Abstract:
Methods, systems, and devices for graphic processing are described. The methods, systems, and devices may include or be associated with identifying a graphics instruction, determining that the graphics instruction is alias enabled for the device, partitioning an alias lookup table into one or more slots, allocating a slot of the alias lookup table based on the partitioning and determining that the graphics instruction is alias enabled, generating an alias instruction based on allocating the slot of the alias lookup table and determining that the graphics instruction is alias enabled, and processing the alias instruction.
Abstract:
Systems and techniques are disclosed for general purpose register dynamic allocation based on latency associated with of instructions in processor threads. A streaming processor can include a general purpose registers configured to stored data associated with threads, and a thread scheduler configured to receive allocation information for the general purpose registers, the information describing general purpose registers that are to be assigned as persistent general purpose registers (pGPRs) and volatile general purpose registers (vGPRs). The plurality of general purpose registers can be allocated according to the received information. The streaming processor can include the general purpose registers allocated according to the received information, the allocated based on execution latencies of instructions included in the threads.
Abstract:
The present disclosure relates to methods and apparatus for compute processing. For example, disclosed techniques facilitate improving performance of matrix multiplication in streaming processor. Aspects of the present disclosure can execute, with a load control unit, a first load instruction to load a set of input data of an input matrix from a first memory to a second memory. Aspects of the present disclosure can also execute, with the load control unit, a second load instruction to load a set of weight data of a weight matrix from the first memory to the second memory. Additionally, aspects of the present disclosure can perform, with an ALU component, a matrix multiplication operation using the set of input data and the set of weight data to generate an output matrix. Further, aspects of the present disclosure can store the output matrix at a general purpose register accessible to the ALU component.
Abstract:
The present disclosure relates to methods and devices for graphics processing including an apparatus, e.g., a GPU. The apparatus may generate a table including a plurality of entries to store data associated with at least one of a constant value or an immediate value. The apparatus may also process, upon generating the table, first data including at least one of a constant value or an immediate value. Further, the apparatus may store, in the generated table, at least one of the constant value or the immediate value of the first data. The apparatus may also transmit, upon storing at least one of the constant value or the immediate value in the table, the table including the stored at least one of the constant value or the immediate value of the first data.
Abstract:
Systems and techniques are disclosed for general purpose register dynamic allocation based on latency associated with of instructions in processor threads. A streaming processor can include a general purpose registers configured to stored data associated with threads, and a thread scheduler configured to receive allocation information for the general purpose registers, the information describing general purpose registers that are to be assigned as persistent general purpose registers (pGPRs) and volatile general purpose registers (vGPRs). The plurality of general purpose registers can be allocated according to the received information. The streaming processor can include the general purpose registers allocated according to the received information, the allocated based on execution latencies of instructions included in the threads.
Abstract:
A method includes storing, with a first programmable processor, shared variable data to cache lines of a first cache of the first processor. The method further includes executing, with the first programmable processor, a store-with-release operation, executing, with a second programmable processor, a load-with-acquire operation, and loading, with the second programmable processor, the value of the shared variable data from a cache of the second programmable processor.
Abstract:
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for dynamic wave pairing. A graphics processor may allocate one or more GPU workloads to one or more wave slots of a plurality of wave slots. The graphics processor may select a first execution slot of a plurality of execution slots for executing the one or more GPU workloads. The selection may be based on one of a plurality of granularities. The graphics processor may execute, at the selected first execution slot, the one or more GPU workloads at the one of the plurality of granularities.
Abstract:
The present disclosure relates to methods and apparatus for compute processing. For example, disclosed techniques facilitate improving performance of matrix multiplication in streaming processor. Aspects of the present disclosure can execute, with a load control unit, a first load instruction to load a set of input data of an input matrix from a first memory to a second memory. Aspects of the present disclosure can also execute, with the load control unit, a second load instruction to load a set of weight data of a weight matrix from the first memory to the second memory. Additionally, aspects of the present disclosure can perform, with an ALU component, a matrix multiplication operation using the set of input data and the set of weight data to generate an output matrix. Further, aspects of the present disclosure can store the output matrix at a general purpose register accessible to the ALU component.
Abstract:
A graphics processing unit (GPU) utilizes block general purpose registers (bGPRs) to load multiple waves of samples for an instruction group into a processing pipeline and receive processed samples from the pipeline. The GPU acquires a credit for the bGPR for execution of the instruction group for a first wave using a persistent GPR and the bGPR. The GPU refunds the credit upon loading the first wave into the pipeline. The GPU executes a subsequent wave for the instruction group to load samples to the pipeline when at least one credit is available and the pipeline is processing the first wave. The GPU stores an indication of each wave that has been loaded into the pipeline in a queue. The GPU returns samples for a next wave in the queue from the pipeline to the bGPR for further processing when the physical slot of the bGPR is available.
Abstract:
Methods, systems, and devices for graphic processing are described. The methods, systems, and devices may include or be associated with identifying a graphics instruction, determining that the graphics instruction is alias enabled for the device, partitioning an alias lookup table into one or more slots, allocating a slot of the alias lookup table based on the partitioning and determining that the graphics instruction is alias enabled, generating an alias instruction based on allocating the slot of the alias lookup table and determining that the graphics instruction is alias enabled, and processing the alias instruction.