Embodiments described herein are directed to a programming language trigger mechanism. The trigger mechanism is a small piece of code that a software developer utilizes in a computer program. The trigger mechanism enables computing operations or tasks to be performed asynchronously and in a parallel fashion. In particular, logic (e.g., operations or tasks) associated with the trigger mechanism are provided to a plurality of resources for processing in parallel. Each resource asynchronously processes the task provided thereto and asynchronously provides the result. The results are asynchronously returned as an enumeration. The enumeration enables the software developer to enumerate through the returned elements as a simple stream of results as they are calculated.

A multi-threaded imperative programming language includes a language construct defining a function call. A circuit implementation for the construct includes a first pipeline, a second pipeline, and a third pipeline. The first hardware pipeline outputs variables to a first queue and outputs parameters for the function to a second queue. The second hardware pipeline obtains the function parameters from the second queue, performs the function, and stores the results of the function in a third queue. The third hardware pipeline retrieves the results generated by the second pipeline from the second queue and retrieves the variables from the first queue. The third hardware pipeline performs hardware operations specified by the source code using the variables and the results of the function. A single instance of the circuit implementation can be utilized to implement calls to the same function made from multiple locations within source code.

Disclosed is a highly parallel processing system for processing graphics applications written in a high-level programming language. The high-performance computing architecture includes a graphics processing unit with numerous processing cores, such as hundreds to thousands of processing cores. The graphics processing unit includes routines written in a low-level programming language. The routines of the graphics processing unit are invoked to process highly computational intensive tasks by the numerous processing cores in parallel.

Various embodiments include systems and methods of operating the systems that include operation of a plurality of first nodes and second nodes in response to a request, where each first node is a first type of processing unit and each second node is a second type of processing unit, where the second type of processing node is different from the first type of processing node. Each of the first and second nodes can be operable in parallel with the other nodes of their respective plurality. Each second node may be operable to respond to the request using data and/or metadata it holds and/or operable in response to data and/or metadata from one or more of the first nodes. Additional apparatus, systems, and methods are disclosed.

A multi-threaded imperative programming language includes a language construct defining a function call. A circuit implementation for the construct includes a first pipeline, a second pipeline, and a third pipeline. The first hardware pipeline outputs variables to a first queue and outputs parameters for the function to a second queue. The second hardware pipeline obtains the function parameters from the second queue, performs the function, and stores the results of the function in a third queue. The third hardware pipeline retrieves the results generated by the second pipeline from the second queue and retrieves the variables from the first queue. The third hardware pipeline performs hardware operations specified by the source code using the variables and the results of the function. A single instance of the circuit implementation can be utilized to implement calls to the same function made from multiple locations within source code.

A data parallel pipeline may specify multiple parallel data objects that contain multiple elements and multiple parallel operations that operate on the parallel data objects. Based on the data parallel pipeline, a dataflow graph of deferred parallel data objects and deferred parallel operations corresponding to the data parallel pipeline may be generated and one or more graph transformations may be applied to the dataflow graph to generate a revised dataflow graph that includes one or more of the deferred parallel data objects and deferred, combined parallel data operations. The deferred, combined parallel operations may be executed to produce materialized parallel data objects corresponding to the deferred parallel data objects.

A data parallel pipeline may specify multiple parallel data objects that contain multiple elements and multiple parallel operations that operate on the parallel data objects. Based on the data parallel pipeline, a dataflow graph of deferred parallel data objects and deferred parallel operations corresponding to the data parallel pipeline may be generated and one or more graph transformations may be applied to the dataflow graph to generate a revised dataflow graph that includes one or more of the deferred parallel data objects and deferred, combined parallel data operations. The deferred, combined parallel operations may be executed to produce materialized parallel data objects corresponding to the deferred parallel data objects.

A data parallel pipeline may specify multiple parallel data objects that contain multiple elements and multiple parallel operations that operate on the parallel data objects. Based on the data parallel pipeline, a dataflow graph of deferred parallel data objects and deferred parallel operations corresponding to the data parallel pipeline may be generated and one or more graph transformations may be applied to the dataflow graph to generate a revised dataflow graph that includes one or more of the deferred parallel data objects and deferred, combined parallel data operations. The deferred, combined parallel operations may be executed to produce materialized parallel data objects corresponding to the deferred parallel data objects.

A data parallel pipeline may specify multiple parallel data objects that contain multiple elements and multiple parallel operations that operate on the parallel data objects. Based on the data parallel pipeline, a dataflow graph of deferred parallel data objects and deferred parallel operations corresponding to the data parallel pipeline may be generated and one or more graph transformations may be applied to the dataflow graph to generate a revised dataflow graph that includes one or more of the deferred parallel data objects and deferred, combined parallel data operations. The deferred, combined parallel operations may be executed to produce materialized parallel data objects corresponding to the deferred parallel data objects.

Examples include quantum computing compiling methods comprising considering a threshold corresponding to a maximum number of qubits available for processing in any one subsystem of a plurality of interconnected qubit subsystems and identifying a total number of qubits submitted to a specific quantum circuit, the total number of qubits exceeding the threshold. The methods comprise compiling a first section of the specific quantum circuit on a first subsystem by successively selecting quantum gates. If a selected quantum gate is to be applied to qubits assigned to different subsystems, the passing of a qubit from the first subsystem to a second subsystem through a junction connecting the first subsystem to the second subsystem is coded, and the second section of the specific quantum circuit is compiled on the second subsystem.