Abstract: An apparatus to facilitate utilizing structured sparsity in systolic arrays is disclosed. The apparatus includes a processor comprising a systolic array to receive data from a plurality of source registers, the data comprising unpacked source data, structured source data that is packed based on sparsity, and metadata corresponding to the structured source data; identify portions of the unpacked source data to multiply with the structured source data, the portions of the unpacked source data identified based on the metadata; and output, to a destination register, a result of multiplication of the portions of the unpacked source data and the structured source data.

Abstract: Described herein is a graphics processing unit (GPU) configured to receive an instruction having multiple operands, where the instruction is a single instruction multiple data (SIMD) instruction configured to use a bfloat16 (BF16) number format and the BF16 number format is a sixteen-bit floating point format having an eight-bit exponent. The GPU can process the instruction using the multiple operands, where to process the instruction includes to perform a multiply operation, perform an addition to a result of the multiply operation, and apply a rectified linear unit function to a result of the addition.

Abstract: Shared local registers for thread team processing is described. An example of an apparatus includes one or more processors including a graphic processor having multiple processing resources; and memory for storage of data, the graphics processor to allocate a first thread team to a first processing resource, the first thread team including hardware threads to be executed solely by the first processing resource; allocate a shared local register (SLR) space that may be directly reference in the ISA instructions to the first processing resource, the SLR space being accessible to the threads of the thread team and being inaccessible to threads outside of the thread team; and allocate individual register spaces to the thread team, each of the individual register spaces being accessible to a respective thread of the thread team.

Abstract: Embodiments described herein provide a technique enable a broadcast load from an L1 cache or shared local memory to register files associated with hardware threads of a graphics core. One embodiment provides a graphics processor comprising a cache memory and a graphics core coupled with the cache memory. The graphics core includes a plurality of hardware threads and memory access circuitry to facilitate access to memory by the plurality of hardware threads. The graphics core is configurable to process a plurality of load request from the plurality of hardware threads, detect duplicate load requests within the plurality of load requests, perform a single read from the cache memory in response to the duplicate load requests, and transmit data associated with the duplicate load requests to requesting hardware threads.

Abstract: An apparatus to facilitate hardware enhancements for double precision systolic support is disclosed. The apparatus includes matrix acceleration hardware having double-precision (DP) matrix multiplication circuitry including a multiplier circuits to multiply pairs of input source operands in a DP floating-point format; adders to receive multiplier outputs from the multiplier circuits and accumulate the multiplier outputs in a high precision intermediate format; an accumulator circuit to accumulate adder outputs from the adders with at least one of a third global source operand on a first pass of the DP matrix multiplication circuitry or an intermediate result from the first pass on a second pass of the DP matrix multiplication circuitry, wherein the accumulator circuit to generate an accumulator output in the high precision intermediate format; and a down conversion and rounding circuit to down convert and round an output of the second pass as final result in the DP floating-point format.

Abstract: Embodiments described herein provide an apparatus comprising a plurality of processing resources including a first processing resource and a second processing resource, a shared local memory communicatively coupled to the first processing resource and the second processing resource, and a processor to receive an instruction to initiate a matrix multiplication operation, write a first set of matrix data into a first set of registers, and share the first set of matrix data between the first processing resource and the second processing resource for use in the matrix multiplication operation. Other embodiments may be described and claimed.

Abstract: An apparatus to facilitate computing efficient cross channel operations in parallel computing machines using systolic arrays is disclosed. The apparatus includes a plurality of registers and one or more processing elements communicably coupled to the plurality of registers. The one or more processing elements include a systolic array circuit to perform cross-channel operations on source data received from a single source register of the plurality of registers, wherein the systolic array circuit is modified to: receive inputs from the single source register at different stages of the systolic array circuit; perform cross-channel operations at channels of the systolic array circuit; bypass disabled channels of the systolic array circuit, the disabled channels not used to compute the cross-channel operations; and broadcast a final result of a final stage of the systolic array circuit to all channels of a destination register.

Abstract: Described herein is a graphics processor including a plurality of processing clusters coupled with a host interface, each processing cluster comprising a plurality of multiprocessors, the plurality of multiprocessors interconnected via a data interconnect, and each multiprocessor comprising sparse matrix multiply acceleration hardware including a systolic processing array with feedback inputs.

Abstract: Described herein is a graphics processing unit (GPU) comprising a first processing cluster to perform parallel processing operations, the parallel processing operations including a ray tracing operation and a matrix multiply operation; and a second processing cluster coupled to the first processing cluster, wherein the first processing cluster includes a floating-point unit to perform floating point operations, the floating-point unit is configured to process an instruction using a bfloat16 (BF16) format with a multiplier to multiply second and third source operands while an accumulator adds a first source operand with output from the multiplier.

Abstract: Described herein is a graphics processing unit (GPU) configured to receive an instruction having multiple operands, where the instruction is a single instruction multiple data (SIMD) instruction configured to use a bfloat16 (BF16) number format and the BF16 number format is a sixteen-bit floating point format having an eight-bit exponent. The GPU can process the instruction using the multiple operands, where to process the instruction includes to perform a multiply operation, perform an addition to a result of the multiply operation, and apply a rectified linear unit function to a result of the addition.

Abstract: An apparatus to facilitate computing efficient cross channel operations in parallel computing machines using systolic arrays is disclosed. The apparatus includes a plurality of registers and one or more processing elements communicably coupled to the plurality of registers. The one or more processing elements include a systolic array circuit to perform cross-channel operations on source data received from a single source register of the plurality of registers, wherein the systolic array circuit is modified to: receive inputs from the single source register at different stages of the systolic array circuit; perform cross-channel operations at channels of the systolic array circuit; bypass disabled channels of the systolic array circuit, the disabled channels not used to compute the cross-channel operations; and broadcast a final result of a final stage of the systolic array circuit to all channels of a destination register.

Abstract: Described herein is an accelerator device including a host interface, a fabric interconnect coupled with the host interface, and one or more hardware tiles coupled with the fabric interconnect, the one or more hardware tiles including sparse matrix multiply acceleration hardware including a systolic array with feedback inputs.

Abstract: A processing apparatus includes a processing resource including a general-purpose parallel processing engine and a matrix accelerator. The matrix accelerator includes first circuitry to receive a command to perform operations associated with an instruction, second circuitry to configure the matrix accelerator according to a physical depth of a systolic array within the matrix accelerator and a logical depth associated with the instruction, third circuitry to read operands for the instruction from a register file associated with the systolic array, fourth circuitry to perform operations for the instruction via one or more passes through one or more physical pipeline stages of the systolic array based on a configuration performed by the second circuitry, and fifth circuitry to write output of the operations to the register file associated with the systolic array.

Abstract: A processing apparatus described herein includes a general-purpose parallel processing engine comprising a systolic array having multiple pipelines, each of the multiple pipelines including multiple pipeline stages, wherein the multiple pipelines include a first pipeline, a second pipeline, and a common input shared between the first pipeline and the second pipeline.

Abstract: A processing apparatus can include a general-purpose parallel processing engine comprising a matrix accelerator including a multi-stage systolic array, where each stage includes multiple processing elements associated with multiple processing channels. The multiple processing elements are configured to receive output sparsity metadata that is independent of input sparsity of input matrix elements and perform processing operations on the input matrix elements based on the output sparsity metadata.

Abstract: A processing apparatus is described herein that includes a general-purpose parallel processing engine comprising a matrix accelerator including one or more systolic arrays, at least one of the one or more systolic arrays comprising multiple pipeline stages, each pipeline stage of the multiple pipeline stages including multiple processing elements, the multiple processing elements configured to perform processing operations on input matrix elements based on output sparsity metadata. The output sparsity metadata indicates to the multiple processing elements to bypass multiplication for a first row of elements of a second matrix and multiply a second row of elements of the second matrix with a column of matrix elements of a first matrix.

Abstract: A processing apparatus includes a general-purpose parallel processing engine including a set of multiple processing elements including a single precision floating-point unit, a double precision floating point unit, and an integer unit; a matrix accelerator including one or more systolic arrays; a first register file coupled with a first read control circuit, wherein the first read control circuit couples with the set of multiple processing elements and the matrix accelerator to arbitrate read requests to the first register file from the set of multiple processing elements and the matrix accelerator; and a second register file coupled with a second read control circuit, wherein the second read control circuit couples with the matrix accelerator to arbitrate read requests to the second register file from the matrix accelerator and limit access to the second register file by the set of multiple processing elements.

Abstract: Described herein is a graphics processing unit (GPU) comprising a first processing cluster to perform parallel processing operations, the parallel processing operations including a ray tracing operation and a matrix multiply operation; and a second processing cluster coupled to the first processing cluster, wherein the first processing cluster includes a floating-point unit to perform floating point operations, the floating-point unit is configured to process an instruction using a bfloat16 (BF16) format with a multiplier to multiply second and third source operands while an accumulator adds a first source operand with output from the multiplier.

Abstract: Embodiments described herein provide an apparatus comprising a plurality of processing resources including a first processing resource and a second processing resource, a shared local memory communicatively coupled to the first processing resource and the second processing resource, and a processor to receive an instruction to initiate a matrix multiplication operation, write a first set of matrix data into a first set of registers, and share the first set of matrix data between the first processing resource and the second processing resource for use in the matrix multiplication operation. Other embodiments may be described and claimed.

Abstract: Described herein is a graphics processing unit (GPU) comprising a single instruction, multiple thread (SIMT) multiprocessor comprising an instruction cache, a shared memory coupled with the instruction cache, and circuitry coupled with the shared memory and the instruction cache, the circuitry including multiple texture units, a first core including hardware to accelerate matrix operations, and a second core configured to receive an instruction having multiple operands in a bfloat16 (BF16) number format, wherein the multiple operands include a first source operand, a second source operand, and a third source operand, and the BF16 number format is a sixteen-bit floating point format having an eight-bit exponent and process the instruction, wherein to process the instruction includes to multiply the second source operand by the third source operand and add a first source operand to a result of the multiply.