Model counting for complex data structures

We extend recent approaches for calculating the probability of program behaviors, to allow model counting for complex data structures with numeric fields. We use symbolic execution with lazy initialization to compute the input structures leading to the occurrence of a target event, while keeping a symbolic representation of the constraints on the numeric data. Off-the-shelf model counting tools are used to count the solutions for numerical constraints and field bounds encoding data structure invariants are used to reduce the search space. The technique is implemented in the Symbolic PathFinder tool and evaluated on several complex data structures. Results show that the technique is much faster than an enumeration-based method that uses the Korat tool and also highlight the benefits of using the field bounds to speed up the analysis

An Efficient Algorithm for the Analysis of Cyclic Circuits

Compiling high-level hardware languages can produce circuits containing combinational cycles that can never be sensitized. Such circuits do have well-defined functional behavior, but wreak havoc with most logic synthesis and timing tools, which assume acyclic combinational logic. As such, some sort of cycle-removal step is usually necessary for handling these circuits. We present an algorithm able to quickly and exactly characterize all combinational behavior of a cyclic circuit. It iteratively examines the boundary between gates whose outputs are and are not defined and works backward to find additional input patterns that make the circuit behave combinationally. It produces a minimal set of sets of assignments to inputs that together cover all combinational behavior. This can be used to restructure the circuit into an acyclic equivalent, report errors, or as an optimization aid. Experiments show our algorithm runs several orders of magnitude faster than existing ones on real-life cyclic circuits, making it useful in practice

Optimizing Subgraph Queries by Combining Binary and Worst-Case Optimal Joins

We study the problem of optimizing subgraph queries using the new worst-case optimal join plans. Worst-case optimal plans evaluate queries by matching one query vertex at a time using multiway intersections. The core problem in optimizing worst-case optimal plans is to pick an ordering of the query vertices to match. We design a cost-based optimizer that (i) picks efficient query vertex orderings for worst-case optimal plans; and (ii) generates hybrid plans that mix traditional binary joins with worst-case optimal style multiway intersections. Our cost metric combines the cost of binary joins with a new cost metric called intersection-cost. The plan space of our optimizer contains plans that are not in the plan spaces based on tree decompositions from prior work. In addition to our optimizer, we describe an adaptive technique that changes the orderings of the worst-case optimal sub-plans during query execution. We demonstrate the effectiveness of the plans our optimizer picks and adaptive technique through extensive experiments. Our optimizer is integrated into the Graphflow DBMS

An Algorithm for Computation of Bond Contributions of the Wiener Index

The Wiener index Wis usually obtained by adding distances between all pairs of vertices i and j (i, j = 1,2, ...N, where N denotes the total number of vertices). The Wiener index may also he obtained by adding all bond contributions We, i.e. W = L We, and We = L C\u27fj / Cij, where cij denotes the number of all the shortest paths between i and j that include edge e, and Cu denotes the total number of the shortest paths between vertices i and j. The summation has to be performed for all edges e and for all pairs of indices i and i. respectively. It is easy to calculate the bond contributions We for a cyclic graphs or for bridges. The present paper introduces an algorithm, which can be used to obtain bond contributions in cycle - containing graphs

Transforming Cyclic Circuits Into Acyclic Equivalents

Designers and high-level synthesis tools can introduce unwanted cycles in digital circuits, and for certain combinational functions, cyclic circuits that are stable and do not hold state are the smallest or most natural representations. Cyclic combinational circuits have well-defined functional behavior yet wreak havoc with most logic synthesis and timing tools, which require combinational logic to be acyclic. As such, some sort of cycle-removal step is necessary to handle these circuits with existing tools. We present a two-stage algorithm for transforming a combinational cyclic circuit into an equivalent acyclic circuit. The first part quickly and exactly characterizes all combinational behavior of a cyclic circuit. It starts by applying input patterns to each input and examining the boundary between gates whose outputs are and are not defined to find additional input patterns that make the circuit behave combinationally. It produces sets of assignments to inputs that together cover all combinational behavior. This can be used to report errors, as an optimization aid, or to restructure the circuit into an acyclic equivalent. The second stage of our algorithm does this restructuring by creating an acyclic circuit fragment from each of these assignments and assembles these fragments into an acyclic circuit that reproduces all the combinational behavior of the original cyclic circuit. Experiments show that our algorithm runs in seconds on real-life cyclic circuits, making it useful in practice