In a general aspect, a method of selecting program files of an executable software application includes: extracting program text files from an executable application file; for each program text file, computing at least one syntactical metrics or program complexity metrics, and computing a score based on the computed metrics; and generating a list of program text files selected as a function of comparisons of the computed scores with threshold values.

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 a service to recommend actions and transformations, on an input data, based on patterns identified from the functional decomposition of a data flow for a software application, including determining possible transformations of the data flow in subsequent applications. Data flows can be decomposed into a model describing transformations of data, predicates, and business rules applied to the data, and attributes used in the data flows.

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.

An electronic device acquires, from program code, two or more program code loops having specified data dependencies. The electronic device places each of the program code loops into a corresponding blocking loop, each blocking loop including at least one blocking loop induction variable that is incremented by a corresponding block size and used to specify a number of iterations for at least one internal loop induction variable of the respective program code loop. The electronic device fuses the blocking loops into a fused loop by placing all of the blocking loops in the fused loop and replacing the blocking loop induction variables of the blocking loops with a fused loop induction variable that is incremented by the corresponding block size and used to specify the number of iterations for respective internal loop induction variables in the blocking loops.

Techniques are provided for managing and isolating build process pipelines. The system can encapsulate all the information needed for each build process step in a build context structure, which may be accessible to the build process step. Each build process step can receive input from the build context, and can generate a child build context as output. Accordingly, the build pipeline may be parallelized, duplicated, and/or virtualized securely and automatically, and the build context can carry, organize, and isolate data for each task. The build context from each step can also be stored and subsequently inspected, e.g. for problem-solving. A computing device can execute a first build step configured to generate the build context including a plurality of output objects. The computing device further executes a second build step based on the build context, which is accessible to the second build step but isolated from other processes.

In the described examples, a non-transitory machine-readable medium includes a compiler that detects a soft-break indicator in a loop included in source code and the compiler applies software pipelining to generate compiled code for the loop. The compiled code includes assembly instructions and the soft-break indicator enables the compiler to arrange the assembly instructions to complete in-flight iterations of the loop after execution of the soft-break.

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 include a software development component and graphical user interface, referred to herein in some embodiments as a pipeline editor, or Lambda Studio IDE, that provides a visual environment for use with the system, including providing real-time recommendations for performing semantic actions on data accessed from an input HUB, based on an understanding of the meaning or semantics associated with the data.

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 perform an ontology analysis of a schema definition, to determine the types of data, and datasets or entities, associated with that schema; and generate, or update, a model from a reference schema that includes an ontology defined based on relationships between datasets or entities, and their attributes. A reference HUB including one or more schemas can be used to analyze data flows, and further classify or make recommendations such as, for example, transformations enrichments, filtering, or cross-entity data fusion of an input data.

A method and system for partial connection of iterations during loop unrolling during compilation of a program by a compiler. Unrolled loop iterations of a loop in the program are selectively connected during loop unrolling during the compilation, including redirecting, to the head of the loop, undesirable edges of a control flow from one iteration to a next iteration of the loop. Merges on a path of hot code are removed to increase a scope for optimization of the program. The head of the loop and a start of a replicated loop body of the loop are equivalent points of the control flow.

In one example, an apparatus comprises processing circuitry to analyze a program at compile time to determine a set of latency parameters associated with instruction sets implemented to execute the program and select a latency management technique based at least in part on the set of latency parameters associated with instruction sets implemented to execute the program. Other examples may be described and claimed.