Intel Corporation

Intel Corparation

Bret L Toll

Elmoustapha Ould-Ahmed-Vall

Robert Valentine

Mark Jay Charney

Jesus Corbal

Alexander F Heinecke

Zeev Sperber

Raanan Sade

Dan Baum

Christopher J Hughes

Amit Gradstein

Martin Guy Dixon

Simon Rubanovich

Menachem Adelman

Milind Baburao Girkar

Vinodh Gopal

Ronak Singhal

Sean Mirkes

Wajdi K Feghali

James D Guilford

Maxim Loktyukhin

Rinat Rappoport

Gilbert M Wolrich

Jason W Brandt

Erdinc Ozturk

Mark C Davis

Alexandre J Farcy

Suleyman Sair

Jesus Corbal San Adrian

Stanislav Shwartsman

Glenn J Hinton

Charles R Yount

Edward Thomas Grochowski

Matthew C Merten

Igor Yanover

Jonathan Cannon Hall

Konrad K Lai

Ravi Rajwar

Michael J Espig

Stephen H Gunther

Scott Dion Rodgers

Andrew Thomas Forsyth

Ronen Zohar

Frank Binns

James S Coke

Andrew J Herdrich

Robert Dale Cavin

Eric W Mahurin

Dennis Ryan Bradford

Seth Abraham

Kshitij Arun Doshi

Gilbert A Neiger

Ravishankar Iyer

Srinivas Chennupaty

David B Papworth

Alan Beecher Kyker

Robert S Chappell

Doron Orenstein

Edwin Verplanke

Rajesh S Parthasarathy

Chris J Newburn

Andrey Naraikin

Cristina S Anderson

Julius Yuli Mandelblat

Mark J Buxton

Jeffrey D Chamberlain

Scott D Hahn

Peter Lachner

Thomas R Huff

Subramaniam M Maiyuran

Eliezer Weissmann

Varghese George

Rajesh M Sankaran

Alon Naveh

Inder M Sodhi

Dheeraj R Subbareddy

Eugene Gorbatov

Venkateswara Rao Madduri

Stephen Anthony Fischer

Dion Rodgers

David A Koufaty

Mikhail Plotnikov

Michael Mishaeli

Michael A Fetterman

Lihu Rappoport

Ganapati Srinivasa

Ashish V Choubal

Andrew D Henroid

Hisham Shafi

Paolo L Narvaez

John G Holm

Vedvyas Shanbhogue

Ren Wang

Benny Eitan

William W Macy, Jr

Russell Jerome Fenger

Willard M Wiseman

Eric L Debes

Amy L Santoni

Deep K Buch

Embodiments detailed herein relate to matrix operations. For example, embodiments of instruction support for matrix (tile) dot product operations are detailed. Exemplary instructions including computing a dot product of signed words and accumulating in a double word with saturation; computing a dot product of bytes and accumulating in to a dword with saturation, where the input bytes can be signed or unsigned and the dword accumulation has output saturation; etc.

Systems, methods, and apparatuses for dot production operations

Embodiments detailed herein relate to systems and methods to store a tile register pair to memory. In one example, a processor includes: decode circuitry to decode a store matrix pair instruction having fields for an opcode and source and destination identifiers to identify source and destination matrices, respectively, each matrix having a PAIR parameter equal to TRUE; and execution circuitry to execute the decoded store matrix pair instruction to store every element of left and right tiles of the identified source matrix to corresponding element positions of left and right tiles of the identified destination matrix, respectively, wherein the executing stores a chunk of C elements of one row of the identified source matrix at a time.

Systems and methods to store a tile register pair to memory

Embodiments detailed herein relate to matrix (tile) operations. For example, decode circuitry to decode an instruction having fields for an opcode and a memory address; and execution circuitry to execute the decoded instruction to set a tile configuration for the processor to utilize tiles in matrix operations based on a description retrieved from the memory address, wherein a tile a set of 2-dimensional registers are discussed.

Systems, methods, and apparatus for tile configuration

Embodiments detailed herein relate to matrix (tile) operations. For example, decode circuitry to decode an instruction having fields for an opcode and a memory address, and execution circuitry to execute the decoded instruction to store configuration information about usage of storage for two-dimensional data structures at the memory address.

Systems, methods, and apparatuses for matrix operations

Disclosed embodiments relate to systems for performing instructions to quickly convert and use matrices (tiles) as one-dimensional vectors. In one example, a processor includes fetch circuitry to fetch an instruction having fields to specify an opcode, locations of a two-dimensional (2D) matrix and a one-dimensional (1D) vector, and a group of elements comprising one of a row, part of a row, multiple rows, a column, part of a column, multiple columns, and a rectangular sub-tile of the specified 2D matrix, and wherein the opcode is to indicate a move of the specified group between the 2D matrix and the 1D vector, decode circuitry to decode the fetched instruction; and execution circuitry, responsive to the decoded instruction, when the opcode specifies a move from 1D, to move contents of the specified 1D vector to the specified group of elements.

Systems for performing instructions to quickly convert and use tiles as 1D vectors

Embodiments detailed herein relate to matrix operations. In particular, support for matrix (tile) addition, subtraction, and multiplication is described. For example, circuitry to support instructions for element-by-element matrix (tile) addition, subtraction, and multiplication are detailed. In some embodiments, for matrix (tile) addition, decode circuitry is to decode an instruction having fields for an opcode, a first source matrix operand identifier, a second source matrix operand identifier, and a destination matrix operand identifier; and execution circuitry is to execute the decoded instruction to, for each data element position of the identified first source matrix operand: add a first data value at that data element position to a second data value at a corresponding data element position of the identified second source matrix operand, and store a result of the addition into a corresponding data element position of the identified destination matrix operand.

Systems, methods, and apparatuses for matrix add, subtract, and multiply

Embodiments detailed herein relate to systems and methods to load a tile register pair. In one example, a processor includes: decode circuitry to decode a load matrix pair instruction having fields for an opcode and source and destination identifiers to identify source and destination matrices, respectively, each matrix having a PAIR parameter equal to TRUE; and execution circuitry to execute the decoded load matrix pair instruction to load every element of left and right tiles of the identified destination matrix from corresponding element positions of left and right tiles of the identified source matrix, respectively, wherein the executing operates on one row of the identified destination matrix at a time, starting with the first row.

Systems and methods to load a tile register pair

Graphics processing systems and methods are described. A graphics processing apparatus may comprise one or more graphics processing engines, a memory, a memory management unit (MMU) including a GPU second level page table and GPU dirty bit tracking, and a provisioning agent to receive a request from a virtual machine monitor (VMM) to provision a subcluster of graphics processing apparatuses, the subcluster including a plurality of graphics processing engines from a plurality of graphics processing apparatuses connected using a scale-up fabric, provision the scale-up fabric to route data within the subcluster of graphics processing apparatuses, and provision a plurality of resources on the graphics processing apparatus for the subcluster based on the request from the VMM.

Virtualization and multi-tenancy support in graphics processors

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.

Instruction execution that broadcasts and masks data values at different levels of granularity

Systems, methods, and apparatuses for resource bandwidth monitoring and control are described. For example, in some embodiments, an apparatus comprising a requestor device to send a credit based request, a receiver device to receive and consume the credit based request, and a delay element in a return path between the requestor and receiver devices, the delay element to delay a credit based response from the receiver to the requestor are detailed.

Systems, apparatuses, and methods for resource bandwidth enforcement

Disclosed embodiments relate to computing dot products of nibbles in tile operands. In one example, a processor includes decode circuitry to decode a tile dot product instruction having fields for an opcode, a destination identifier to identify a M by N destination matrix, a first source identifier to identify a M by K first source matrix, and a second source identifier to identify a K by N second source matrix, each of the matrices containing doubleword elements, and execution circuitry to execute the decoded instruction to perform a flow K times for each element (M,N) of the identified destination matrix to generate eight products by multiplying each nibble of a doubleword element (M,K) of the identified first source matrix by a corresponding nibble of a doubleword element (K,N) of the identified second source matrix, and to accumulate and saturate the eight products with previous contents of the doubleword element (M,N).

Systems and methods for computing dot products of nibbles in two tile operands

Embodiments detailed herein relate to matrix operations. In particular, the loading of a matrix (tile) from memory. For example, support for a loading instruction is described in the form of decode circuitry to decode an instruction having fields for an opcode, a destination matrix operand identifier, and source memory information, and execution circuitry to execute the decoded instruction to load groups of strided data elements from memory into configured rows of the identified destination matrix operand to memory.

Systems, methods, and apparatuses for tile load, multiplication and accumulation

Disclosed embodiments relate to matrix compress/decompress instructions. In one example, a processor includes fetch circuitry to fetch a compress instruction having a format with fields to specify an opcode and locations of decompressed source and compressed destination matrices, decode circuitry to decode the fetched compress instructions, and execution circuitry, responsive to the decoded compress instruction, to: generate a compressed result according to a compress algorithm by compressing the specified decompressed source matrix by either packing non-zero-valued elements together and storing the matrix position of each non-zero-valued element in a header, or using fewer bits to represent one or more elements and using the header to identify matrix elements being represented by fewer bits; and store the compressed result to the specified compressed destination matrix.

Systems and methods for performing matrix compress and decompress instructions

Embodiments detailed herein relate to matrix operations. In particular, matrix (tile) multiply accumulate and negated matrix (tile) multiply accumulate are discussed. For example, in some embodiments decode circuitry to decode an instruction having fields for an opcode, an identifier for a first source matrix operand, an identifier of a second source matrix operand, and an identifier for a source/destination matrix operand; and execution circuitry to execute the decoded instruction to multiply the identified first source matrix operand by the identified second source matrix operand, add a result of the multiplication to the identified source/destination matrix operand, and store a result of the addition in the identified source/destination matrix operand and zero unconfigured columns of identified source/destination matrix operand are detailed.

Systems, methods, and apparatuses for tile matrix multiplication and accumulation

Embodiments of an invention a processor architecture are disclosed. In an embodiment, a processor includes a decoder, an execution unit, a coherent cache, and an interconnect. The decoder is to decode an instruction to zero a cache line. The execution unit is to issue a write command to initiate a cache line sized write of zeros. The coherent cache is to receive the write command, to determine whether there is a hit in the coherent cache and whether a cache coherency protocol state of the hit cache line is a modified state or an exclusive state, to configure a cache line to indicate all zeros, and to issue the write command toward the interconnect. The interconnect is to, responsive to receipt of the write command, issue a snoop to each of a plurality of other coherent caches for which it must be determined if there is a hit.

Apparatuses and methods for a processor architecture

Growing community of inventors

Tigard, OR, United States of America

Bret L Toll