This application discloses a stream computing job processing method, a stream computing system and an electronic device, and the method includes: obtaining a stream computing job; running the stream computing job in a process-based manner, where the stream computing job includes at least one process.

This application discloses a stream computing job processing method, a stream computing system and an electronic device, and the method includes: obtaining a stream computing job; running the stream computing job in a process-based manner, where the stream computing job includes at least one process.

A multi-chip compatible compiling method includes: extracting common characteristic information of Makefiles; configuring variable attribute information of the Makefiles; obtaining a universal Makefile template for multiple chips according to the common characteristic information and the variable attribute information of the Makefiles (S130); traversing a project to be compiled in the universal Makefile template by a compilation module, recording path information of a dependency file of the project to be compiled, replacing a first variable value in the universal Makefile template, and determining automation configuration information of the universal Makefile template (S140); invoking an incremental compilation mechanism of a Make tool according to the automation configuration information of the universal Makefile template to obtain a target Makefile of the project to be compiled (S150); and processing the target Makefile by rule information and pseudo-target tag information in the Makefiles to obtain an executable file generated by compilation (S160).

The invention provides an ETL pipeline system including an interface configured to obtain a plurality of graph configuration components. Each graph configuration component includes information representative of one or more computational logic rules. The system further includes a computation graph generator configured to generate a computation graph based on the obtained graph configuration components. The generated computation graph includes a node for each graph configuration component and one or more links representative of relationships between the nodes. The system further includes a computation graph adaptor configured to receive, from an external source, external information relating to the graph configuration components, and to adapt the generated computation graph based on the external information. The system further includes a computation graph runner configured to run the adapted computation graph.

A software and hardware collaborative compilation processing method and system. The system comprises an environment configurator, a command parser, a code filler, a scheduler and a heterogeneous target system, wherein the code filler is configured for obtaining the source program path of a user, reading source codes and identifying the heterogeneous target system according to a macro definition, complementing the codes related to the heterogeneous target system, carrying out primary filling and secondary filling on the source codes; the scheduler is configured for realizing compilation scheduling and execution scheduling functions respectively; the heterogeneous target system is configured for compiling and processing user modal data, and comprises at least two heterogeneous target subsystems; each target subsystem comprises a target-related middle-end compiler, a back-end compiler and a target-related running environment.

A code processing method includes: obtaining first code, where the first code is code that is obtained through compilation and that is applicable to a source platform; then relocating addresses of variables associated with functions in the first code, to obtain logical addresses of the variables; and then performing decompilation based on the logical addresses of the variables and the first code, to obtain second code applicable to a target platform.

A repository of graph based visual programming language code instances is analyzed. A similar code portion pattern duplicated is detected among a group of graph based visual programming language code instances included in the repository of graph based visual programming language code instances including by using an index and tokenizing one or more graph nodes connected by one or more graph edges included in a flow corresponding to at least one graph based visual programming language code instance in the group of graph based visual programming language code instances. Within a visual representation of at least one of the group of graph based visual programming language code instances, elements belonging to the detected similar code portion pattern are visually indicated.

An automated system for resolving program merges uses a multi-task neural transformer with attention. Each component of a merge conflict tuple (A, B, O) is represented as an AST and transformed into aligned AST-node sequences and aligned editing sequences. The multi-task neural transformer model predicts the tree editing steps needed to resolve the merge conflict and applies them to the AST representation of the code base. The tree editing steps include the edit actions that needed to be applied to the AST of the code base and the edit labels that are inserted or updated with the edit actions.

An example apparatus includes a dependency graph generator to generate a dependency graph based on a resource request file specifying a first resource and a second resource to deploy to a resource-based service, the dependency graph representative of the first resource being dependent on a second resource, a verification controller to generate a status indicator after a determination that a time-based ordering of a first request relative to a second request satisfies the dependency graph, and a resource controller to cause transmission of the first request and the second request to the resource-based service based on the dependency graph, and, after determining that the time-based ordering of the first request relative to the second request satisfies the dependency graph, cause transmission of the status indicator to a user device.

Techniques are described herein for training a machine learning model and using the trained machine learning model to more accurately determine alignments between matching/corresponding nodes of predecessor and successor graphs representing predecessor and successor source code snippets. A method includes: obtaining a first abstract syntax tree that represents a predecessor source code snippet and a second abstract syntax tree that represents a successor source code snippet; determining a mapping across the first and second abstract syntax trees; obtaining a first control-flow graph that represents the predecessor source code snippet and a second control-flow graph that represents the successor source code snippet; aligning blocks in the first control-flow graph with blocks in the second control-flow graph; and applying the aligned blocks as inputs across a trained machine learning model to generate an alignment of nodes in the first abstract syntax tree with nodes in the second abstract syntax tree.