Computing systems, for example, multi-tenant systems deploy software artifacts in data centers created in a cloud platform using a cloud platform infrastructure language that is cloud platform independent. The system generates pipelines for deploying software artifacts in data center entities configured in a cloud platform. The system allows partial execution of pipelines such that the pipeline can be executed again to complete execution of the remaining stages. The system maintains state of the pipeline execution and checks the state to determine whether a stage should be executed during subsequent executions. The system allows a failed stage to be retried multiple times based on a retry strategy. A retry strategy may depend on the data center entity in a hierarchy of data venter entities of a data center.

Representative apparatus, method, and system embodiments are disclosed for a self-scheduling processor which also provides additional functionality. Representative embodiments include a self-scheduling processor, comprising: a processor core adapted to execute a received instruction; and a core control circuit adapted to automatically schedule an instruction for execution by the processor core in response to a received work descriptor data packet. In another embodiment, the core control circuit is also adapted to schedule a fiber create instruction for execution by the processor core, to reserve a predetermined amount of memory space in a thread control memory to store return arguments, and to generate one or more work descriptor data packets to another processor or hybrid threading fabric circuit for execution of a corresponding plurality of execution threads. Event processing, data path management, system calls, memory requests, and other new instructions are also disclosed.

A processing section executes processes concerning a plurality of applications in a time division manner. A CSDMA engine detects a switching timing of an application to be executed in the processing section. When detecting the switching timing, the CSDMA engine saves a context of an application that is being executed in the processing section 46, to a main memory from the processing section, and installs a context of an application to be subsequently executed in the processing section, from the main memory to the processing section, not through a process by software managing the plurality of applications.

The present disclosure concerns a method to train, on a computing device, a machine-learning model adapted to determine a dysfunction of a monitored vehicle electronic control unit (ECU) or vehicle embedded computer. In aspects, the computing device stores, in a memory, historical data from a plurality of ECUs having a dysfunction. The historical data may include usage values over a period of time of at least one ECU resource by applications running on the ECUs. Further, the computing device may process the historical data to obtain two-dimensional training files. In implementations, each usage value may be linked with a specific application in a first dimension and a specific time in a second dimension. Still further, the computing device may train a machine-learning model with the training files.

A processor includes a register file having a plurality of register file addresses, a processing unit, configured to perform processing in accordance with a configuration defined by information stored in the register file, and an instruction sequencer. The instruction sequencer is configured to control the processing unit by retrieving a sequence of instructions from a memory, in which each instruction includes an opcode, and a subset of the instructions includes a data portion. For each instruction in the sequence of instructions, the instruction sequencer performs an action defined by the opcode. The action for the subset of the opcodes includes writing the data portion to a register file address defined by the opcode. The sequence of instructions includes variable length instructions.

For certain applications, parts of the application data held in memory of a processing device (e.g. that are produced as a result of operations performed by the execution unit) are arranged in regular repeating patterns in the memory, and therefore, the execution unit may set up a suitable striding pattern for use by a send engine. The send engine accesses the memory at locations in accordance with the configured striding pattern so as to access a plurality of items of data that are arranged together in a regular pattern. In a similar manner as done for sends, the execution may set up a striding pattern for use by a receive engine. The receive engine, upon receiving a plurality of items of data, causes those items of data to be stored at locations in the memory, as determined in accordance with the configured striding pattern.

This disclosure provides systems, devices, apparatus and methods, including computer programs encoded on storage media, for GPR optimization in a GPU based on a GPR release mechanism. More specifically, a GPU may determine at least one unutilized branch within an executable shader based on constants defined for the executable shader. Based on the at least one unutilized branch, the GPU may further determine a number of GPRs that can be deallocated from previously allocated GPRs. The GPU may deallocate, for a subsequent thread within a draw call, the number of GPRs from the previously allocated GPRs during execution of the executable shader based on the determined number of GPRs to be deallocated.

There is provided a data processing apparatus comprising a plurality of registers, each of the registers having data bits to store data and metadata bits to store metadata. Each of the registers is adapted to operate in a metadata mode in which the metadata bits and the data bits are valid, and a data mode in which the data bits are valid and the metadata bits are invalid. Mode bit storage circuitry indicates whether each of the registers is in the data mode or the metadata mode. Execution circuitry is responsive to a memory operation that is a store operation on one or more given registers.

A system and method for a virtual processor base/virtual execution context arrangement. The disclosed arrangement utilizes chiplets comprising core logic and defined instruction sets. The chiplets are adapted to operate in conjunction with one or more active execution contexts to enable the execution of particular processes. In particular, the defined instruction sets includes a instructions for processor debugging. The system and method support the compartmentalization of such debugging instructions so as to provide enhanced processor and process security.

This disclosure provides systems, devices, apparatus and methods, including computer programs encoded on storage media, for GPR optimization in a GPU based on a GPR release mechanism. More specifically, a GPU may determine at least one unutilized branch within an executable shader based on constants defined for the executable shader. Based on the at least one unutilized branch, the GPU may further determine a number of GPRs that can be deallocated from previously allocated GPRs. The GPU may deallocate, for a subsequent thread within a draw call, the number of GPRs from the previously allocated GPRs during execution of the executable shader based on the determined number of GPRs to be deallocated.