Abstract:
One embodiment provides for a graphics processing unit (GPU) to accelerate machine learning operations, the GPU comprising an instruction cache to store a first instruction and a second instruction, the first instruction to cause the GPU to perform a floating-point operation, including a multi-dimensional floating-point operation, and the second instruction to cause the GPU to perform an integer operation; and a general-purpose graphics compute unit having a single instruction, multiple thread (SIMT) architecture, the general-purpose graphics compute unit to simultaneously execute the first instruction and the second instruction, wherein the integer operation corresponds to a memory address calculation.
Abstract:
An apparatus is described having instruction execution logic circuitry to execute first, second, third and fourth instruction. Both the first instruction and the second instruction insert a first group of input vector elements to one of multiple first non overlapping sections of respective first and second resultant vectors. The first group has a first bit width. Each of the multiple first non overlapping sections have a same bit width as the first group. Both the third instruction and the fourth instruction insert a second group of input vector elements to one of multiple second non overlapping sections of respective third and fourth resultant vectors. The second group has a second bit width that is larger than said first bit width. Each of the multiple second non overlapping sections have a same bit width as the second group. The apparatus also includes masking layer circuitry to mask the first and third instructions at a first resultant vector granularity, and, mask the second and fourth instructions at a second resultant vector granularity.
Abstract:
An apparatus is described that includes instruction execution circuitry to execute first, second, third, and fourth instructions, the first and second instructions select a first group of input vector elements from one of multiple first non-overlapping sections of respective first and second input vectors. Each of the multiple first non-overlapping sections have a same bit width as the first group. Both the third and fourth instructions select a second group of input vector elements from one of multiple second non-overlapping sections of respective third and fourth input vectors. The second group has a second bit width that is larger than the first bit width. Each of multiple second non-overlapping sections have a same bit width as the second group. The apparatus includes masking layer circuitry to mask the first and second groups at a first granularity a second granularity.
Abstract:
An apparatus is described that includes an execution unit to execute a first instruction and a second instruction. The execution unit includes input register space to store a first data structure to be replicated when executing the first instruction and to store a second data structure to be replicated when executing the second instruction. The first and second data structures are both packed data structures. Data values of the first packed data structure are twice as large as data values of the second packed data structure. The execution unit also includes replication logic circuitry to replicate the first data structure when executing the first instruction to create a first replication data structure, and, to replicate the second data structure when executing the second data instruction to create a second replication data structure. The execution unit also includes masking logic circuitry to mask the first replication data structure at a first granularity and mask the second replication data structure at a second granularity. The second granularity is twice as fine as the first granularity.
Abstract:
An apparatus and method for performing a vector permute. For example, one embodiment of a processor comprises: a source vector register to store a plurality of source data elements; a destination vector register to store a plurality of destination data elements; a control vector register to store a plurality of control data elements, each control data element corresponding to one of the destination data elements and including an N bit value indicating whether a source data element is to be copied to the corresponding destination data element; vector permute logic to compare the N bit value of each control data element to an N bit portion of an immediate to determine whether to copy a source data element to the corresponding destination data element, wherein if the N bit values match, then the vector permute logic is to identify a source data element using an index value included in the control data element and to responsively copy the source data element to the corresponding destination data element in the destination vector register.
Abstract:
An apparatus and method for performing a variable mask-vector expand. For example, one embodiment of a processor comprises: a source mask register to store a plurality of mask bit values; an index register to store a plurality of index values each associated with a vector data element in a destination vector register and identifying a bit within the source mask register; and variable mask-vector expand logic to expand each of the mask bit values from the source mask register into the associated vector data elements using the index values from the index register, wherein all bits of a vector data element are to be set equal to the mask bit value identified by the index value associated with that vector data element.
Abstract:
Receiving an instruction indicating a source operand and a destination operand. Storing a result in the destination operand in response to the instruction. The result operand may have: (1) first range of bits having a first end explicitly specified by the instruction in which each bit is identical in value to a bit of the source operand in a corresponding position; and (2) second range of bits that all have a same value regardless of values of bits of the source operand in corresponding positions. Execution of instruction may complete without moving the first range of the result relative to the bits of identical value in the corresponding positions of the source operand, regardless of the location of the first range of bits in the result. Execution units to execute such instructions, computer systems having processors to execute such instructions, and machine-readable medium storing such an instruction are also disclosed.
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:
One embodiment provides for a graphics processing unit (GPU) to accelerate machine learning operations, the GPU comprising an instruction cache to store a first instruction and a second instruction, the first instruction to cause the GPU to perform a floating-point operation, including a multi-dimensional floating-point operation, and the second instruction to cause the GPU to perform an integer operation; and a general-purpose graphics compute unit having a single instruction, multiple thread architecture, the general-purpose graphics compute unit to concurrently execute the first instruction and the second instruction.
Abstract:
An apparatus and method for performing a transform on complex data. For example, one embodiment of a processor comprises: multiplier circuitry to multiply packed real N-bit data elements in the first source register with packed real M-bit data elements in the second source register and to multiply packed imaginary N-bit data elements in the first source register with packed imaginary M-bit data elements in the second source register to generate at least four real products, adder circuitry to subtract a first selected real product from a second selected real product to generate a first temporary result and to subtract a third selected real product from a fourth selected real product to generate a second temporary result, the adder circuitry to add the first temporary result to a first packed N-bit data element from the third source register to generate a first pre-scaled result, to subtract the first temporary result from the first packed N-bit data element to generate a second pre-scaled result, to add the second temporary result to a second packed N-bit data element from the third source register to generate a third pre-scaled result, and to subtract the second temporary result from the second packed N-bit data element to generate a fourth pre-scaled result; scaling circuitry to scale the first, second, third and fourth pre-scaled results to a specified bit width to generate first, second, third, and fourth final results; and a destination register to store the first, second, third, and fourth final results in specified data element positions.