Abstract: Rendering system combines point sampling and volume sampling operations to produce rendering outputs. For example, to determine color information for a surface location in a 3-D scene, one or more point sampling operations are conducted in a volume around the surface location, and one or more sampling operations of volumetric light transport data are performed farther from the surface location. A transition zone between point sampling and volume sampling can be provided, in which both point and volume sampling operations are conducted. Data obtained from point and volume sampling operations can be blended in determining color information for the surface location. For example, point samples are obtained by tracing a ray for each point sample, to identify an intersection between another surface and the ray, to be shaded, and volume samples are obtained from a nested 3-D grids of volume elements expressing light transport data at different levels of granularity.

Abstract: Rendering system combines point sampling and volume sampling operations to produce rendering outputs. For example, to determine color information for a surface location in a 3-D scene, one or more point sampling operations are conducted in a volume around the surface location, and one or more sampling operations of volumetric light transport data are performed farther from the surface location. A transition zone between point sampling and volume sampling can be provided, in which both point and volume sampling operations are conducted. Data obtained from point and volume sampling operations can be blended in determining color information for the surface location. For example, point samples are obtained by tracing a ray for each point sample, to identify an intersection between another surface and the ray, to be shaded, and volume samples are obtained from a nested 3-D grids of volume elements expressing light transport data at different levels of granularity.

Abstract: Aspects include a multistage collector to receive outputs from plural processing elements. Processing elements may comprise (each or collectively) a plurality of clusters, with one or more ALUs that may perform SIMD operations on a data vector and produce outputs according to the instruction stream being used to configure the ALU(s). The multistage collector includes substituent components each with at least one input queue, a memory, a packing unit, and an output queue; these components can be sized to process groups of input elements of a given size, and can have multiple input queues and a single output queue. Some components couple to receive outputs from the ALUs and others receive outputs from other components. Ultimately, the multistage collector can output groupings of input elements. Each grouping of elements (e.g., at input queues, or stored in the memories of component) can be formed based on matching of index elements.

Abstract: A graphics processor architecture provides for scan conversion and ray tracing approaches to visible surface determination as concurrent and separate processes. Surfaces can be identified for shading by scan conversion and ray tracing. Data produced by each can be normalized, so that instances of shaders, being executed on a unified shading computation resource, can shade surfaces originating from both ray tracing and rasterization. Such resource also may execute geometry shaders. The shaders can emit rays to be tested for intersection by the ray tracing process. Such shaders can complete, without waiting for those emitted rays to complete. Where scan conversion operates on tiles of 2-D screen pixels, the ray tracing can be tile aware, and controlled to prioritize testing of rays based on scan conversion status. Ray population can be controlled by feedback to any of scan conversion, and shading.

Abstract: Aspects include a multistage collector to receive outputs from plural processing elements. Processing elements may comprise (each or collectively) a plurality of clusters, with one or more ALUs that may perform SIMD operations on a data vector and produce outputs according to the instruction stream being used to configure the ALU(s). The multistage collector includes substituent components each with at least one input queue, a memory, a packing unit, and an output queue; these components can be sized to process groups of input elements of a given size, and can have multiple input queues and a single output queue. Some components couple to receive outputs from the ALUs and others receive outputs from other components. Ultimately, the multistage collector can output groupings of input elements. Each grouping of elements (e.g., at input queues, or stored in the memories of component) can be formed based on matching of index elements.

Abstract: A graphics processor architecture provides for scan conversion and ray tracing approaches to visible surface determination as concurrent and separate processes. Surfaces can be identified for shading by scan conversion and ray tracing. Data produced by each can be normalized, so that instances of shaders, being executed on a unified shading computation resource, can shade surfaces originating from both ray tracing and rasterization. Such resource also may execute geometry shaders. The shaders can emit rays to be tested for intersection by the ray tracing process. Such shaders can complete, without waiting for those emitted rays to complete. Where scan conversion operates on tiles of 2-D screen pixels, the ray tracing can be tile aware, and controlled to prioritize testing of rays based on scan conversion status. Ray population can be controlled by feedback to any of scan conversion, and shading.

Abstract: Aspects include computation systems that can identify computation instances that are not capable of being reentrant, or are not reentrant capable on a target architecture, or are non-reentrant as a result of having a memory conflict in a particular execution situation. For example, a system can have a plurality of computation units, each with an independently schedulable SIMD vector. Computation instances can be defined by a program module, and a data element(s) that may be stored in a local cache for a particular computation unit of the plurality. Each local cache does not maintain coherency controls for such data elements. During scheduling, a scheduler can maintain a list of running (or runnable) instances, and attempt to schedule new computation instances by determining whether any new computation instance conflicts with a running instance and responsively defer scheduling. Such memory conflict checks can be conditioned on a flag or other indication of the potential for non-reentrancy.

Abstract: Aspects include computation systems that can identify computation instances that are not capable of being reentrant, or are not reentrant capable on a target architecture, or are non-reentrant as a result of having a memory conflict in a particular execution situation. A system can have a plurality of computation units, each with an independently schedulable SIMD vector. Computation instances can be defined by a program module, and a data element(s) that may be stored in a local cache for a particular computation unit. Each local cache does not maintain coherency controls for such data elements. During scheduling, a scheduler can maintain a list of running (or runnable) instances, and attempt to schedule new computation instances by determining whether any new computation instance conflicts with a running instance and responsively defer scheduling. Memory conflict checks can be conditioned on a flag or other indication of the potential for non-reentrancy.

Abstract: Aspects can be for ray tracing of 3-D scenes, and include dynamically controlling a population of rays being stored in a memory, to keep the population within a target, a memory footprint or other resource usage specification. An example includes controlling the population by examining indicia associated with rays returning from intersection testing, to be shaded, the indicia correlated with behavior of shaders to be run for those rays, such that population control selects, or reorders rays for shading, to prioritize shading of rays whose shaders are expected to produce fewer rays.

Abstract: Rendering system combines point sampling and volume sampling operations to produce rendering outputs. For example, to determine color information for a surface location in a 3-D scene, one or more point sampling operations are conducted in a volume around the surface location, and one or more sampling operations of volumetric light transport data are performed farther from the surface location. A transition zone between point sampling and volume sampling can be provided, in which both point and volume sampling operations are conducted. Data obtained from point and volume sampling operations can be blended in determining color information for the surface location. For example, point samples are obtained by tracing a ray for each point sample, to identify an intersection between another surface and the ray, to be shaded, and volume samples are obtained from a nested 3-D grids of volume elements expressing light transport data at different levels of granularity.

Abstract: Rendering system combines point sampling and volume sampling operations to produce rendering outputs. For example, to determine color information for a surface location in a 3-D scene, one or more point sampling operations are conducted in a volume around the surface location, and one or more sampling operations of volumetric light transport data are performed farther from the surface location. A transition zone between point sampling and volume sampling can be provided, in which both point and volume sampling operations are conducted. Data obtained from point and volume sampling operations can be blended in determining color information for the surface location. For example, point samples are obtained by tracing a ray for each point sample, to identify an intersection between another surface and the ray, to be shaded, and volume samples are obtained from a nested 3-D grids of volume elements expressing light transport data at different levels of granularity.

Abstract: Aspects include a multistage collector to receive outputs from plural processing elements. Processing elements may comprise (each or collectively) a plurality of clusters, with one or more ALUs that may perform SIMD operations on a data vector and produce outputs according to the instruction stream being used to configure the ALU(s). The multistage collector includes substituent components each with at least one input queue, a memory, a packing unit, and an output queue; these components can be sized to process groups of input elements of a given size, and can have multiple input queues and a single output queue. Some components couple to receive outputs from the ALUs and others receive outputs from other components. Ultimately, the multistage collector can output groupings of input elements. Each grouping of elements (e.g., at input queues, or stored in the memories of component) can be formed based on matching of index elements.

Abstract: Aspects include, for example, a method for interpreting information in a computer program, or profiling such a program to estimate a group size for instances of that program (program module, or portion thereof). Such a method can be used in a system that supports collecting outputs of executing instances, where those outputs can specify new program instances. Scheduling of new instances (or allocation of resources for executing such instances) can be deferred. A trigger to begin scheduling (or allocation) for a collection of instances uses a target group size for that program. Thus, different programs can have different group sizes, which can be set explicitly, or based on profiling. The profiling can occur during one or more of pre-execution and during execution. The group size estimate can be an input into an algorithm that also accounts for system state during execution.

Abstract: Aspects include a multistage collector to receive outputs from plural processing elements. Processing elements may comprise (each or collectively) a plurality of clusters, with one or more ALUs that may perform SIMD operations on a data vector and produce outputs according to the instruction stream being used to configure the ALU(s). The multistage collector includes substituent components each with at least one input queue, a memory, a packing unit, and an output queue; these components can be sized to process groups of input elements of a given size, and can have multiple input queues and a single output queue. Some components couple to receive outputs from the ALUs and others receive outputs from other components. Ultimately, the multistage collector can output groupings of input elements. Each grouping of elements (e.g., at input queues, or stored in the memories of component) can be formed based on matching of index elements.

Abstract: In some aspects, finer grained parallelism is achieved by segmenting programmatic workloads into smaller discretized portions, where a first element can be indicative both of a configuration or program to be executed, and a first data set to be used in such execution, while a second element can be indicative of a second data element or group. The discretized portions can cause program execute on distributed processors. Approaches to selecting processors, and allocating local memory associated with those processors are disclosed. In one example, discretized portions that share a program have an anti-affinity to cause dispersion, for initial execution assignment. Flags, such as programmer and compiler generated flags can be used in determining such allocations. Workloads can be grouped according to compatibility of memory usage requirements.

Abstract: In some aspects, finer grained parallelism is achieved by segmenting programmatic workloads into smaller discretized portions, where a first element can be indicative both of a configuration or program to be executed, and a first data set to be used in such execution, while a second element can be indicative of a second data element or group. The discretized portions can cause program execute on distributed processors. Approaches to selecting processors, and allocating local memory associated with those processors are disclosed. In one example, discretized portions that share a program have an anti-affinity to cause dispersion, for initial execution assignment. Flags, such as programmer and compiler generated flags can be used in determining such allocations. Workloads can be grouped according to compatibility of memory usage requirements.

Abstract: A graphics processor architecture provides for scan conversion and ray tracing approaches to visible surface determination as concurrent and separate processes. Surfaces can be identified for shading by scan conversion and ray tracing. Data produced by each can be normalized, so that instances of shaders, being executed on a unified shading computation resource, can shade surfaces originating from both ray tracing and rasterization. Such resource also may execute geometry shaders. The shaders can emit rays to be tested for intersection by the ray tracing process. Such shaders can complete, without waiting for those emitted rays to complete. Where scan conversion operates on tiles of 2-D screen pixels, the ray tracing can be tile aware, and controlled to prioritize testing of rays based on scan conversion status. Ray population can be controlled by feedback to any of scan conversion, and shading.

Abstract: A graphics processor architecture provides for scan conversion and ray tracing approaches to visible surface determination as concurrent and separate processes. Surfaces can be identified for shading by scan conversion and ray tracing. Data produced by each can be normalized, so that instances of shaders, being executed on a unified shading computation resource, can shade surfaces originating from both ray tracing and rasterization. Such resource also may execute geometry shaders. The shaders can emit rays to be tested for intersection by the ray tracing process. Such shaders can complete, without waiting for those emitted rays to complete. Where scan conversion operates on tiles of 2-D screen pixels, the ray tracing can be tile aware, and controlled to prioritize testing of rays based on scan conversion status. Ray population can be controlled by feedback to any of scan conversion, and shading.

Abstract: Aspects can be for ray tracing of 3-D scenes, and include dynamically controlling a population of rays being stored in a memory, to keep the population within a target, a memory footprint or other resource usage specification. An example includes controlling the population by examining indicia associated with rays returning from intersection testing, to be shaded, the indicia correlated with behavior of shaders to be run for those rays, such that population control selects, or reorders rays for shading, to prioritize shading of rays whose shaders are expected to produce fewer rays.

Abstract: Aspects can be for ray tracing of 3-D scenes, and include dynamically controlling a population of rays being stored in a memory, to keep the population within a target, a memory footprint or other resource usage specification. An example includes controlling the population by examining indicia associated with rays returning from intersection testing, to be shaded, the indicia correlated with behavior of shaders to be run for those rays, such that population control selects, or reorders rays for shading, to prioritize shading of rays whose shaders are expected to produce fewer rays.