Abstract: In some embodiments, a method includes determining a position for a search query and a position for each audience record from multiple audience records in an embedding space. The method further includes receiving multiple device records, each associated with an audience record. The method further includes determining multiple keywords, each associated with an audience record and determining a position for each keyword in the embedding space. The method further includes calculating a first distance between the position of the search query in the embedding space and the position of each audience record in the embedding space. The method further includes calculating a second distance between the position of the search query in the embedding space and the position of each keyword in the embedding space. The method further includes ranking each audience record based on the first distance and the second distance.

Abstract: In some embodiments, a method includes determining a position for a search query and a position for each audience record from multiple audience records in an embedding space. The method further includes receiving multiple device records, each associated with an audience record. The method further includes determining multiple keywords, each associated with an audience record and determining a position for each keyword in the embedding space. The method further includes calculating a first distance between the position of the search query in the embedding space and the position of each audience record in the embedding space. The method further includes calculating a second distance between the position of the search query in the embedding space and the position of each keyword in the embedding space. The method further includes ranking each audience record based on the first distance and the second distance.

Abstract: In some embodiments, a method includes determining a position for a search query and a position for each audience record from multiple audience records in an embedding space. The method further includes receiving multiple device records, each associated with an audience record. The method further includes determining multiple keywords, each associated with an audience record and determining a position for each keyword in the embedding space. The method further includes calculating a first distance between the position of the search query in the embedding space and the position of each audience record in the embedding space. The method further includes calculating a second distance between the position of the search query in the embedding space and the position of each keyword in the embedding space. The method further includes ranking each audience record based on the first distance and the second distance.

Abstract: In some embodiments, a method includes determining a position for a search query and a position for each audience record from multiple audience records in an embedding space. The method further includes receiving multiple device records, each associated with an audience record. The method further includes determining multiple keywords, each associated with an audience record and determining a position for each keyword in the embedding space. The method further includes calculating a first distance between the position of the search query in the embedding space and the position of each audience record in the embedding space. The method further includes calculating a second distance between the position of the search query in the embedding space and the position of each keyword in the embedding space. The method further includes ranking each audience record based on the first distance and the second distance.

Abstract: In some embodiments, a method includes determining a position for a search query and a position for each audience record from multiple audience records in an embedding space. The method further includes receiving multiple device records, each associated with an audience record. The method further includes determining multiple keywords, each associated with an audience record and determining a position for each keyword in the embedding space. The method further includes calculating a first distance between the position of the search query in the embedding space and the position of each audience record in the embedding space. The method further includes calculating a second distance between the position of the search query in the embedding space and the position of each keyword in the embedding space. The method further includes ranking each audience record based on the first distance and the second distance.

Abstract: In some embodiments, a method includes determining a position for a search query and a position for each audience record from multiple audience records in an embedding space. The method further includes receiving multiple device records, each associated with an audience record. The method further includes determining multiple keywords, each associated with an audience record and determining a position for each keyword in the embedding space. The method further includes calculating a first distance between the position of the search query in the embedding space and the position of each audience record in the embedding space. The method further includes calculating a second distance between the position of the search query in the embedding space and the position of each keyword in the embedding space. The method further includes ranking each audience record based on the first distance and the second distance.

Abstract: In some embodiments, a method includes determining a position for a search query and a position for each audience record from multiple audience records in an embedding space. The method further includes receiving multiple device records, each associated with an audience record. The method further includes determining multiple keywords, each associated with an audience record and determining a position for each keyword in the embedding space. The method further includes calculating a first distance between the position of the search query in the embedding space and the position of each audience record in the embedding space. The method further includes calculating a second distance between the position of the search query in the embedding space and the position of each keyword in the embedding space. The method further includes ranking each audience record based on the first distance and the second distance.

Abstract: A method and apparatus creates and manages persistent memory (PM) in a multi-node computing system. A PM Manager in the service node creates and manages pools of nodes with various sizes of PM. A node manager uses the pools of nodes to load applications to the nodes according to the size of the available PM. The PM Manager can dynamically adjust the size of the PM according to the needs of the applications based on historical use or as determined by a system administrator. The PM Manager works with an operating system kernel on the nodes to provide persistent memory for application data and system metadata. The PM Manager uses the persistent memory to load applications to preserve data from one application to the next. Also, the data preserved in persistent memory may be system metadata such as file system data that will be available to subsequent applications.

Abstract: A method and apparatus creates and manages persistent memory (PM) in a multi-node computing system. A PM Manager in the service node creates and manages pools of nodes with various sizes of PM. A node manager uses the pools of nodes to load applications to the nodes according to the size of the available PM. The PM Manager can dynamically adjust the size of the PM according to the needs of the applications based on historical use or as determined by a system administrator. The PM Manager works with an operating system kernel on the nodes to provide persistent memory for application data and system metadata. The PM Manager uses the persistent memory to load applications to preserve data from one application to the next. Also, the data preserved in persistent memory may be system metadata such as file system data that will be available to subsequent applications.

Abstract: Embodiments off the invention provide a mechanism for process migration on a massively parallel computer system. In particular, embodiments of the invention may be used to update process state data for a migrated compute node, such as MPI (or other communication library) state data, across a full collection of compute nodes present in a given parallel system executing a parallel task. Migrating a process form one compute node to another may be useful to address a variety of sub-optimal operating conditions. For example, one or more processes may be migrated to cure network congestion resulting from a poorly mapped task or when a compute node is predicted to experience a hardware failure.

Abstract: A method and apparatus creates and manages persistent memory (PM) in a multi-node computing system. A PM Manager in the service node creates and manages pools of nodes with various sizes of PM. A node manager uses the pools of nodes to load applications to the nodes according to the size of the available PM. The PM Manager can dynamically adjust the size of the PM according to the needs of the applications based on historical use or as determined by a system administrator. The PM Manager works with an operating system kernel on the nodes to provide persistent memory for application data and system metadata. The PM Manager uses the persistent memory to load applications to preserve data from one application to the next. Also, the data preserved in persistent memory may be system metadata such as file system data that will be available to subsequent applications.

Abstract: An apparatus, program product and method in which application program-specified resource allocation and allocation strength preferences are used to allocate hardware resources from a computer for that application program. The resource allocation preference is used to allocate hardware resources for the application program, while the allocation strength preference is used to determine whether to allocate alternate hardware resources when the resource allocation preference specified by the application program cannot be met.

Abstract: An apparatus, program product and method in which application program-specified resource allocation and allocation strength preferences are used to allocate hardware resources from a computer for that application program. The resource allocation preference is used to allocate hardware resources for the application program, while the allocation strength preference is used to determine whether to allocate alternate hardware resources when the resource allocation preference specified by the application program cannot be met.

Abstract: An apparatus and method for overcoming a torus network failure in a parallel computer system. A mesh routing mechanism in the service node of the computer system configures the nodes from a torus to a mesh network when a failure occurs in the torus network. The mesh routing mechanism takes advantage of cutoff registers in each node to route node to node data transfers around the faulty node or network connection.

Abstract: Methods, systems, and media for managing dynamic memory are disclosed. Embodiments may disclose identifying nodes with having memory for dynamic storage, and reserving a portion of the memory from the identified nodes for a heap pool. After generating a heap pool, embodiments may allocate dynamic storage from the heap pool to tasks received that are associated with one of the identified nodes. More specifically, embodiments identify the node or home node associated with the task, the amount of dynamic storage requested by the task, and create a heap object in the node associated with the task to provide the requested dynamic storage. Some embodiments involve de-allocating the dynamic storage assigned to the task upon receipt of an indication that the task is complete and the dynamic storage is no longer needed for the task. Several of such embodiments return the de-allocated dynamic storage to the heap pool for reuse.

Abstract: A data collector for a massively parallel computer system obtains call-return stack traceback data for multiple nodes by retrieving partial call-return stack traceback data from each node, grouping the nodes in subsets according to the partial traceback data, and obtaining further call-return stack traceback data from a representative node or nodes of each subset. Preferably, the partial data is a respective instruction address from each node, nodes having identical instruction address being grouped together in the same subset. Preferably, a single node of each subset is chosen and full stack traceback data is retrieved from the call-return stack within the chosen node.

Abstract: A method and apparatus for dynamically rerouting node processes on the compute nodes of a massively parallel computer system using hint bits to route around failed nodes or congested networks without restarting applications executing on the system. When a node has a failure or there are indications that it may fail, the application software on the system is suspended while the data on the failed node is moved to a backup node. The torus network traffic is routed around the failed node and traffic for the failed node is rerouted to the backup node. The application can then resume operation without restarting from the beginning.

Abstract: A method and apparatus creates and manages persistent memory (PM) in a multi-node computing system. A PM Manager in the service node creates and manages pools of nodes with various sizes of PM. A node manager uses the pools of nodes to load applications to the nodes according to the size of the available PM. The PM Manager can dynamically adjust the size of the PM according to the needs of the applications based on historical use or as determined by a system administrator. The PM Manager works with an operating system kernel on the nodes to provide persistent memory for application data and system metadata. The PM Manager uses the persistent memory to load applications to preserve data from one application to the next. Also, the data preserved in persistent memory may be system metadata such as file system data that will be available to subsequent applications.

Abstract: A method and apparatus for fault recovery of on a parallel computer system from a soft failure without ending an executing job on a partition of nodes. In preferred embodiments a failed hardware recovery mechanism on a service node uses a heartbeat monitor to determine when a node failure occurs. Where possible, the failed node is reset and re-loaded with software without ending the software job being executed by the partition containing the failed node.

Abstract: Disclosed is an apparatus, method, and program product that enables distribution of operating system resources on a nodal basis in the same proportions as the expected system workload. The preferred embodiment of the present invention accomplishes this by assigning various types of weights to each node to represent their proportion of the overall balance within the system. Target Weights represent the desired distribution of the workload based on the existing proportions of processor and memory resources on each node. The actual workload balance on the system is represented by Current Weights, which the operating system strives to keep as close to the Target Weights as possible, on an ongoing basis. When the system is started, operating system services distribute their resources nodally in the same proportions as the Target Weights, and can request to be notified if the Target Weights ever change.