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公开(公告)号:US11138048B2
公开(公告)日:2021-10-05
申请号:US15391549
申请日:2016-12-27
Applicant: Intel Corporation
Inventor: Rajkishore Barik , Stephan A. Herhut , Jaswanth Sreeram , Tatiana Shpeisman , Richard L. Hudson
Abstract: A work stealer apparatus includes a determination module. The determination module is to determine to steal work from a first hardware computation unit of a first type for a second hardware computation unit of a second type that is different than the first type. The work is to be queued in a first work queue, which is to correspond to the first hardware computation unit, and which is to be stored in a shared memory that is to be shared by the first and second hardware computation units. A synchronized work stealer module is to steal the work through a synchronized memory access to the first work queue. The synchronized memory access is to be synchronized relative to memory accesses to the first work queue from the first hardware computation unit.
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公开(公告)号:US20210294649A1
公开(公告)日:2021-09-23
申请号:US17206514
申请日:2021-03-19
Applicant: Intel Corporation
Inventor: Chandrasekaran Sakthivel , Prasoonkumar Surti , John C. Weast , Sara S. Baghsorkhi , Justin E. Gottschlich , Abhishek R. Appu , Nicolas C. Galoppo Von Borries , Joydeep Ray , Narayan Srinivasa , Feng Chen , Ben J. Ashbaugh , Rajkishore Barik , Tsung-Han Lin , Kamal Sinha , Eriko Nurvitadhi , Balaji Vembu , Altug Koker
Abstract: In an example, an apparatus comprises a plurality of processing unit cores, a plurality of cache memory modules associated with the plurality of processing unit cores, and a machine learning model communicatively coupled to the plurality of processing unit cores, wherein the plurality of cache memory modules share cache coherency data with the machine learning model. Other embodiments are also disclosed and claimed.
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53.
公开(公告)号:US11080046B2
公开(公告)日:2021-08-03
申请号:US17169232
申请日:2021-02-05
Applicant: Intel Corporation
Inventor: Himanshu Kaul , Mark A. Anders , Sanu K. Mathew , Anbang Yao , Joydeep Ray , Ping T. Tang , Michael S. Strickland , Xiaoming Chen , Tatiana Shpeisman , Abhishek R. Appu , Altug Koker , Kamal Sinha , Balaji Vembu , Nicolas C. Galoppo Von Borries , Eriko Nurvitadhi , Rajkishore Barik , Tsung-Han Lin , Vasanth Ranganathan , Sanjeev Jahagirdar
IPC: G06F9/302 , G06F7/483 , G06N3/04 , G06F17/16 , G06F9/30 , G09G5/393 , G06F7/544 , G06F9/38 , G06N3/08 , G06N3/063 , G06N20/00 , G06T15/00
Abstract: A processing apparatus is provided comprising a multiprocessor having a multithreaded architecture. The multiprocessor can execute at least one single instruction to perform parallel mixed precision matrix operations. In one embodiment the apparatus includes a memory interface and an array of multiprocessors coupled to the memory interface. At least one multiprocessor in the array of multiprocessors is configured to execute a fused multiply-add instruction in parallel across multiple threads.
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公开(公告)号:US10943325B2
公开(公告)日:2021-03-09
申请号:US16930841
申请日:2020-07-16
Applicant: Intel Corporation
Inventor: Eriko Nurvitadhi , Balaji Vembu , Tsung-Han Lin , Kamal Sinha , Rajkishore Barik , Nicolas C. Galoppo Von Borries
IPC: G06F17/16 , G06T1/20 , G06F9/30 , G06T1/60 , G06K9/62 , G06F12/0888 , G06F12/0815 , H03M7/30 , G06F9/48 , G06T15/00 , G06N3/04 , G06F9/38 , G06F12/0831 , G06F12/0811 , G06N3/08 , G06N20/00
Abstract: Techniques to improve performance of matrix multiply operations are described in which a compute kernel can specify one or more element-wise operations to perform on output of the compute kernel before the output is transferred to higher levels of a processor memory hierarchy.
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55.
公开(公告)号:US10706496B2
公开(公告)日:2020-07-07
申请号:US16282553
申请日:2019-02-22
Applicant: INTEL CORPORATION
Inventor: Brian T. Lewis , Rajkishore Barik , Tatiana Shpeisman
Abstract: Generally, this disclosure provides systems, devices, methods and computer readable media for implementing function callback requests between a first processor (e.g., a GPU) and a second processor (e.g., a CPU). The system may include a shared virtual memory (SVM) coupled to the first and second processors, the SVM configured to store at least one double-ended queue (Deque). An execution unit (EU) of the first processor may be associated with a first of the Deques and configured to push the callback requests to that first Deque. A request handler thread executing on the second processor may be configured to: pop one of the callback requests from the first Deque; execute a function specified by the popped callback request; and generate a completion signal to the EU in response to completion of the function.
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公开(公告)号:US10417731B2
公开(公告)日:2019-09-17
申请号:US15494886
申请日:2017-04-24
Applicant: Intel Corporation
Inventor: Prasoonkumar Surti , Narayan Srinivasa , Feng Chen , Joydeep Ray , Ben J. Ashbaugh , Nicolas C. Galoppo Von Borries , Eriko Nurvitadhi , Balaji Vembu , Tsung-Han Lin , Kamal Sinha , Rajkishore Barik , Sara S. Baghsorkhi , Justin E. Gottschlich , Altug Koker , Nadathur Rajagopalan Satish , Farshad Akhbari , Dukhwan Kim , Wenyin Fu , Travis T. Schluessler , Josh B. Mastronarde , Linda L. Hurd , John H. Feit , Jeffery S. Boles , Adam T. Lake , Karthik Vaidyanathan , Devan Burke , Subramaniam Maiyuran , Abhishek R. Appu
Abstract: An apparatus to facilitate compute optimization is disclosed. The apparatus includes a plurality of processing units each comprising a plurality of execution units (EUs), wherein the plurality of EUs comprise a first EU type and a second EU type.
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公开(公告)号:US20190243764A1
公开(公告)日:2019-08-08
申请号:US16277267
申请日:2019-02-15
Applicant: Intel Corporation
Inventor: Chandrasekaran Sakthivel , Prasoonkumar Surti , John C. Weast , Sara S. Baghsorkhi , Justin E. Gottschlich , Abhishek R. Appu , Nicolas C. Galoppo Von Borries , Joydeep Ray , Narayan Srinivasa , Feng Chen , Ben J. Ashbaugh , Rajkishore Barik , Tsung-Han Lin , Kamal Sinha , Eriko Nurvitadhi , Balaji Vembu , Altug Koker
IPC: G06F12/0837 , G06N20/00 , G06T1/20 , G06N3/08
CPC classification number: G06F12/0837 , G06F12/0815 , G06F2212/62 , G06N3/0445 , G06N3/0454 , G06N3/063 , G06N3/08 , G06N3/084 , G06N3/088 , G06N20/00 , G06T1/20
Abstract: In an example, an apparatus comprises a plurality of processing unit cores, a plurality of cache memory modules associated with the plurality of processing unit cores, and a machine learning model communicatively coupled to the plurality of processing unit cores, wherein the plurality of cache memory modules share cache coherency data with the machine learning model. Other embodiments are also disclosed and claimed.
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58.
公开(公告)号:US10353706B2
公开(公告)日:2019-07-16
申请号:US15819152
申请日:2017-11-21
Applicant: Intel Corporation
Inventor: Himanshu Kaul , Mark A. Anders , Sanu K. Mathew , Anbang Yao , Joydeep Ray , Ping T. Tang , Michael S. Strickland , Xiaoming Chen , Tatiana Shpeisman , Abhishek R. Appu , Altug Koker , Kamal Sinha , Balaji Vembu , Nicolas C. Galoppo Von Borries , Eriko Nurvitadhi , Rajkishore Barik , Tsung-Han Lin , Vasanth Ranganathan , Sanjeev Jahagirdar
IPC: G06F9/30 , G06F9/38 , G06F7/483 , G06F7/544 , G06N3/04 , G09G5/393 , G06N3/08 , G06N3/063 , G06T15/00 , G06N20/00
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 a 32-bit intermediate product of 16-bit operands and to compute a 32-bit sum based on the 32-bit intermediate product.
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公开(公告)号:US20190080429A1
公开(公告)日:2019-03-14
申请号:US16185965
申请日:2018-11-09
Applicant: Intel Corporation
Inventor: Rajkishore Barik , Tatiana Shpeisman , Brian T. Lewis , Rashid Kaleem
CPC classification number: G06T1/20 , G06F3/14 , G09G5/001 , G09G5/363 , G09G2360/08
Abstract: Generally, this disclosure provides systems, devices, methods and computer readable media for adaptive scheduling of task assignment among heterogeneous processor cores. The system may include any number of CPUs, a graphics processing unit (GPU) and memory configured to store a pool of work items to be shared by the CPUs and GPU. The system may also include a GPU proxy profiling module associated with one of the CPUs to profile execution of a first portion of the work items on the GPU. The system may further include profiling modules, each associated with one of the CPUs, to profile execution of a second portion of the work items on each of the CPUs. The measured profiling information from the CPU profiling modules and the GPU proxy profiling module is used to calculate a distribution ratio for execution of a remaining portion of the work items between the CPUs and the GPU.
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公开(公告)号:US10186011B2
公开(公告)日:2019-01-22
申请号:US15581182
申请日:2017-04-28
Applicant: Intel Corporation
Inventor: Eriko Nurvitadhi , Balaji Vembu , Nicolas C. Galoppo Von Borries , Rajkishore Barik , Tsung-Han Lin , Kamal Sinha , Nadathur Rajagopalan Satish , Jeremy Bottleson , Farshad Akhbari , Altug Koker , Narayan Srinivasa , Dukhwan Kim , Sara S. Baghsorkhi , Justin E. Gottschlich , Feng Chen , Elmoustapha Ould-Ahmed-Vall , Kevin Nealis , Xiaoming Chen , Anbang Yao
Abstract: One embodiment provides for a compute apparatus to perform machine learning operations, the compute apparatus comprising a decode unit to decode a single instruction into a decoded instruction, the decoded instruction to cause the compute apparatus to perform a complex machine learning compute operation.
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