Embodiments for providing automated machine learning visualization. Machine learning tasks, transformers, and estimators may be received into one or more machine learning composition modules. The machine learning composition modules generate one or more machine learning models. A machine learning model pipeline is a sequence of transformers and estimators and an ensemble of machine learning pipelines are an ensemble of machine learning pipelines. A machine learning model pipeline, an ensemble of a plurality of machine learning model pipelines, or a combination thereof, along with corresponding metadata, may be generated using the machine learning composition modules. Metadata may be extracted from the machine learning model pipeline, the ensemble of a plurality of machine learning model pipelines, or combination thereof. An interactive visualization graphical user interface of the machine learning model pipeline, the ensemble of a plurality of machine learning model pipelines, or combination thereof, and the extracted metadata may be generated.

A storage device is disclosed. The storage device may include compute engines. The compute engines may include storage for data, a storage processing unit to manage writing data to the storage and reading data from the storage, a data processing unit to perform some functions on the data, and an accelerator to perform other functions on the data. An Ethernet component may receive a request at the storage device from a host over a network. A data processing coordinator may process the request using a compute engine.

A front-end portion of a pipeline includes a stage that speculatively issues at least some instructions out-of-order. A back-end portion of the pipeline includes one or more stages that access a processor memory system. In the front-end (back-end), execution of instructions is managed based on information available in the front-end (back-end). Managing execution of a first memory barrier instruction includes preventing speculative out-of-order issuance of store instructions. The back-end control circuitry provides information accessible to the front-end control circuitry indicating that one or more particular memory instructions have completed handling by the processor memory system. The front-end control circuitry identifies one or more load instructions that were issued before the first memory barrier instruction was issued and are ordered after the first memory barrier instruction, and causes at least one of the identified load instructions to be reissued after the first memory barrier instruction has been issued.

An apparatus includes a CPU core, a first cache subsystem coupled to the CPU core, and a second memory coupled to the cache subsystem. The first cache subsystem includes a configuration register, a first memory, and a controller. The controller is configured to: receive a request directed to an address in the second memory and, in response to the configuration register having a first value, operate in a non-caching mode. In the non-caching mode, the controller is configured to provide the request to the second memory without caching data returned by the request in the first memory. In response to the configuration register having a second value, the controller is configured to operate in a caching mode. In the caching mode the controller is configured to provide the request to the second memory and cache data returned by the request in the first memory.

One embodiment of a computer-implemented method for compiling a material graph into a set of instructions for execution within an execution unit includes receiving a first material graph having a plurality of nodes, wherein each node included in the plurality of nodes represents a different surface property of a material; parsing the material graph to generate an expression tree that includes one or more expressions for each node included in the plurality of nodes; and generating a set of byte code instructions corresponding to the material graph based on the expression tree, wherein the byte code instructions are executable by a plurality of processing cores included within the execution unit.

Devices and techniques for thread-based processor halting are described herein. A processor monitors control-status register (CSR) values that correspond to a halt condition for a thread. The processor then compares the halt condition to a current state of the thread and halts in response to the current state of the thread meeting the halt condition.

An extensible processor can include an execution pipeline, one or more extensible control engines and architectural visible control states. The extensible processor can be configured to determine a control state of the one or more extensible control engines from the architectural visible control states. The extensible processor can be further configured to initiate execution of a given one of the extensible control engines when a control state in the architectural visible control states corresponding to the given one of the extensible control engines is enabled, wherein the given one of the extensible control engines comprises control input and control outputs based on one or more control transitions of an instruction. The extensible processor can also be further configured to output a result of execution of the given one of the extensible control engines to the architectural visible control states.

In an example, an apparatus comprises a plurality of execution units, and logic, at least partially including hardware logic, to assemble a general register file (GRF) message and hold the GRF message in storage in a data port until all data for the GRF message is received. Other embodiments are also disclosed and claimed.

A method of activating scheduling instructions within a parallel processing unit is described. The method comprises decoding, in an instruction decoder, an instruction in a scheduled task in an active state and checking, by an instruction controller, if a swap flag is set in the decoded instruction. If the swap flag in the decoded instruction is set, a scheduler is triggered to de-activate the scheduled task by changing the scheduled task from the active state to a non-active state.

This invention is a streaming engine employed in a digital signal processor. A fixed data stream sequence is specified by a control register. The streaming engine fetches stream data ahead of use by a central processing unit and stores it in a stream buffer. Upon occurrence of a fault reading data from memory, the streaming engine identifies the data element triggering the fault preferably storing this address in a fault address register. The streaming engine defers signaling the fault to the central processing unit until this data element is used as an operand. If the data element is never used by the central processing unit, the streaming engine never signals the fault. The streaming engine preferably stores data identifying the fault in a fault source register. The fault address register and the fault source register are preferably extended control registers accessible only via a debugger.