Abstract: The disclosed embodiments provide a system that manages access to data associated with an online storage system. During operation, the system enables synchronization of the data between an electronic device and the online storage system through an application programming interface (API) with an application on the electronic device. Next, the system uses the API to provide a synchronization state of the data to the application, wherein the synchronization state comprises at least one of a download state, an upload state, an idle state, a transfer progress, a cached state, and an error state.

Abstract: Processing data communications events in a parallel active messaging interface (‘PAMI’) of a parallel computer that includes compute nodes that execute a parallel application, with the PAMI including data communications endpoints, and the endpoints are coupled for data communications through the PAMI and through other data communications resources, including determining by an advance function that there are no actionable data communications events pending for its context, placing by the advance function its thread of execution into a wait state, waiting for a subsequent data communications event for the context; responsive to occurrence of a subsequent data communications event for the context, awakening by the thread from the wait state; and processing by the advance function the subsequent data communications event now pending for the context.

Abstract: Methods, parallel computers, and computer program products for analyzing update conditions for shared variable directory (SVD) information in a parallel computer are provided. Embodiments include a runtime optimizer receiving a compare-and-swap operation header. The compare-and-swap operation header includes an SVD key, a first SVD address, and an updated first SVD address. The first SVD address is associated with the SVD key in a first SVD associated with a first task. Embodiments also include the runtime optimizer retrieving from a remote address cache associated with the second task, a second SVD address indicating a location within a memory partition associated with the first SVD in response to receiving the compare-and-swap operation header. Embodiments also include the runtime optimizer determining whether the second SVD address matches the first SVD address and transmitting a result indicating whether the second SVD address matches the first SVD address.

Abstract: Methods, parallel computers, and computer program products for analyzing update conditions for shared variable directory (SVD) information in a parallel computer are provided. Embodiments include a runtime optimizer receiving a compare-and-swap operation header. The compare-and-swap operation header includes an SVD key, a first SVD address, and an updated first SVD address. The first SVD address is associated with the SVD key in a first SVD associated with a first task. Embodiments also include the runtime optimizer retrieving from a remote address cache associated with the second task, a second SVD address indicating a location within a memory partition associated with the first SVD in response to receiving the compare-and-swap operation header. Embodiments also include the runtime optimizer determining whether the second SVD address matches the first SVD address and transmitting a result indicating whether the second SVD address matches the first SVD address.

Abstract: Methods, parallel computers, and computer program products for conditionally updating shared variable directory (SVD) information in a parallel computer are provided. Embodiments include a runtime optimizer receiving a broadcast reduction operation header. The broadcast reduction operation header includes an SVD key and a first SVD address. The first SVD address is associated with the SVD key in a first SVD associated with a first task. Embodiments also include the runtime optimizer retrieving from a remote address cache associated with the second task, a second SVD address indicating a location within a memory partition associated with the first SVD, in response to receiving the broadcast reduction operation header. Embodiments also include the runtime optimizer determining that the first SVD address does not match the second SVD address and updating the remote address cache with the first SVD address.

Abstract: Methods, parallel computers, and computer program products for acquiring remote shared variable directory (SVD) information in a parallel computer are provided. Embodiments include a runtime optimizer determining that a first thread of a first task requires shared resource data stored in a memory partition corresponding to a second thread of a second task. Embodiments also include the runtime optimizer requesting from the second thread, in response to determining that the first thread of the first task requires the shared resource data, SVD information associated with the shared resource data. Embodiments also include the runtime optimizer receiving from the second thread, the SVD information associated with the shared resource data.

Abstract: Methods, parallel computers, and computer program products for acquiring remote shared variable directory (SVD) information in a parallel computer are provided. Embodiments include a runtime optimizer determining that a first thread of a first task requires shared resource data stored in a memory partition corresponding to a second thread of a second task. Embodiments also include the runtime optimizer requesting from the second thread, in response to determining that the first thread of the first task requires the shared resource data, SVD information associated with the shared resource data. Embodiments also include the runtime optimizer receiving from the second thread, the SVD information associated with the shared resource data.

Abstract: Methods, parallel computers, and computer program products for conditionally updating shared variable directory (SVD) information in a parallel computer are provided. Embodiments include a runtime optimizer receiving a broadcast reduction operation header. The broadcast reduction operation header includes an SVD key and a first SVD address. The first SVD address is associated with the SVD key in a first SVD associated with a first task. Embodiments also include the runtime optimizer retrieving from a remote address cache associated with the second task, a second SVD address indicating a location within a memory partition associated with the first SVD, in response to receiving the broadcast reduction operation header. Embodiments also include the runtime optimizer determining that the first SVD address does not match the second SVD address and updating the remote address cache with the first SVD address.

Abstract: A parallel computer executes a number of tasks, each task includes a number of endpoints and the endpoints are configured to support collective operations. In such a parallel computer, establishing a group of endpoints receiving a user specification of a set of endpoints included in a global collection of endpoints, where the user specification defines the set in accordance with a predefined virtual representation of the endpoints, the predefined virtual representation is a data structure setting forth an organization of tasks and endpoints included in the global collection of endpoints and the user specification defines the set of endpoints without a user specification of a particular endpoint; and defining a group of endpoints in dependence upon the predefined virtual representation of the endpoints and the user specification.

Abstract: A parallel computer executes a number of tasks, each task includes a number of endpoints and the endpoints are configured to support collective operations. In such a parallel computer, establishing a group of endpoints receiving a user specification of a set of endpoints included in a global collection of endpoints, where the user specification defines the set in accordance with a predefined virtual representation of the endpoints, the predefined virtual representation is a data structure setting forth an organization of tasks and endpoints included in the global collection of endpoints and the user specification defines the set of endpoints without a user specification of a particular endpoint; and defining a group of endpoints in dependence upon the predefined virtual representation of the endpoints and the user specification.

Abstract: Determining a path for network traffic between a source compute node and a destination compute node in a parallel computer including identifying a group of compute nodes, the group of compute nodes having topological network locations included in a predefined topological shape; selecting, from the predefined topological shape, in dependence upon a global contention counter stored on the source compute node, a path on which to send a data communications message from the source compute node to the destination compute node; and sending, by the messaging module of the source compute node, the data communications message along the selected path for network traffic between the source and destination compute nodes.

Abstract: Methods, apparatuses, and computer program products for collective operation management in a parallel computer are provided. Embodiments include a parallel computer having a first compute node operatively coupled for data communications over a tree data communications network with a plurality of child compute nodes. Embodiments also include each child compute node performing a first collective operation. The first compute rode, for each child compute node, receives from the child compute node, a result of the first collective operation performed by the child compute node. In response to receiving at least one result, the first compute node reduces a power consumption level of the child compute node.

Abstract: Algorithm selection for collective operations in a parallel computer that includes a plurality of compute nodes may include: profiling a plurality of algorithms for each of a set of collective operations, including for each collective operation: executing the operation a plurality times with each execution varying one or more of: geometry, message size, data type, and algorithm to effect the collective operation, thereby generating performance metrics for each execution; storing the performance metrics in a performance profile; at load time of a parallel application including a plurality of parallel processes configured in a particular geometry, filtering the performance profile in dependence upon the particular geometry; during run-time of the parallel application, selecting, for at least one collective operation, an algorithm to effect the operation in dependence upon characteristics of the parallel application and the performance profile; and executing the operation using the selected algorithm.

Abstract: Determining a system configuration for performing a collective operation on a parallel computer that includes a plurality of compute nodes, the compute nodes coupled for data communications over a data communications network, including: selecting a system configuration on the parallel computer for executing the collective operation; executing the collective operation on the selected system configuration on the parallel computer; determining performance metrics associated with executing the collective operation on the selected system configuration on the parallel computer; selecting, using a simulated annealing algorithm, a plurality of test system configurations on the parallel computer for executing the collective operation, wherein the simulated annealing algorithm specifies a similarity threshold between a plurality of system configurations; executing, the collective operation on each of the test system configurations; and determining performance metrics associated with executing the collective operation o

Abstract: Algorithm selection for collective operations in a parallel computer that includes a plurality of compute nodes may include: profiling a plurality of algorithms for each of a set of collective operations, including for each collective operation: executing the operation a plurality times with each execution varying one or more of: geometry, message size, data type, and algorithm to effect the collective operation, thereby generating performance metrics for each execution; storing the performance metrics in a performance profile; at load time of a parallel application including a plurality of parallel processes configured in a particular geometry, filtering the performance profile in dependence upon the particular geometry; during run-time of the parallel application, selecting, for at least one collective operation, an algorithm to effect the operation in dependence upon characteristics of the parallel application and the performance profile; and executing the operation using the selected algorithm.

Abstract: A guardrail system includes a guardrail, a support post, and a fastener joining the guardrail and the support post. The support post includes a hole receiving the fastener, a fastener retention mechanism, and a slot for the movement of the fastener during an impact. The fastener retention mechanism retains the fastener in the hole until a predetermined level of force is attained during an impact, after which the fastener is released and moves into the slot. Methods of moving the guardrail relative to the post are also provided.

Abstract: Executing a collective operation algorithm in a parallel computer includes a compute node of an operational group determining a required number of participants for execution of a collective operation algorithm and determining a number of contributing nodes having data to participate in the algorithm. Embodiments also include the compute node calculating a number of ghost nodes to participate in the algorithm. According to embodiments of the present invention, the number of ghost nodes is the required number of participants minus the number of contributing nodes having data to participate. Embodiments also include the compute node selecting from a plurality of ghost nodes, the calculated number of ghost nodes for participation in the execution of the algorithm and executing the algorithm with both the selected ghost nodes and the contributing nodes.

Abstract: Executing a collective operation algorithm in a parallel computer includes a compute node of an operational group determining a required number of participants for execution of a collective operation algorithm and determining a number of contributing nodes having data to participate in the algorithm. Embodiments also include the compute node calculating a number of ghost nodes to participate in the algorithm. According to embodiments of the present invention, the number of ghost nodes is the required number of participants minus the number of contributing nodes having data to participate. Embodiments also include the compute node selecting from a plurality of ghost nodes, the calculated number of ghost nodes for participation in the execution of the algorithm and executing the algorithm with both the selected ghost nodes and the contributing nodes.

Abstract: Background collective operation management in a parallel computer, the parallel computer including one or more compute nodes operatively coupled for data communications over one or more data communications networks, including: determining, by a management availability module, whether a compute node in the parallel computer is available to perform a background collective operation management task; responsive to determining that the compute node is available to perform the background collective operation management task, determining, by the management availability module, whether the compute node has access to sufficient resources to perform the background collective operation management task; and responsive to determining that the compute node has access to sufficient resources to perform the background collective operation management task, initiating, by the management availability module, execution of the background collective operation management task.

Abstract: Background collective operation management in a parallel computer, the parallel computer including one or more compute nodes operatively coupled for data communications over one or more data communications networks, including: determining, by a management availability module, whether a compute node in the parallel computer is available to perform a background collective operation management task; responsive to determining that the compute node is available to perform the background collective operation management task, determining, by the management availability module, whether the compute node has access to sufficient resources to perform the background collective operation management task; and responsive to determining that the compute node has access to sufficient resources to perform the background collective operation management task, initiating, by the management availability module, execution of the background collective operation management task.