A system including a machine learning accelerator (MLA) hardware configured to perform machine-learning operations according to native instructions; an interpreter computing module configured to: generate, based on virtual instructions, machine language instructions configured to be processed by a processing hardware implementing the interpreter computing module; and cause the processing hardware to perform machine-learning operations according to the machine language instructions; and a compiler computing module associated with the MLA hardware, the compiler computing module configured to: receive instructions for performing an inference using a machine-learning model; based on the received instructions: generate the native instructions configured to be processed by the MLA hardware, the native instructions specifying first machine-learning operations associated with performing the inference; and generate the virtual instructions configured to be processed by the interpreter computing module, the virtual instructions specifying second machine-learning operations associated with performing the inference.

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.

Provided is a consumable chip and a communication method thereof. The method is adopted for a consumable chip including a first consumable MCU unit, a verification cryptographic operation unit and a second consumable MCU unit; the method includes: when the first consumable MCU unit is incompatible with the printer, the first consumable MCU unit activating the second consumable MCU unit to execute a second consumable chip core processing flow, and calling the verification cryptographic algorithm function program of the verification cryptographic operation unit to calculate a new verification password; after the first consumable MCU unit inquires that the second consumable MCU unit has completed the second consumable chip core processing flow, the first consumable MCU unit reading the new verification password and sending to the printer, the new verification password is received by the printer to achieve compatibility between the first consumable MCU unit and the printer.

Embodiments disclosed herein provide for systems and methods for scanning application code to determine cloud platform portability. The systems and methods provide for a rules processing engine configured to perform a portability analysis on the application code and a list of cloud platform dependencies, and returns a score indicating the “stickiness” of the application code to a particular cloud platform.

A computer-implemented data processing method, including the steps of: providing a first program including a group of operations arranged to satisfy a first set of operation dependencies, the group of operations being adapted for computing data from at least one data source, generating a second program including the group of operations, arranged to satisfy a second set of operation dependencies, and processing the data from the at least one data source with the second program. The group of operations includes a first operation, a second operation, and a third operation. The first set of operation dependencies includes a first dependency between the first operation and the second operation, a second dependency between the first operation and the third operation, and a third dependency between the second operation and the third operation.

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 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.

Abstract interpretation based static analysis tools use relational/non-relational abstract domains to verify program properties. Precision and scalability of analysis vary basis usage of abstract domains. K-limited path-sensitive interval domain is an abstract domain that was conventionally proposed for analysis on industry strength programs. The domain maintains variables' intervals along a configurable K subsets of paths at each program point, which implicitly provides co-relation among variables. When the number of paths at the join point exceeds K, set of paths are partitioned into K subsets, arbitrarily, which results in loss of precision required to verify program properties. To address the above problem, embodiments of the present disclosure provide selective merging of paths in such a way that the intervals computed help verifying more properties. The selective path-sensitive method of the present disclosure is based on the knowledge of variables whose values influence the verification outcome of program properties.

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.