Abstract: Apparatuses, systems, and techniques to cause a first tensor to be translated into a second tensor according to a tensor map. In at least one embodiment, one or more circuits are to perform an application programming interface (API) to cause a first tensor to be translated into a second tensor according to a tensor map.

Abstract: Apparatuses, systems, and techniques to indicate how to generate image-to-column transformations. In at least one embodiment, one or more circuits are to perform an application programming interface (API) to indicate how to generate one or more image-to-column transformations.

Abstract: Apparatuses, systems, and techniques to cause a first tensor to be translated into a second tensor according to a tensor map without storing information about a memory transaction corresponding to the translation. In at least one embodiment, one or more circuits are to perform an application programming interface (API) to cause a first tensor to be translated into a second tensor according to a tensor map without storing information about one or more memory transactions corresponding to the translation.

Abstract: Apparatuses, systems, and techniques to access a multidimensional tensor from memory while minimizing tile quantization is disclosed. In at least one embodiment, a processor includes one or more circuits to cause a first one or more portions of at least one tensor to be accessed from a memory using a first technique and a second one or more portions of the at least one tensor to be accessed from the memory using a second technique based, at least in part, on an input to combine the first and second techniques.

Abstract: A system, method, and computer program product are provided for redistributing multi-sample processing workloads between threads. A workload for a plurality of multi-sample pixels is received and each thread in a parallel thread group is associated with a corresponding multi-sample pixel of the plurality of pixels. The workload is redistributed between the threads in the parallel thread group based on a characteristic of the workload and the workload is processed by the parallel thread group. In one embodiment, the characteristic is rasterized coverage information for the plurality of multi-sample pixels.

Abstract: A system, method, and computer program product are provided for accessing multi-sample surfaces. A multi-sample store instruction that specifies data for a single sample of a multi-sample pixel and a sample mask is received and the data for the single sample is stored to each sample of the multi-sample pixel that is enabled according to the sample mask. A multi-sample load instruction that specifies a multi-sample pixel is received, and, in response to executing the multi-sample load instruction, data for one sample of the multi-sample pixel is received. A determination is made that the data for the one sample of the multi-sample pixel represents multi-sample pixel data for at least one additional sample of the multi-sample pixel.

Abstract: A system, method, and computer program product are provided for multi-sample processing. The multi-sample pixel data is received and is analyzed to identify subsets of samples of a multi-sample pixel that have equal data, such that data for one sample in a subset represents multi-sample pixel data for all samples in the subset. An encoding state is generated that indicates which samples of the multi-sample pixel are included in each one of the subsets.

Abstract: A system, method, and computer program product are provided for multi-sample processing. The multi-sample pixel data is received and an encoding state associated with the multi-sample pixel data is determined. Data for one sample of a multi-sample pixel and the encoding state are provided to a processing unit. The one sample of the multi-sample pixel is processed by the processing unit to generate processed data for the one sample that represents processed multi-sample pixel data for all samples of the multi-sample pixel or two or more samples of the multi-sample pixel.

Abstract: A system, method, and computer program product are provided for redistributing multi-sample processing workloads between threads. A workload for a plurality of multi-sample pixels is received and each thread in a parallel thread group is associated with a corresponding multi-sample pixel of the plurality of pixels. The workload is redistributed between the threads in the parallel thread group based on a characteristic of the workload and the workload is processed by the parallel thread group. In one embodiment, the characteristic is rasterized coverage information for the plurality of multi-sample pixels.

Abstract: A system, method, and computer program product are provided for accessing multi-sample surfaces. A multi-sample store instruction that specifies data for a single sample of a multi-sample pixel and a sample mask is received and the data for the single sample is stored to each sample of the multi-sample pixel that is enabled according to the sample mask. A multi-sample load instruction that specifies a multi-sample pixel is received, and, in response to executing the multi-sample load instruction, data for one sample of the multi-sample pixel is received. A determination is made that the data for the one sample of the multi-sample pixel represents multi-sample pixel data for at least one additional sample of the multi-sample pixel.

Abstract: A system, method, and computer program product are provided for multi-sample processing. The multi-sample pixel data is received and an encoding state associated with the multi-sample pixel data is determined. Data for one sample of a multi-sample pixel and the encoding state are provided to a processing unit. The one sample of the multi-sample pixel is processed by the processing unit to generate processed data for the one sample that represents processed multi-sample pixel data for all samples of the multi-sample pixel or two or more samples of the multi-sample pixel.

Abstract: A system, method, and computer program product are provided for multi-sample processing. The multi-sample pixel data is received and is analyzed to identify subsets of samples of a multi-sample pixel that have equal data, such that data for one sample in a subset represents multi-sample pixel data for all samples in the subset. An encoding state is generated that indicates which samples of the multi-sample pixel are included in each one of the subsets.

Abstract: A scalable pipelined pixel shader that processes packets of data and preserves the format of each packet at each processing stage. Each packet is an ordered array of data values, at least one of which is an instruction pointer. Each member of the ordered array can be indicative of any type of data. As a packet progresses through the pixel shader during processing, each member of the ordered array can be replaced by a sequence of data values indicative of different types of data (e.g., an address of a texel, a texel, or a partially or fully processed color value). Information required for the pixel shader to process each packet is contained in the packet, and thus the pixel shader is scalable in the sense that it can be implemented in modular fashion to include any number of identical pipelined processing stages and can execute the same program regardless of the number of stages.