Abstract: A method for resource management includes associating respective variables with resource consumers, and identifying resources as values applicable to the variables. A group of the variables are identified as preferred variables. An assignment of the values to the variables that satisfies constraints applying to the allocation of the resources is found by repeatedly performing the steps of choosing a variable from the group, instantiating the chosen variable with a value, removing the chosen variable from the group, and pruning the domains of the other variables by propagation of the constraints. The resources are assigned to the resource consumers responsively to the assignment of the values to the variables.

Abstract: A computer-implemented method for solving a constraint satisfaction problem (CSP), which is defined by variables and constraints applicable to the variables, and which has states corresponding to respective sets of values of the variables. The method includes assigning cost functions to the constraints so that the states have respective costs determined by application of the cost functions to the respective sets of values of the variables, the respective costs defining a problem topography of the CSP having global extrema corresponding to solutions of the CSP. The constraints of the CSP are reformulated so as to perform at least one of increasing a density of the solutions in the problem topography and smoothing a gradient of the problem topography. One or more of the solutions of the CSP are found by applying a stochastic CSP solver to the reformulated constraints.

Abstract: A method for solving a constraint satisfaction problem (CSP) comprises identifying a first illegal combination associated with at least one variable, wherein the first illegal combination contradicts at least one constraint; grouping the first illegal combination with a second illegal combination, in response to determining that the first and second illegal combinations contradict the same constraint; identifying at least one failure explanation for each group of illegal combinations, wherein the failure explanation is associated with at least one illegal combination in said group to provide a reason for a contradicted constraint associated with the illegal combination; assigning a value to each variable in the given domain to solve the CSP within the limitations defined by the constraints in a solution path; and generating a result, in response to determining that at least one assigned value to a variable contradicts a constraint.

Abstract: A system and method are disclosed for allocating human resources to tasks using constraint satisfaction programming, where fictitious persons are used to satisfy required constraints, to ensure that the solution process continues until a solution is found, and capability constraints and job constraints are relaxed until a solution is found. Tasks using fictitious persons are identified, and information about task capability requirements not met and capabilities of unallocated human resources are displayed so that constraints may be relaxed and fictitious persons removed. There is provision for handling multi-task jobs where if a fictitious person is assigned to any task then all tasks will be assigned a fictitious person.

Abstract: A method for job selection and resource allocation of massively parallel processors, the method includes: providing to a constraint satisfaction problem solver multiple domains, variables, and constraints representative of a massively parallel processor, of queued job requests and of jobs being processed by the massively parallel processor, and generating, by the constraint satisfaction problem solver a result representative of multiple jobs to be executed, at least partially in parallel, by multiple resources of the massively parallel processor.

Abstract: Techniques are provided for modeling memory operations when generating test cases to verify multi-processor designs. Each memory operation has associated therewith a set of transfer attributes that can be referenced by a test generator. Using the transfer attributes, it is possible to generate a variety of interesting scenarios that handle read-write collisions and generally avoid reloading or resources. The model provides accurate result prediction, and allows write access restrictions to be removed from sensitive memory areas, such as control areas.

Abstract: Test generation is improved by learning the relationship between an initial state vector for a stimuli generator and generation success. A stimuli generator for a design-under-verification is provided with information about the success probabilities of potential assignments to an initial state bit vector. Selection of initial states according to the success probabilities ensures a higher success rate than would be achieved without this knowledge. The approach for obtaining an initial state bit vector employs a CSP solver. A learning system is directed to model the behavior of possible initial state assignments. The learning system develops the structure and parameters of a Bayesian network that describes the relation between the initial state and generation success.

Abstract: A method for solving a constraint satisfaction problem (CSP) comprises identifying a first illegal combination associated with at least one variable, wherein the first illegal combination contradicts at least one constraint; grouping the first illegal combination with a second illegal combination, in response to determining that the first and second illegal combinations contradict the same constraint; identifying at least one failure explanation for each group of illegal combinations, wherein the failure explanation is associated with at least one illegal combination in said group to provide a reason for a contradicted constraint associated with the illegal combination; assigning a value to each variable in the given domain to solve the CSP within the limitations defined by the constraints in a solution path; and generating a result, in response to determining that at least one assigned value to a variable contradicts a constraint.

Abstract: A system and method are disclosed for allocating human resources to tasks using constraint satisfaction programming, where fictitious persons are used to satisfy required constraints, to ensure that the solution process continues until a solution is found, and capability constraints and job constraints are relaxed until a solution is found. Tasks using fictitious persons are identified, and information about task capability requirements not met and capabilities of unallocated human resources are displayed so that constraints may be relaxed and fictitious persons removed. There is provision for handling multi-task jobs where if a fictitious person is assigned to any task then all tasks will be assigned a fictitious person.

Abstract: A method for solving a constraint satisfaction problem (CSP) includes choosing a first state corresponding to a first set of values of a set of variables, and selecting a hop distance within a state space of the variables responsively to a random distance selection criterion. A second state corresponding to a second set of the values of the variables is selected, such that the second state is separated from the first state by the hop distance. Constraint costs of the first and second states are compared. If the cost of the second state is lower than the cost of the first state, the first state is redefined to correspond to the second set of the values of the variables. These steps are repeated until a solution of the CSP is found.

Abstract: Test generation is improved by learning the relationship between an initial state vector for a stimuli generator and generation success. A stimuli generator for a design-under-verification is provided with information about the success probabilities of potential assignments to an initial state bit vector. Selection of initial states according to the success probabilities ensures a higher success rate than would be achieved without this knowledge. The approach for obtaining an initial state bit vector employs a CSP solver. A learning system is directed to model the behavior of possible initial state assignments. The learning system develops the structure and parameters of a Bayesian network that describes the relation between the initial state and generation success.

Abstract: A computer-implemented method for solving a constraint satisfaction problem (CSP), which is defined by variables and constraints applicable to the variables, and which has states corresponding to respective sets of values of the variables. The method includes assigning cost functions to the constraints so that the states have respective costs determined by application of the cost functions to the respective sets of values of the variables, the respective costs defining a problem topography of the CSP having global extrema corresponding to solutions of the CSP. The constraints of the CSP are reformulated so as to perform at least one of increasing a density of the solutions in the problem topography and smoothing a gradient of the problem topography. One or more of the solutions of the CSP are found by applying a stochastic CSP solver to the reformulated constraints.

Abstract: A test-program generator capable of implementing a methodology, based on a formal language, for scheduling system-level transactions in generated test programs. A system to be tested may be composed of multiple processors, busses, bus-bridges, shared memories, etc. The scheduling methodology is based on an exploration of scheduling abilities in a hardware system and features a Hierarchical Scheduling Language for specifying transactions and their ordering. Through a grouping hierarchy, which may also be expressed in the form of an equivalent tree, the Hierarchical Scheduling Language combines the ability to stress related logical areas of the system with the possibility of applying high-level scheduling requests. A method for generating testcases based on request-files written in the Hierarchical Scheduling Language is also presented.

Abstract: A method for solving a constraint satisfaction problem (CSP) includes choosing a first state corresponding to a first set of values of a set of variables, and selecting a hop distance within a state space of the variables responsively to a random distance selection criterion. A second state corresponding to a second set of the values of the variables is selected, such that the second state is separated from the first state by the hop distance. Constraint costs of the first and second states are compared. If the cost of the second state is lower than the cost of the first state, the first state is redefined to correspond to the second set of the values of the variables. These steps are repeated until a solution of the CSP is found.

Abstract: A test-program generator capable of implementing a methodology, based on a formal language, for scheduling system-level transactions in generated test programs. A system to be tested may be composed of multiple processors, busses, bus-bridges, shared memories, etc. The scheduling methodology is based on an exploration of scheduling abilities in a hardware system and features a Hierarchical Scheduling Language for specifying transactions and their ordering. Through a grouping hierarchy, which may also be expressed in the form of an equivalent tree, the Hierarchical Scheduling Language combines the ability to stress related logical areas of the system with the possibility of applying high-level scheduling requests. A method for generating testcases based on request-files written in the Hierarchical Scheduling Language is also presented.