular vectors solve a real relaxation to the NP-complete graph bipartitioning problem. We present experimental results to verify that the resulting co-clustering algorithm works well in practice.

Partitioning sparse matrices with eigenvectors of graphs

Abstract: The problem of computing a small vertex separator in a graph arises in the context of computing a good ordering for the parallel factorization of sparse, symmetric matrices. An algebraic approach for computing vertex separators is considered in this paper. It is, shown that lower bounds on separator sizes can be obtained in terms of the eigenvalues of the Laplacian matrix associated with a graph. The Laplacian eigenvectors of grid graphs can be computed from Kronecker products involving the eigenvectors of path graphs, and these eigenvectors can be used to compute good separators in grid graphs. A heuristic algorithm is designed to compute a vertex separator in a general graph by first computing an edge separator in the graph from an eigenvector of the Laplacian matrix, and then using a maximum matching in a subgraph to compute the vertex separator. Results on the quality of the separators computed by the spectral algorithm are presented, and these are compared with separators obtained from other algorith...

Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters

Abstract: A large body of work has been devoted to defining and identifying clusters or communities in social and information networks, i.e., in graphs in which the nodes represent underlying social entities and the edges represent some sort of interaction between pairs of nodes. Most such research begins with the premise that a community or a cluster should be thought of as a set of nodes that has more and/or better connections between its members than to the remainder of the network. In this paper, we explore from a novel perspective several questions related to identifying meaningful communities in large social and information networks, and we come to several striking conclusions. Rather than defining a procedure to extract sets of nodes from a graph and then attempting to interpret these sets as "real" communities, we employ approximation algorithms for the graph-partitioning problem to characterize as a function of size the statistical and structural properties of partitions of graphs that could plausibly be i...