Systems and methods for version control of pipelined enterprise software are disclosed. Exemplary implementations may: store information for executable code of software applications that are installed and executable by users, receive first user input from a first user that represents selection by the first user of a first software pipeline for execution; receive second user input from a second user that represents a second selection by the second user of a second software pipeline for execution, wherein the second software pipeline includes different versions of software applications that are included in the first software pipeline; facilitate execution of the first software pipeline for the first user; and facilitate execution of the second software pipeline for the second user at the same time as the execution of the first software pipeline for the first user.

In accordance with various embodiments, described herein is a system (Data Artificial Intelligence system, Data AI system), for use with a data integration or other computing environment, that leverages machine learning (ML, DataFlow Machine Learning, DFML), for use in managing a flow of data (dataflow, DF), and building complex dataflow software applications (dataflow applications, pipelines). In accordance with an embodiment, the system can provide support for auto-mapping of complex data structures, datasets or entities, between one or more sources or targets of data, referred to herein in some embodiments as HUBs. The auto-mapping can be driven by a metadata, schema, and statistical profiling of a dataset; and used to map a source dataset or entity associated with an input HUB, to a target dataset or entity or vice versa, to produce an output data prepared in a format or organization (projection) for use with one or more output HUBs.

In accordance with various embodiments, described herein is a system (Data Artificial Intelligence system, Data AI system), for use with a data integration or other computing environment, that leverages machine learning (ML, DataFlow Machine Learning, DFML), for use in managing a flow of data (dataflow, DF), and building complex dataflow software applications (dataflow applications, pipelines). In accordance with an embodiment, the system can provide support for auto-mapping of complex data structures, datasets or entities, between one or more sources or targets of data, referred to herein in some embodiments as HUBs. The auto-mapping can be driven by a metadata, schema, and statistical profiling of a dataset; and used to map a source dataset or entity associated with an input HUB, to a target dataset or entity or vice versa, to produce an output data prepared in a format or organization (projection) for use with one or more output HUBs.

A multi-threaded programming language and compiler generates synchronous digital circuits that maintain thread execution order by generating pipelines with code paths that have the same number of stages. The compiler balances related code paths within a pipeline by adding additional stages to a code path that has fewer stages. Programming constructs that, by design, allow thread execution to be re-ordered, may be placed in a reorder block construct that releases threads in the order they entered the programming construct. First-in-first-out (FIFO) queues pass local variables between pipelines. Local variables are popped from FIFOs in the order they were pushed, preserving thread execution order across pipelines.

A method for scheduling instructions for execution on a computer system includes scanning a plurality of loop instructions that are modulo scheduled to identify a first instruction and a second instruction that both utilize a register of the computer system upon execution of the plurality of instructions. The loop has a first initiation interval. The first instruction defines a first value of the register in a first iteration of the loop and he second instruction redefines the value of the register to a second value in a subsequent iteration of the loop prior to a use of the first value in the first iteration of the loop. A copy instruction is inserted in the loop instructions to copy the first value prior to execution of the second instruction. A schedule is determined after the insertion of the one or more copy instructions giving a second initiation interval.

Systems, methods, and non-transitory computer readable media are provided for managing pipelines of operations on data. A system may access data and provide a set of functions for the data. The system may receive a user's selection of one or more functions from the set of functions. The system may generate a pipeline of operations for the data based on the user's selection. The pipeline of operations may include the function(s) selected by the user.

Systems, apparatuses and methods may provide for technology that identifies that an iterative loop includes a first code portion that executes in response to a condition being satisfied, generates a first vector mask that is to represent one or more instances of the condition being satisfied for one or more values of a first vector of values, and one or more instances of the condition being unsatisfied for the first vector of values, where the first vector of values is to correspond to one or more first iterations of the iterative loop, and conducts a vectorization process of the iterative loop based on the first vector mask.

Systems and methods for version control of pipelined enterprise software are disclosed. Exemplary implementations may: store information for executable code of software applications that are installed and executable by users, receive first user input from a first user that represents selection by the first user of a first software pipeline for execution; receive second user input from a second user that represents a second selection by the second user of a second software pipeline for execution, wherein the second software pipeline includes different versions of software applications that are included in the first software pipeline; facilitate execution of the first software pipeline for the first user; and facilitate execution of the second software pipeline for the second user at the same time as the execution of the first software pipeline for the first user.

A system and method to automatically generate a software service to provide service layer functionalities to legacy computing systems that are inherently incompatible with a Service Oriented Architecture (SOA) consumer environment. A configuration specification defining the characteristics of the software service, including data mapping rules is received. Based on the specification, at least one pattern template for the software service is selected from a library of templates. The pattern templates provide source code patterns usable to build the software service. Source code of the software service is outputted using programming code provided in the at least one design pattern template. The outputted source code is packaged or assembled into a source code package for deployment.

In accordance with various embodiments, described herein is a system (Data Artificial Intelligence system, Data AI system), for use with a data integration or other computing environment, that leverages machine learning (ML, DataFlow Machine Learning, DFML), for use in managing a flow of data (dataflow, DF), and building complex dataflow software applications (dataflow applications, pipelines). In accordance with an embodiment, the system provides a programmatic interface, referred to herein in some embodiments as a foreign function interface, by which a user or third-party can define a service, functional and business types, semantic actions, and patterns or predefined complex data flows based on functional and business types, in a declarative manner, to extend the functionality of the system.