Abstract: Described herein is a graphics processor comprising a memory interface and a graphics processing cluster coupled with the memory interface. The graphics processing cluster includes a multi-lane parallel floating-point unit and a multi-lane parallel integer unit. The multi-lane parallel integer unit includes an integer pipeline including a plurality of parallel integer logic units configured to perform integer compute operations on a plurality of input data elements and a format conversion pipeline including a plurality of parallel format conversion units configured to convert a plurality of input data elements from a first one of a plurality of datatype formats to a second one of the plurality of datatype formats, the plurality of datatype formats including integer and floating-point formats.

Abstract: Described herein is a graphics processor comprising a memory interface and a graphics processing cluster coupled with the memory interface. The graphics processing cluster includes a plurality of processing resources. A processing resource of the plurality of processing resources includes a source crossbar communicatively coupled with a register file, the source crossbar to reorder data elements of a source operand and a format conversion pipeline to convert a plurality of input data elements specified by the source operand from a first format of a plurality of datatype formats to a second format of the plurality of datatype formats, the plurality of datatype formats including integer and floating-point formats.

Abstract: Described herein is a graphics processor comprising an instruction cache and a plurality of processing elements coupled with the instruction cache. The plurality of processing elements include functional units configured to provide an integer pipeline to execute instructions to perform operations on integer data elements. The integer pipeline including a first multiplier and a second multiplier, the first multiplier and the second multiplier configured to execute operations for a single instruction.

Abstract: The present invention provides a cryopreservation device with an integrated tracking device chamber. The tracking device can be an RFID tag which can be placed in the cryopreservation device as part of the procedure for vitrification of a biological specimen. The cryopreservation device maintains its integrity and keeps the RFID tag securely in the chamber at both ambient conditions when the specimen is collected and under liquid nitrogen where the specimen is stored. The cryopreservation device with an integrated tracking device chamber allows for each individual biological sample to have its own tracking device for monitoring during cryogenic storage.

Abstract: The present invention provides a cryopreservation device with an integrated tracking device chamber. The tracking device can be an RFID tag which can be placed in the cryopreservation device as part of the procedure for vitrification of a biological specimen. The cryopreservation device maintains its integrity and keeps the RFID tag securely in the chamber at both ambient conditions when the specimen is collected and under liquid nitrogen where the specimen is stored. The cryopreservation device with an integrated tracking device chamber allows for each individual biological sample to have its own tracking device for monitoring during cryogenic storage.

Abstract: The present invention provides a cryopreservation device with an integrated tracking device chamber. The tracking device can be an RFID tag which can be placed in the cryopreservation device as part of the procedure for vitrification of a biological specimen. The cryopreservation device maintains its integrity and keeps the RFID tag securely in the chamber at both ambient conditions when the specimen is collected and under liquid nitrogen where the specimen is stored. The cryopreservation device with an integrated tracking device chamber allows for each individual biological sample to have its own tracking device for monitoring during cryogenic storage.

Abstract: Examples described herein relate to a software and hardware optimization that manages scenarios where a write operation to a register is less than an entirety of the register. A compiler detects instructions that make partial writes to the same register, groups such instructions, and provides hints to hardware of the partial write. The execution unit combines the output data for grouped instructions and updates the destination register as single write instead of multiple separate partial writes.

Abstract: Embodiments described herein provide for an instruction and associated logic to enable a vector multiply add instructions with automatic zero skipping for sparse input. One embodiment provides for a general-purpose graphics processor comprising logic to perform operations comprising fetching a hardware macro instruction having a predicate mask, a repeat count, and a set of initial operands, where the initial operands include a destination operand and multiple source operands. The hardware macro instruction is configured to perform one or more multiply/add operations on input data associated with a set of matrices.

Abstract: Embodiments described herein provided for an instruction and associated logic to enable GPGPU program code to access special purpose hardware logic to accelerate dot product operations. One embodiment provides for a graphics processing unit comprising a fetch unit to fetch an instruction for execution and a decode unit to decode the instruction into a decoded instruction. The decoded instruction is a matrix instruction to cause the graphics processing unit to perform a parallel dot product operation. The GPGPU also includes systolic dot product circuitry to execute the decoded instruction across one or more SIMD lanes using multiple systolic layers, wherein to execute the decoded instruction, a dot product computed at a first systolic layer is to be output to a second systolic layer, wherein each systolic layer includes one or more sets of interconnected multipliers and adders, each set of multipliers and adders to generate a dot product.

Abstract: Embodiments detailed herein relate to reduction operations on a plurality of data element values. In one embodiment, a process comprises decoding circuitry to decode an instruction and execution circuitry to execute the decoded instruction. The instruction specifies a first input register containing a plurality of data element values, a first index register containing a plurality of indices, and an output register, where each index of the plurality of indices maps to one unique data element position of the first input register. The execution includes to identify data element values that are associated with one another based on the indices, perform one or more reduction operations on the associated data element values based on the identification, and store results of the one or more reduction operations in the output register.

Abstract: Embodiments described herein provide for an instruction and associated logic to enable a vector multiply add instructions with automatic zero skipping for sparse input. One embodiment provides for a general-purpose graphics processor comprising logic to perform operations comprising fetching a hardware macro instruction having a predicate mask, a repeat count, and a set of initial operands, where the initial operands include a destination operand and multiple source operands. The hardware macro instruction is configured to perform one or more multiply/add operations on input data associated with a set of matrices.

Abstract: Examples described herein relate to a software and hardware optimization that manages scenarios where a write operation to a register is less than an entirety of the register. A compiler detects instructions that make partial writes to the same register, groups such instructions, and provides hints to hardware of the partial write. The execution unit combines the output data for grouped instructions and updates the destination register as single write instead of multiple separate partial writes.

Abstract: Embodiments detailed herein relate to reduction operations on a plurality of data element values. In one embodiment, a process comprises decoding circuitry to decode an instruction and execution circuitry to execute the decoded instruction. The instruction specifies a first input register containing a plurality of data element values, a first index register containing a plurality of indices, and an output register, where each index of the plurality of indices maps to one unique data element position of the first input register. The execution includes to identify data element values that are associated with one another based on the indices, perform one or more reduction operations on the associated data element values based on the identification, and store results of the one or more reduction operations in the output register.

Abstract: Examples described herein relate to a software and hardware optimization that manages scenarios where a write operation to a register is less than an entirety of the register. A compiler detects instructions that make partial writes to the same register, groups such instructions, and provides hints to hardware of the partial write. The execution unit combines the output data for grouped instructions and updates the destination register as single write instead of multiple separate partial writes.

Abstract: Embodiments described herein provide for an instruction and associated logic to enable a vector multiply add instructions with automatic zero skipping for sparse input. One embodiment provides for a general-purpose graphics processor comprising logic to perform operations comprising fetching a hardware macro instruction having a predicate mask, a repeat count, and a set of initial operands, where the initial operands include a destination operand and multiple source operands. The hardware macro instruction is configured to perform one or more multiply/add operations on input data associated with a set of matrices.

Abstract: Embodiments detailed herein relate to reduction operations on a plurality of data element values. In one embodiment, a process comprises decoding circuitry to decode an instruction and execution circuitry to execute the decoded instruction. The instruction specifies a first input register containing a plurality of data element values, a first index register containing a plurality of indices, and an output register, where each index of the plurality of indices maps to one unique data element position of the first input register. The execution includes to identify data element values that are associated with one another based on the indices, perform one or more reduction operations on the associated data element values based on the identification, and store results of the one or more reduction operations in the output register.

Abstract: Embodiments described herein provide for an instruction and associated logic to enable a vector multiply add instructions with automatic zero skipping for sparse input. One embodiment provides for a general-purpose graphics processor comprising logic to perform operations comprising fetching a hardware macro instruction having a predicate mask, a repeat count, and a set of initial operands, where the initial operands include a destination operand and multiple source operands. The hardware macro instruction is configured to perform one or more multiply/add operations on input data associated with a set of matrices.

Abstract: Examples described herein relate to a software and hardware optimization that manages scenarios where a write operation to a register is less than an entirety of the register. A compiler detects instructions that make partial writes to the same register, groups such instructions, and provides hints to hardware of the partial write. The execution unit combines the output data for grouped instructions and updates the destination register as single write instead of multiple separate partial writes.

Abstract: Embodiments described herein provided for an instruction and associated logic to enable GPGPU program code to access special purpose hardware logic to accelerate dot product operations. One embodiment provides for a graphics processing unit comprising a fetch unit to fetch an instruction for execution and a decode unit to decode the instruction into a decoded instruction. The decoded instruction is a matrix instruction to cause the graphics processing unit to perform a parallel dot product operation. The GPGPU also includes a systolic dot product unit to execute the decoded instruction across one or more SIMD lanes using multiple systolic layers, wherein to execute the decoded instruction, a dot product computed at a first systolic layer is to be output to a second systolic layer, wherein each systolic layer includes one or more sets of interconnected multipliers and adders, each set of multipliers and adders to generate a dot product.

Abstract: An processor to facilitate register sharing is disclosed. The processor includes a plurality of execution units (EUs), each including a General Purpose Register File (GRF) having a plurality of registers; and register sharing hardware to divide the plurality of registers into a first set of registers dedicated for execution of a first set of threads and a second set of registers shared for execution of a second set of threads.