Abstract: An apparatus to facilitate copying surface data is disclosed. The apparatus includes copy engine hardware to receive a command to access surface data from a source location in memory to a destination location in the memory, divide the surface data into a plurality of surface data sub-blocks, process the surface data sub-blocks to calculate virtual addresses to which accesses to the memory are to be performed and perform the memory accesses.

Abstract: Apparatus and method for scheduling inference tasks. For example, one embodiment of an apparatus comprises: a plurality of compute units (CUs) to execute inferencing routines, an inferencing routine comprising a plurality of phases, at least one CU comprising execution circuitry configurable to operate in a single instruction multiple data (SIMD) mode or a single instruction multiple thread (SIMT) mode; and dispatching hardware logic to determine whether a current phase of an inferencing routine is to be executed in the SIMD mode or the SIMT mode, and to dispatch instructions of the current phase for execution by the execution circuitry of a CU in accordance with the SIMD mode or the SIMT mode, respectively.

Abstract: A cache streaming apparatus and method for machine learning. For example, one embodiment of an apparatus comprises: a plurality of compute units to perform machine learning operations; a cache subsystem comprising a hierarchy of cache levels, at least some of the cache levels shared by two or more of the plurality of compute units; and data streaming hardware logic to stream machine learning data in and out of the cache subsystem based on the machine learning operations, the data streaming hardware logic to load data into the cache subsystem from memory before the data is needed by a first portion of the machine learning operations and to ensure that results produced by the first portion of machine learning operations are maintained in the cache subsystem until used by a second portion of the machine learning operations.

Abstract: Described herein is a partitional graphics processor having multiple hard partitions with separate software execution and fault domains. One embodiment provides a graphics processor comprising a system interface and a plurality of graphics processing resources coupled with the system interface. The plurality of graphics processing resources is configurable to be partitioned into a plurality of isolated device partitions, each isolated device partition configured for fault isolation and independent concurrent execution of workloads associated with a plurality of clients, and the system interface is configured to present each of the plurality of isolated device partitions as a virtual function.

Abstract: Described herein is a partitionable graphics processor having a plurality of flexibly partitioned processing resources. One embodiment provides a graphics processor comprising a plurality of processing resources configurable to be flexibly partitioned into a plurality of resource partitions and circuitry to compose multiple graphics processor device partitions from the plurality of resource partitions. The multiple graphics processor device partitions are configurable to be asymmetrically composed of different types of functional units.

Abstract: Described herein is a partitionable graphics processor having multiple render front ends. The partitions of the graphics processor maintain render functionality when partitioned and enable fault isolation and independent multi-client rendering.

Abstract: Apparatus and method for prefetching node data. For example, one embodiment of an apparatus comprises: ray tracing acceleration hardware to manage active ray tracing stack allocations within a traversal stack; and stack management hardware logic to issue a prefetch operation comprising an indication of bounding volume hierarchy (BVH) node data to be prefetched and an indication of a cache level, wherein responsive to performing the prefetch operation, the BVH node data is to be prefetched to the indicated cache level.

Abstract: In an example, an apparatus comprises a plurality of execution units comprising at least a first type of execution unit and a second type of execution unit and logic, at least partially including hardware logic, to analyze a workload and assign the workload to one of the first type of execution unit or the second type of execution unit. Other embodiments are also disclosed and claimed.

Abstract: An apparatus to facilitate efficient data sharing for graphics data processing operations is disclosed. The apparatus includes a processing resource to generate a stream of instructions, an L1 cache communicably coupled to the processing resource and comprising an on-page detector circuit to determine that a set of memory requests in the stream of instructions access a same memory page; and set a marker in a first request of the set of memory requests; and arbitration circuitry communicably coupled to the L1 cache, the arbitration circuitry to route the set of memory requests to memory comprising the memory page and to, in response to receiving the first request with the marker set, remain with the processing resource to process the set of memory requests.

Abstract: A mechanism is described for facilitating inference coordination and processing utilization for machine learning. A method of embodiments, as described herein, includes limiting execution of workloads for the respective contexts of a plurality of contexts to a specified subset of a plurality of processing resources of a processing system according to physical resource slices of the processing system that are associated with the respective contexts of the plurality of contexts.

Abstract: In an example, an apparatus comprises a plurality of execution units, and a first memory communicatively couple to the plurality of execution units, wherein the first shared memory is shared by the plurality of execution units and a copy engine to copy context state data from at least a first of the plurality of execution units to the first shared memory. Other embodiments are also disclosed and claimed.

Abstract: A mechanism is described for facilitating adaptive resolution and viewpoint-prediction for immersive media in computing environments. An apparatus of embodiments, as described herein, includes one or more processors to receive viewing positions associated with a user with respect to a display, and analyze relevance of media contents based on the viewing positions, where the media content includes immersive videos of scenes captured by one or more cameras. The one or more processors are further to predict portions of the media contents as relevant portions based on the viewing positions and transmit the relevant portions to be rendered and displayed.

Abstract: Methods and apparatus relating to techniques for avoiding cache lookup for cold cache. In an example, an apparatus comprises logic, at least partially comprising hardware logic, to collect user information for a user of a data processing device, generate a user profile for the user of the data processing device from the user information, and set a power profile a processor in the data processing device using the user profile. Other embodiments are also disclosed and claimed.

Abstract: Embodiments provide mechanisms to facilitate compute operations for deep neural networks. One embodiment comprises a graphics processing unit comprising one or more multiprocessors, at least one of the one or more multiprocessors including a register file to store a plurality of different types of operands and a plurality of processing cores. The plurality of processing cores includes a first set of processing cores of a first type and a second set of processing cores of a second type. The first set of processing cores are associated with a first memory channel and the second set of processing cores are associated with a second memory channel.

Abstract: One embodiment provides circuitry coupled with cache memory and a memory interface, the circuitry to compress compute data at multiple cache line granularity, and a processing resource coupled with the memory interface and the cache memory. The processing resource is configured to perform a general-purpose compute operation on compute data associated with multiple cache lines of the cache memory. The circuitry is configured to compress the compute data before a write of the compute data via the memory interface to the memory bus, in association with a read of the compute data associated with the multiple cache lines via the memory interface, decompress the compute data, and provide the decompressed compute data to the processing resource.

Abstract: Embodiments described herein provide an apparatus comprising a plurality of processing resources including a first processing resource and a second processing resource, a memory communicatively coupled to the first processing resource and the second processing resource, and a processor to receive data dependencies for one or more tasks comprising one or more producer tasks executing on the first processing resource and one or more consumer tasks executing on the second processing resource and move a data output from one or more producer tasks executing on the first processing resource to a cache memory communicatively coupled to the second processing resource. Other embodiments may be described and claimed.

Abstract: Embodiments are generally directed to compression for compression for sparse data structures utilizing mode search approximation. An embodiment of an apparatus includes one or more processors including a graphics processor to process data; and a memory for storage of data, including compressed data. The one or more processors are to provide for compression of a data structure, including identification of a mode in the data structure, the data structure including a plurality of values and the mode being a most repeated value in a data structure, wherein identification of the mode includes application of a mode approximation operation, and encoding of an output vector to include the identified mode, a significance map to indicate locations at which the mode is present in the data structure, and remaining uncompressed data from the data structure.

Abstract: One embodiment provides an apparatus comprising a memory device configured to store a page table that includes a set of page table entries and a graphics processing cluster array including a plurality of graphics multiprocessors, the plurality of graphics multiprocessors coupled via a data interconnect. The graphics multiprocessor of the plurality of graphics multiprocessors includes a translation lookaside buffer (TLB) coupled with the memory device, the TLB to cache a first page table entry of the set of page table entries, the first page table entry to indicate that a first virtual page is a valid page that is cleared to a clear color and circuitry to bypass an access to the memory device for the first virtual page and determine a color associated with the first virtual page based on the indication that the first virtual page is a valid page that is cleared to the clear color.

Abstract: Position-based rendering apparatus and method for multi-die/GPU graphics processing. For example, one embodiment of a method comprises: distributing a plurality of graphics draws to a plurality of graphics processors; performing position-only shading using vertex data associated with tiles of a first draw on a first graphics processor, the first graphics processor responsively generating visibility data for each of the tiles; distributing subsets of the visibility data associated with different subsets of the tiles to different graphics processors; limiting geometry work to be performed on each tile by each graphics processor using the visibility data, each graphics processor to responsively generate rendered tiles; and wherein the rendered tiles are combined to generate a complete image frame.

Abstract: Embodiments are generally directed to compression in machine learning and deep learning processing. An embodiment of an apparatus for compression of untyped data includes a graphical processing unit (GPU) including a data compression pipeline, the data compression pipeline including a data port coupled with one or more shader cores, wherein the data port is to allow transfer of untyped data without format conversion, and a 3D compression/decompression unit to provide for compression of untyped data to be stored to a memory subsystem and decompression of untyped data from the memory subsystem.