Abstract: A technology is described for debugging in a cluster processing network. A scheduler can dispatch a process that is part of the cluster job for execution. Further, a compute node can be used to execute the process dispatched by the scheduler to the compute node. A debugger can be activated in response to an unhandled suspension event in the process on the compute node. In addition, the debugger can send notification messages regarding the unhandled suspension event. A job monitor can receive a notification from the debugger that an unhandled suspension event has occurred. The notification can be displayed to a user via the job monitor.

Abstract: A technology is described for debugging in a cluster processing network. A scheduler can dispatch a process that is part of the cluster job for execution. Further, a compute node can be used to execute the process dispatched by the scheduler to the compute node. A debugger can be activated in response to an unhandled suspension event in the process on the compute node. In addition, the debugger can send notification messages regarding the unhandled suspension event. A job monitor can receive a notification from the debugger that an unhandled suspension event has occurred. The notification can be displayed to a user via the job monitor.

Abstract: A method for generating a Bayesian network in a parallel manner is based on an initial model having a plurality of nodes. Each node corresponds to a variable of a data set and has a local distribution associated therewith. The method includes assigning a plurality of subsets of the nodes to a respective plurality of constructors. The plurality of constructors is operated in a parallel manner to identify edges to add between nodes in the initial model. The identified edges are added to the initial model to generate the Bayesian network. The edges indicate dependency between nodes connected by the edges.

Abstract: A method for generating a Bayesian network in a parallel manner is based on an initial model having a plurality of nodes. Each node corresponds to a variable of a data set and has a local distribution associated therewith. The method includes assigning a plurality of subsets of the nodes to a respective plurality of constructors. The plurality of constructors is operated in a parallel manner to identify edges to add between nodes in the initial model. The identified edges are added to the initial model to generate the Bayesian network. The edges indicate dependency between nodes connected by the edges.