A system comprising a quantum control data exchange circuit that enables a large, variable number of pulse generation circuits to exchange data within the coherence time of a plurality of quantum elements to enable feedback-based quantum control of a large, variable number of quantum elements.

In various examples, a VPU and associated components may be optimized to improve VPU performance and throughput. For example, the VPU may include a min/max collector, automatic store predication functionality, a SIMD data path organization that allows for inter-lane sharing, a transposed load/store with stride parameter functionality, a load with permute and zero insertion functionality, hardware, logic, and memory layout functionality to allow for two point and two by two point lookups, and per memory bank load caching capabilities. In addition, decoupled accelerators may be used to offload VPU processing tasks to increase throughput and performance, and a hardware sequencer may be included in a DMA system to reduce programming complexity of the VPU and the DMA system. The DMA and VPU may execute a VPU configuration mode that allows the VPU and DMA to operate without a processing controller for performing dynamic region based data movement operations.

The present disclosure relates to a non-transitory computer-readable recording medium storing an analysis program that causes a computer to execute a process. The process includes sampling an instruction address of one of instructions included in a program during execution of the program, identifying a first function that includes the sampled instruction address in an address range, rewriting mark information associated with the identified first function, identifying first information corresponding to the instruction address of the first function among a plurality of first information based on the rewritten mark information, identifying second information corresponding to the instruction address of the first function among a plurality of second information based on the rewritten mark information, storing the first information and the second information in a memory, and analyzing performance of the program based on the first information and the second information stored in the memory.

In various examples, a VPU and associated components may be optimized to improve VPU performance and throughput. For example, the VPU may include a min/max collector, automatic store predication functionality, a SIMD data path organization that allows for inter-lane sharing, a transposed load/store with stride parameter functionality, a load with permute and zero insertion functionality, hardware, logic, and memory layout functionality to allow for two point and two by two point lookups, and per memory bank load caching capabilities. In addition, decoupled accelerators may be used to offload VPU processing tasks to increase throughput and performance, and a hardware sequencer may be included in a DMA system to reduce programming complexity of the VPU and the DMA system. The DMA and VPU may execute a VPU configuration mode that allows the VPU and DMA to operate without a processing controller for performing dynamic region based data movement operations.

In an embodiment, a data processing method comprises accessing a computer memory comprising a shareable cell-based computation notebook comprising: notebook metadata specifying a kernel for execution, and a computational cell comprising cell metadata, a source code reference, and an output reference, wherein the cell metadata identifies a particular version of source code of a function that defines an input dataset, a transformation, and one or more variables that are to be associated with output data that is to be generated as a result of executing the particular version of the source code; updating the source code reference to identify a first storage location that is to contain the particular version of the source code of the function; and updating the output reference to identify a second storage location that is to contain the output data that is to be generated as a result of executing the particular version of the source code identified in the cell metadata using the kernel specified in the notebook metadata, wherein the method is performed by one or more computing devices.

An input/output store instruction is handled. A data processing system includes a system nest coupled to at least one input/output bus by an input/output bus controller. The data processing system further includes at least a data processing unit including a core, system firmware and an asynchronous core-nest interface. The data processing unit is coupled to the system nest via an aggregation buffer. The system nest is configured to asynchronously load from and/or store data to at least one external device which is coupled to the at least one input/output bus. The data processing unit is configured to complete the input/output store instruction before an execution of the input/output store instruction in the system nest is completed. The asynchronous core-nest interface includes an input/output status array with multiple input/output status buffers. The system firmware includes a retry buffer and the core includes an analysis and retry logic.

An apparatus to facilitate machine learning matrix processing is disclosed. The apparatus comprises a memory to store matrix data one or more processors to execute an instruction to examine a message descriptor included in the instruction to determine a type of matrix layout manipulation operation that is to be executed, examine a message header included in the instruction having a plurality of parameters that define a two-dimensional (2D) memory surface that is to be retrieved, retrieve one or more blocks of the matrix data from the memory based on the plurality of parameters and a register file including a plurality of registers, wherein the one or more blocks of the matrix data is stored within a first set of the plurality of registers.

A digital data processor includes an instruction memory storing instructions specifying a data processing operation and a data operand field, an instruction decoder coupled to the instruction memory for recalling instructions from the instruction memory and determining the operation and the data operand, and an operational unit coupled to a data register file and to an instruction decoder to perform a data processing operation upon an operand corresponding to an instruction decoded by the instruction decoder and storing results of the data processing operation. The operational unit is configured to perform a table write in response to a look up table initialization instruction by duplicating at least one data element from a source data register to create duplicated data elements, and writing the duplicated data elements to a specified location in a specified number of at least one table and a corresponding location in at least one other table.

Described herein is a graphics processing unit (GPU) comprising a first processing cluster to perform parallel processing operations, the parallel processing operations including a ray tracing operation and a matrix multiply operation; and a second processing cluster coupled to the first processing cluster, wherein the first processing cluster includes a floating-point unit to perform floating point operations, the floating-point unit is configured to process an instruction using a bfloat16 (BF16) format with a multiplier to multiply second and third source operands while an accumulator adds a first source operand with output from the multiplier.

Embodiments detailed herein relate to matrix operations. In particular, the loading of a matrix (tile) from memory. For example, support for a loading instruction is described in the form of decode circuitry to decode an instruction having fields for an opcode, a destination matrix operand identifier, and source memory information, and execution circuitry to execute the decoded instruction to load groups of strided data elements from memory into configured rows of the identified destination matrix operand to memory.