Imagination Technologies Limited

Apple Inc.

Luca O Iuliano

Simon Thomas Nield

Yoong-Chert Foo

Adam De Grasse

Ollie Mower

Ali Rabbani Rankouhi

Thomas Michael Rose

Justin A Hensley

Jonathan M Redshaw

Christopher A Burns

Doron Rajwan

Jonathan Mark Redshaw

Methods and parallel processing units for avoiding inter-pipeline data hazards identified at compile time. For each identified inter-pipeline data hazard the primary instruction and secondary instruction(s) thereof are identified as such and are linked by a counter which is used to track that inter-pipeline data hazard. When a primary instruction is output by the instruction decoder for execution the value of the counter associated therewith is adjusted to indicate that there is hazard related to the primary instruction, and when primary instruction has been resolved by one of multiple parallel processing pipelines the value of the counter associated therewith is adjusted to indicate that the hazard related to the primary instruction has been resolved. When a secondary instruction is output by the decoder for execution, the secondary instruction is stalled in a queue associated with the appropriate instruction pipeline if at least one counter associated with the primary instructions from which it depends indicates that there is a hazard related to the primary instruction.

Methods and systems for inter-pipeline data hazard avoidance

A method of activating scheduling instructions within a parallel processing unit is described. The method comprises decoding, in an instruction decoder, an instruction in a scheduled task in an active state and checking, by an instruction controller, if a swap flag is set in the decoded instruction. If the swap flag in the decoded instruction is set, a scheduler is triggered to de-activate the scheduled task by changing the scheduled task from the active state to a non-active state.

Scheduling tasks using swap flags

Apparatus identifies a set of M output memory addresses from a larger set of N input memory addresses containing at least one non-unique memory address. A comparator block performs comparisons of memory addresses from a set of N input memory addresses to generate a binary classification dataset that identifies a subset of addresses from the set of input addresses, where each address in the subset identified by the binary classification dataset is unique within that subset. Combination logic units receive a predetermined selection of bits of the binary classification dataset and sort its received predetermined selection of bits into an intermediary binary string in which the bits are ordered into a first group identifying addresses belonging to the identified subset, and a second group identifying addresses not belonging to the identified subset. Output generating logic selects between bits belonging to different intermediary binary strings to generate a binary output identifying a set of output memory addresses containing at least one address in the identified subset.

Sorting memory address requests for parallel memory access using input address match masks

Disclosed techniques relate to acceleration data structure for ray intersection testing. In some embodiments, storage circuitry stores node data for a spatially organized acceleration data structure, including to store the following node information for a given node: origin coordinates for the node and, for a given child node of multiple child nodes, child information that includes: quantized bounding region information for a bounding region corresponding to the child node, where the quantized bounding region information encodes bounding region coordinates as offsets relative to the origin coordinates. Traversal circuitry may traverse multiple nodes of the data structure and determine whether a ray intersects a bounding region indicated by given a node of the data structure based on the node information. Disclosed techniques may provide substantial improvements to performance, data size, and power consumption.

Node encoding for spatially-organized ray tracing acceleration data structure

A method of scheduling instructions within a parallel processing unit is described. The method comprises decoding, in an instruction decoder, an instruction in a scheduled task in an active state, and checking, by an instruction controller, if an ALU targeted by the decoded instruction is a primary instruction pipeline. If the targeted ALU is a primary instruction pipeline, a list associated with the primary instruction pipeline is checked to determine whether the scheduled task is already included in the list. If the scheduled task is already included in the list, the decoded instruction is sent to the primary instruction pipeline.

Scheduling tasks using targeted pipelines

A method of activating scheduling instructions within a parallel processing unit includes checking if an ALU targeted by a decoded instruction is full by checking a value of an ALU work fullness counter stored in the instruction controller and associated with the targeted ALU. If the targeted ALU is not full, the decoded instruction is sent to the targeted ALU for execution and the ALU work fullness counter associated with the targeted ALU is updated. If, however, the targeted ALU is full, a scheduler is triggered to de-activate the scheduled task by changing the scheduled task from the active state to a non-active state. When an ALU changes from being full to not being full, the scheduler is triggered to re-activate an oldest scheduled task waiting for the ALU by removing the oldest scheduled task from the non-active state.

Scheduling tasks using work fullness counter

A method of scheduling tasks within a GPU or other highly parallel processing unit is described which is both age-aware and wakeup event driven. Tasks which are received are added to an age-based task queue. Wakeup event bits for task types, or combinations of task types and data groups, are set in response to completion of a task dependency and these wakeup event bits are used to select an oldest task from the queue that satisfies predefined criteria.

Task scheduling in a GPU using wakeup event state data

Queues for inter-pipeline data hazard avoidance

A memory subsystem for use with a single-instruction multiple-data (SIMD) processor comprising a plurality of processing units configured for processing one or more workgroups each comprising a plurality of SIMD tasks, the memory subsystem comprising: a shared memory partitioned into a plurality of memory portions for allocation to tasks that are to be processed by the processor; and a resource allocator configured to, in response to receiving a memory resource request for first memory resources in respect of a first-received task of a workgroup, allocate to the workgroup a block of memory portions sufficient in size for each task of the workgroup to receive memory resources in the block equivalent to the first memory resources.

Allocation of memory resources to SIMD workgroups

Disclosed techniques relate to primitive testing associated with ray intersection processing for ray tracing. In some embodiments, shader circuitry executes a first SIMD group that includes a ray intersect instruction for a set of rays. Ray intersect circuitry traverses, in response to the ray intersect instruction, multiple nodes in a spatially organized acceleration data structure (ADS). In response to reaching a node of the ADS that indicates one or more primitives, the apparatus forms a second SIMD group that executes one or more instructions to determine whether a set of rays that have reached the node intersect the one or more primitives. The shader circuitry may execute the first SIMD group to shade one or more primitives that are indicated as intersected based on results of execution of the second SIMD group. Thus, disclosed techniques may use both dedicated ray intersect circuitry and dynamically formed SIMD groups executed by shader processors to detect ray intersection.

SIMD group formation techniques for primitive testing associated with ray intersect traversal

Disclosed techniques relate to grouping rays during traversal of a spatially-organized acceleration data structure (e.g., a bounding volume hierarchy) for ray intersection processing. The grouping may provide temporal locality for accesses to bounding region data. In some embodiments, ray intersect circuitry is configured to group rays based on the node of the data structure that they target next. The ray intersect circuitry may select one or more groups of rays for issuance each clock cycle, e.g., to bounding region test circuitry.

Grouping techniques for ray intersection traversal

Disclosed techniques relate to ray intersection processing for ray tracing. In some embodiments, ray intersection circuitry traverses a spatially organized acceleration data structure and includes bounding region circuitry configured to test, in parallel, whether a ray intersects multiple different bounding regions indicated by a node of the data structure. Shader circuitry may execute a ray intersect instruction to invoke traversal by the ray intersect circuitry and the traversal may generate intersection results. The shader circuitry may shade intersected primitives based on the intersection results. Disclosed techniques that share processing between intersection circuitry and shader processors may improve performance, reduce power consumption, or both, relative to traditional techniques.

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Chesham, United Kingdom

Luca O Iuliano