Abstract: Graphics processing unit (GPU) performance and power efficiency is improved using machine learning to tune operating parameters based on performance monitor values and application information. Performance monitor values are processed using machine learning techniques to generate model parameters, which are used by a control unit within the GPU to provide real-time updates to the operating parameters. In one embodiment, a neural network processes the performance monitor values to generate operating parameters in real-time.

Abstract: A display device for measuring the end-to-end latency of a computing system. The computing system includes an input device, a computing device, and the display device. The display device is directly connected with the input device and receives input data packets generated by the input device in response to received user input events. The display device passes the input packets to the computing device for graphics processing. The display device measures the end-to-end latency comprising the sum of three latencies. A first latency comprises an input delay of the input device. A second latency comprises an amount of time between generation of the input packet and a corresponding change in pixel values caused by the input event at the display device. A third latency comprises a display latency. The display device also displays latency information associated with the measured end-to-end latency.

Abstract: Graphics processing unit (GPU) performance and power efficiency is improved using machine learning to tune operating parameters based on performance monitor values and application information. Performance monitor values are processed using machine learning techniques to generate model parameters, which are used by a control unit within the GPU to provide real-time updates to the operating parameters. In one embodiment, a neural network processes the performance monitor values to generate operating parameters in real-time.

Abstract: Graphics processing unit (GPU) performance and power efficiency is improved using machine learning to tune operating parameters based on performance monitor values and application information. Performance monitor values are processed using machine learning techniques to generate model parameters, which are used by a control unit within the GPU to provide real-time updates to the operating parameters. In one embodiment, a neural network processes the performance monitor values to generate operating parameters in real-time.

Abstract: A display device for measuring the end-to-end latency of a computing system. The computing system includes an input device, a computing device, and the display device. The display device is directly connected with the input device and receives input data packets generated by the input device in response to received user input events. The display device passes the input packets to the computing device for graphics processing. The display device measures the end-to-end latency comprising the sum of three latencies. A first latency comprises an input delay of the input device. A second latency comprises an amount of time between generation of the input packet and a corresponding change in pixel values caused by the input event at the display device. A third latency comprises a display latency. The display device also displays latency information associated with the measured end-to-end latency.

Abstract: Graphics processing unit (GPU) performance and power efficiency is improved using machine learning to tune operating parameters based on performance monitor values and application information. Performance monitor values are processed using machine learning techniques to generate model parameters, which are used by a control unit within the GPU to provide real-time updates to the operating parameters. In one embodiment, a neural network processes the performance monitor values to generate operating parameters in real-time.

Abstract: Graphics processing unit (GPU) performance and power efficiency is improved using machine learning to tune operating parameters based on performance monitor values and application information. Performance monitor values are processed using machine learning techniques to generate model parameters, which are used by a control unit within the GPU to provide real-time updates to the operating parameters. In one embodiment, a neural network processes the performance monitor values to generate operating parameters in real-time.

Abstract: One or more copy commands are scheduled for locating one or more pages of data in a local memory of a graphics processing unit (GPU) for more efficient access to the pages of data during rendering. A first processing unit that is coupled to a first GPU receives a notification that an access request count has reached a specified threshold. The first processing unit schedules a copy command to copy the first page of data to a first memory circuit of the first GPU from a second memory circuit of the second GPU. The copy command is included within a GPU command stream.

Abstract: A method for rendering graphics frames allocates rendering work to multiple graphics processing units (GPUs) that are configured to allow access to pages of data stored in locally attached memory of a peer GPU. The method includes the steps of generating, by a first GPU coupled to a first memory circuit, one or more first memory access requests to render a first primitive for a first frame, where at least one of the first memory access requests targets a first page of data that physically resides within a second memory circuit coupled to a second GPU. The first GPU requests the first page of data through a first data link coupling the first GPU to the second GPU and a register circuit within the first GPU accumulates an access request count for the first page of data. The first GPU notifies a driver that the access request count has reached a specified threshold.

Abstract: Graphics processing unit (GPU) performance and power efficiency is improved using machine learning to tune operating parameters based on performance monitor values and application information. Performance monitor values are processed using machine learning techniques to generate model parameters, which are used by a control unit within the GPU to provide real-time updates to the operating parameters. In one embodiment, a neural network processes the performance monitor values to generate operating parameters in real-time.

Abstract: One or more copy commands are scheduled for locating one or more pages of data in a local memory of a graphics processing unit (GPU) for more efficient access to the pages of data during rendering. A first processing unit that is coupled to a first GPU receives a notification that an access request count has reached a specified threshold. The first processing unit schedules a copy command to copy the first page of data to a first memory circuit of the first GPU from a second memory circuit of the second GPU. The copy command is included within a GPU command stream.

Abstract: A method for rendering graphics frames allocates rendering work to multiple graphics processing units (GPUs) that are configured to allow access to pages of data stored in locally attached memory of a peer GPU. The method includes the steps of generating, by a first GPU coupled to a first memory circuit, one or more first memory access requests to render a first primitive for a first frame, where at least one of the first memory access requests targets a first page of data that physically resides within a second memory circuit coupled to a second GPU. The first GPU requests the first page of data through a first data link coupling the first GPU to the second GPU and a register circuit within the first GPU accumulates an access request count for the first page of data. The first GPU notifies a driver that the access request count has reached a specified threshold.

Abstract: A system, method, and computer program product are provided for shading primitive fragments. A target buffer may be recast when shaded samples that are covered by a primitive fragment are generated at a first shading rate using a first sampling mode, the shaded samples are stored in the target buffer that is associated with the first sampling mode and the first shading rate, a second sampling mode is determined, and the target buffer is associated with the second sampling mode. A sampling mode and/or shading rate may be changed for a primitive. A primitive fragment that is associated with a first sampling mode and a first shading rate is received and a second sampling mode is determined for the primitive fragment. Shaded samples corresponding to the primitive fragment are generated, at a second shading rate, using the second sampling mode and the shaded samples are stored in a target buffer.

Abstract: A system, method, and computer program product are provided for shading primitive fragments. A target buffer may be recast when shaded samples that are covered by a primitive fragment are generated at a first shading rate using a first sampling mode, the shaded samples are stored in the target buffer that is associated with the first sampling mode and the first shading rate, a second sampling mode is determined, and the target buffer is associated with the second sampling mode. A sampling mode and/or shading rate may be changed for a primitive. A primitive fragment that is associated with a first sampling mode and a first shading rate is received and a second sampling mode is determined for the primitive fragment. Shaded samples corresponding to the primitive fragment are generated, at a second shading rate, using the second sampling mode and the shaded samples are stored in a target buffer.

Abstract: A system, method, and computer program product are provided for a dynamic display refresh. In use, a state of a display device is identified in which an entirety of an image frame is currently displayed by the display device. In response to the identification of the state, it is determined whether an entirety of a next image frame to be displayed has been rendered to memory. The next image frame is transmitted to the display device for display thereof, when it is determined that the entirety of the next image frame to be displayed has been rendered to the memory. Further, a refresh of the display device is delayed, when it is determined that the entirety of the next image frame to be displayed has not been rendered to the memory.