Abstract: An execution environment in a computer system supports a declarative programming model where user code is written with a query syntax in a native programming language to express inherent parallelism in terms of data flow. The execution environment translates queries in the user code into a runtime agnostic representation and dynamically selects an execution runtime for executing the runtime agnostic representation.

Abstract: An execution environment in a computer system provides dynamic data and compute resources elasticity for user code to improve execution efficiency. The execution environment translates the user code into a runtime agnostic representation with a set of tasks. For each task, the execution environment determines a level of concurrency for executing the task based on the size of the set of input data for the task, the amount of compute resources available at the time of invocation of the task, and any context-sensitive heuristics provided by the user code.

Abstract: An execution environment in a computer system allows user code to be executed using multiple execution runtimes. The execution environment translates the user code into a runtime agnostic representation, selects an execution runtime for executing the runtime agnostic representation, and invokes a scheduler for the selected execution runtime. The scheduler dispatches tasks from the runtime agnostic representation for execution by the computer system using concurrency mechanisms in the selected execution runtime.

Abstract: An execution environment in a computer system supports a declarative programming model where user code is written with a query syntax in a native programming language to express inherent parallelism in terms of data flow. The execution environment translates queries in the user code into a runtime agnostic representation and dynamically selects an execution runtime for executing the runtime agnostic representation.

Abstract: An execution environment in a computer system provides dynamic data and compute resources elasticity for user code to improve execution efficiency. The execution environment translates the user code into a runtime agnostic representation with a set of tasks. For each task, the execution environment determines a level of concurrency for executing the task based on the size of the set of input data for the task, the amount of compute resources available at the time of invocation of the task, and any context-sensitive heuristics provided by the user code.

Abstract: The described embodiments include a networking subsystem in a second computing device that is configured to receive a task message from a first computing device. Based on the task message, the networking subsystem updates an entry in a task queue with task information from the task message. A processing subsystem in the second computing device subsequently retrieves the task information from the task queue and performs the corresponding task. In these embodiments, the networking subsystem processes the task message (e.g., stores the task information in the task queue) without causing the processing subsystem to perform operations for processing the task message.

Abstract: A concurrent data structure allows synchronization to be elided for read accesses. Processing resources that remove one or more elements of the concurrent data structure are allowed to delete the elements only after all other processing resources have reached a safe point. Each processing resource maintains an indicator that indicates whether the processing resource has reached as safe point (i.e., will not access the concurrent data structure). When the indicators indicate that all processing resources have reached a safe point, elements of the data structure may be deleted.

Abstract: The described embodiments include a networking subsystem in a second computing device that is configured to receive a task message from a first computing device. Based on the task message, the networking subsystem updates an entry in a task queue with task information from the task message. A processing subsystem in the second computing device subsequently retrieves the task information from the task queue and performs the corresponding task. In these embodiments, the networking subsystem processes the task message (e.g., stores the task information in the task queue) without causing the processing subsystem to perform operations for processing the task message.

Abstract: Processing messages in dataflow networks. The method includes, at a first entity, receiving from a second entity a first offer of a first message to process. The method further includes determining to not process the first message. As a result of determining to not process the first message, such an indication is made to the second entity. Further as a result of determining to not process the first message, an indication is stored that the second entity offered a message. The indication includes an indicator correlated to the second entity. Subsequent to indicating to the second entity, using the indication the method includes indicating to the second entity availability to process a message.

Abstract: Processing messages in dataflow networks. The method includes, at a first entity, receiving from a second entity a first offer of a first message to process. The method further includes determining to not process the first message. As a result of determining to not process the first message, such an indication is made to the second entity. Further as a result of determining to not process the first message, an indication is stored that the second entity offered a message. The indication includes an indicator correlated to the second entity. Subsequent to indicating to the second entity, using the indication the method includes indicating to the second entity availability to process a message.

Abstract: Functionality is described for providing a compiled program that can be executed in a parallel and a distributed manner by any selected runtime environment. The functionality includes a compiler module for producing the compiled program based on a dataflow representation of a program (i.e., a dataflow-expressed program). The dataflow-expressed program, in turn, includes a plurality of tasks that are connected together in a manner specified by a graph (such as a directed acyclic graph). The compiler module also involves performing static type-checking on the dataflow-expressed program to identify the presence of any mismatch errors in the dataflow-expressed program. By virtue of this approach, the above-described functionality can identify any errors in constructing the graph prior to its instantiation and execution in a runtime environment.

Abstract: An execution environment in a computer system supports a declarative programming model where user code is written with a query syntax in a native programming language to express inherent parallelism in terms of data flow. The execution environment translates queries in the user code into a runtime agnostic representation and dynamically selects an execution runtime for executing the runtime agnostic representation.

Abstract: An execution environment in a computer system allows user code to be executed using multiple execution runtimes. The execution environment translates the user code into a runtime agnostic representation, selects an execution runtime for executing the runtime agnostic representation, and invokes a scheduler for the selected execution runtime. The scheduler dispatches tasks from the runtime agnostic representation for execution by the computer system using concurrency mechanisms in the selected execution runtime.

Abstract: An execution environment in a computer system provides dynamic data and compute resources elasticity for user code to improve execution efficiency. The execution environment translates the user code into a runtime agnostic representation with a set of tasks. For each task, the execution environment determines a level of concurrency for executing the task based on the size of the set of input data for the task, the amount of compute resources available at the time of invocation of the task, and any context-sensitive heuristics provided by the user code.

Abstract: A concurrent data structure allows synchronization to be elided for read accesses. Processing resources that remove one or more elements of the concurrent data structure are allowed to delete the elements only after all other processing resources have reached a safe point. Each processing resource maintains an indicator that indicates whether the processing resource has reached as safe point (i.e., will not access the concurrent data structure). When the indicators indicate that all processing resources have reached a safe point, elements of the data structure may be deleted.