Abstract: Apparatuses including general-purpose graphics processing units having on chip dense memory for temporal buffering are disclosed. In one embodiment, a graphics multiprocessor includes a plurality of compute engines to perform first computations to generate a first set of data, cache for storing data, and a high density memory that is integrated on chip with the plurality of compute engines and the cache. The high density memory to receive the first set of data, to temporarily store the first set of data, and to provide the first set of data to the cache during a first time period that is prior to a second time period when the plurality of compute engines will use the first set of data for second computations.

Abstract: Methods and apparatus relating to data initialization techniques. In an example, an apparatus comprises a processor to read one or more metadata codes which map to one or more cache lines in a cache memory and invoke a random number generator to generate random numerical data for the one or more cache lines in response to a determination that the one more metadata codes indicate that the cache lines are to contain random numerical data. Other embodiments are also disclosed and claimed.

Abstract: Methods and apparatus relating to scalar core integration in a graphics processor. In an example, an apparatus comprises a processor to receive a set of workload instructions for a graphics workload from a host complex, determine a first subset of operations in the set of operations that is suitable for execution by a scalar processor complex of the graphics processing device and a second subset of operations in the set of operations that is suitable for execution by a vector processor complex of the graphics processing device, assign the first subset of operations to the scalar processor complex for execution to generate a first set of outputs, assign the second subset of operations to the vector processor complex for execution to generate a second set of outputs. Other embodiments are also disclosed and claimed.

Abstract: Generation and storage of compressed z-planes in graphics processing is described. An example of a processor includes a rasterizer to generate a fragment of pixel data including blocks of pixel data; a depth pipeline to receive the fragment, the pipeline including a first and second depth test hardware, the first depth test hardware to perform a coarse depth test including determining minimum and maximum depths for each block; and a depth buffer, wherein the processor is to determine whether the fragment meets requirements that the fragment fully covers a tile of pixel data and passes a first depth test, and that each of the minimum and maximum depths of the fragment has a same sign and exponent, and, upon determining that the fragment meets the requirements, to generate a compressed depth plane utilizing the first depth test and update the depth buffer with the compressed depth plane.

Abstract: Embodiments described herein provide techniques to disaggregate an architecture of a system on a chip integrated circuit into multiple distinct chiplets that can be packaged onto a common chassis. In one embodiment, a graphics processing unit or parallel processor is composed from diverse silicon chiplets that are separately manufactured. A chiplet is an at least partially and distinctly packaged integrated circuit that includes distinct units of logic that can be assembled with other chiplets into a larger package. A diverse set of chiplets with different IP core logic can be assembled into a single device.

Abstract: One embodiment provides a parallel processor comprising a hardware scheduler to schedule pipeline commands for compute operations to one or more of multiple types of compute units, a plurality of processing resources including a first sparse compute unit configured for input at a first level of sparsity and hybrid memory circuitry including a memory controller, a memory interface, and a second sparse compute unit configured for input at a second level of sparsity that is greater than the first level of sparsity.

Abstract: Embodiments described herein provide a scalable coherency tracking implementation that utilizes shared virtual memory to manage data coherency. In one embodiment, coherency tracking granularity is reduced relative to existing coherency tracking solutions, with coherency tracking storage memory moved to memory as a page table metadata. For example and in one embodiment, storage for coherency state is moved from dedicated hardware blocks to system memory, effectively providing a directory structure that is limitless in size.

Abstract: One embodiment provides an apparatus comprising an interconnect fabric comprising a processing cluster including an array of multiprocessors coupled to an interconnect fabric, scheduling circuitry to distribute a plurality of thread groups across the array of multiprocessors, each thread group comprising a plurality of threads. A first multiprocessor of the array of multiprocessors can be assigned to process a first thread group comprising a first plurality of threads including a first thread sub-group and a second thread sub-group. The second thread sub-group has a data dependency on the first thread sub-group and the first multiprocessor includes circuitry to cause threads of the second thread sub-group to sleep until the threads of the first thread sub-group have satisfied the data dependency.

Abstract: Apparatuses to synchronize lanes that diverge or threads that drift are disclosed. In one embodiment, a graphics multiprocessor includes a queue having an initial state of groups with a first group having threads of first and second instruction types and a second group having threads of the first and second instruction types. A regroup engine (or regroup circuitry) regroups threads into a third group having threads of the first instruction type and a fourth group having threads of the second instruction type.

Abstract: One embodiment provides for a graphics processing unit to accelerate machine-learning operations, the graphics processing unit comprising a multiprocessor having a single instruction, multiple thread (SIMT) architecture, the multiprocessor to execute at least one single instruction; and a first compute unit included within the multiprocessor, the at least one single instruction to cause the first compute unit to perform a two-dimensional matrix multiply and accumulate operation, wherein to perform the two-dimensional matrix multiply and accumulate operation includes to compute an intermediate product of 16-bit operands and to compute a 32-bit sum based on the intermediate product.

Abstract: An apparatus to facilitate thread scheduling is disclosed. The apparatus includes logic to store barrier usage data based on a magnitude of barrier messages in an application kernel and a scheduler to schedule execution of threads across a plurality of multiprocessors based on the barrier usage data.

Abstract: A mechanism is described for facilitating intelligent thread scheduling at autonomous machines. A method of embodiments, as described herein, includes detecting dependency information relating to a plurality of threads corresponding to a plurality of workloads associated with tasks relating to a processor including a graphics processor. The method may further include generating a tree of thread groups based on the dependency information, where each thread group includes multiple threads, and scheduling one or more of the thread groups associated a similar dependency to avoid dependency conflicts.

Abstract: Systems and methods for improving cache efficiency and utilization are disclosed. In one embodiment, a graphics processor includes processing resources to perform graphics operations and a cache controller of a cache coupled to the processing resources. The cache controller is configured to control cache priority by determining whether default settings or an instruction will control cache operations for the cache.

Abstract: Embodiments described herein include software, firmware, and hardware that provides techniques to enable deterministic scheduling across multiple general-purpose graphics processing units. One embodiment provides a multi-GPU architecture with uniform latency. One embodiment provides techniques to distribute memory output based on memory chip thermals. One embodiment provides techniques to enable thermally aware workload scheduling. One embodiment provides techniques to enable end to end contracts for workload scheduling on multiple GPUs.

Abstract: Embodiments are generally directed to graphics processor data access and sharing. An embodiment of an apparatus includes a circuit element to produce a result in processing of an application; a load-store unit to receive the result and generate pre-fetch information for a cache utilizing the result; and a prefetch generator to produce prefetch addresses based at least in part on the pre-fetch information; wherein the load-store unit is to receive software assistance for prefetching, and wherein generation of the pre-fetch information is based at least in part on the software assistance.

Abstract: A mechanism is described for facilitating dynamic cache allocation in computing devices in computing devices. A method of embodiments, as described herein, includes facilitating monitoring one or more bandwidth consumptions of one or more clients accessing a cache associated with a processor; computing one or more bandwidth requirements of the one or more clients based on the one or more bandwidth consumptions; and allocating one or more portions of the cache to the one or more clients in accordance with the one or more bandwidth requirements.

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: 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: Methods and apparatus relating to techniques for multi-tile memory management. In an example, a graphics processor includes an interposer, a first chiplet coupled with the interposer, the first chiplet including a graphics processing resource and an interconnect network coupled with the graphics processing resource, cache circuitry coupled with the graphics processing resource via the interconnect network, and a second chiplet coupled with the first chiplet via the interposer, the second chiplet including a memory-side cache and a memory controller coupled with the memory-side cache. The memory controller is configured to enable access to a high-bandwidth memory (HBM) device, the memory-side cache is configured to cache data associated with a memory access performed via the memory controller, and the cache circuitry is logically positioned between the graphics processing resource and a chiplet interface.

Abstract: An apparatus to facilitate compute optimization is disclosed. The apparatus includes a mixed precision core including mixed-precision execution circuitry to execute one or more of the mixed-precision instructions to perform a mixed-precision dot-product operation comprising to perform a set of multiply and accumulate operations.