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1. 发明申请

US20120011348A1 Matrix Multiplication Operations Using Pair-Wise Load and Splat Operations 有权
标题翻译：使用配对加载和Splat操作的矩阵乘法运算
公开(公告)号：US20120011348A1
公开(公告)日：2012-01-12
申请号：US12834464
申请日：2010-07-12
申请人： Alexandre E. Eichenberger , Michael K. Gschwind , John A. Gunnels , Valentina Salapura
发明人： Alexandre E. Eichenberger , Michael K. Gschwind , John A. Gunnels , Valentina Salapura
IPC分类号： G06F9/302
CPC分类号： G06F9/30043 , G06F9/30014 , G06F9/30032 , G06F9/30036 , G06F9/30109 , G06F9/30112 , G06F9/30145 , G06F9/3887
摘要： Mechanisms for performing a matrix multiplication operation are provided. A vector load operation is performed to load a first vector operand of the matrix multiplication operation to a first target vector register. A pair-wise load and splat operation is performed to load a pair of scalar values of a second vector operand and replicate the pair of scalar values within a second target vector register. An operation is performed on elements of the first target vector register and elements of the second target vector register to generate a partial product of the matrix multiplication operation. The partial product is accumulated with other partial products and a resulting accumulated partial product is stored. This operation may be repeated for a second pair of scalar values of the second vector operand.
摘要翻译：提供了执行矩阵乘法运算的机构。执行向量加载操作以将矩阵乘法运算的第一向量操作数加载到第一目标向量寄存器。执行成对的加载和拼接操作以加载第二向量操作数的一对标量值，并在第二目标向量寄存器内复制一对标量值。对第一目标向量寄存器的元素和第二目标向量寄存器的元素执行操作，以生成矩阵乘法运算的部分乘积。部分产品与其他部分产品一起积累，并存储所得累积的部分产品。对于第二向量操作数的第二对标量值可以重复该操作。

2. 发明授权

US09600281B2 Matrix multiplication operations using pair-wise load and splat operations 有权
公开(公告)号：US09600281B2
公开(公告)日：2017-03-21
申请号：US12834464
申请日：2010-07-12
申请人： Alexandre E. Eichenberger , Michael K. Gschwind , John A. Gunnels , Valentina Salapura
发明人： Alexandre E. Eichenberger , Michael K. Gschwind , John A. Gunnels , Valentina Salapura
IPC分类号： G06F9/30 , G06F9/312 , G06F9/38
CPC分类号： G06F9/30043 , G06F9/30014 , G06F9/30032 , G06F9/30036 , G06F9/30109 , G06F9/30112 , G06F9/30145 , G06F9/3887
摘要： Mechanisms for performing a matrix multiplication operation are provided. A vector load operation is performed to load a first vector operand of the matrix multiplication operation to a first target vector register. A pair-wise load and splat operation is performed to load a pair of scalar values of a second vector operand and replicate the pair of scalar values within a second target vector register. An operation is performed on elements of the first target vector register and elements of the second target vector register to generate a partial product of the matrix multiplication operation. The partial product is accumulated with other partial products and a resulting accumulated partial product is stored. This operation may be repeated for a second pair of scalar values of the second vector operand.

3. 发明申请

US20110219208A1 MULTI-PETASCALE HIGHLY EFFICIENT PARALLEL SUPERCOMPUTER 有权
标题翻译：多层高效平行超级计算机
公开(公告)号：US20110219208A1
公开(公告)日：2011-09-08
申请号：US13004007
申请日：2011-01-10
申请人： Sameh Asaad , Ralph E. Bellofatto , Michael A. Blocksome , Matthias A. Blumrich , Peter Boyle , Jose R. Brunheroto , Dong Chen , Chen-Yong Cher , George L. Chiu , Norman Christ , Paul W. Coteus , Kristan D. Davis , Gabor J. Dozsa , Alexandre E. Eichenberger , Noel A. Eisley , Matthew R. Ellavsky , Kahn C. Evans , Bruce M. Fleischer , Thomas W. Fox , Alan Gara , Mark E. Giampapa , Thomas M. Gooding , Michael K. Gschwind , John A. Gunnels , Shawn A. Hall , Rudolf A. Haring , Philip Heidelberger , Todd A. Inglett , Brant L. Knudson , Gerard V. Kopcsay , Sameer Kumar , Amith R. Mamidala , James A. Marcella , Mark G. Megerian , Douglas R. Miller , Samuel J. Miller , Adam J. Muff , Michael B. Mundy , John K. O'Brien , Kathryn M. O'Brien , Martin Ohmacht , Jeffrey J. Parker , Ruth J. Poole , Joseph D. Ratterman , Valentina Salapura , David L. Satterfield , Robert M. Senger , Brian Smith , Burkhard Steinmacher-Burow , William M. Stockdell , Craig B. Stunkel , Krishnan Sugavanam , Yutaka Sugawara , Todd E. Takken , Barry M. Trager , James L. Van Oosten , Charles D. Wait , Robert E. Walkup , Alfred T. Watson , Robert W. Wisniewski , Peng Wu
发明人： Sameh Asaad , Ralph E. Bellofatto , Michael A. Blocksome , Matthias A. Blumrich , Peter Boyle , Jose R. Brunheroto , Dong Chen , Chen-Yong Cher , George L. Chiu , Norman Christ , Paul W. Coteus , Kristan D. Davis , Gabor J. Dozsa , Alexandre E. Eichenberger , Noel A. Eisley , Matthew R. Ellavsky , Kahn C. Evans , Bruce M. Fleischer , Thomas W. Fox , Alan Gara , Mark E. Giampapa , Thomas M. Gooding , Michael K. Gschwind , John A. Gunnels , Shawn A. Hall , Rudolf A. Haring , Philip Heidelberger , Todd A. Inglett , Brant L. Knudson , Gerard V. Kopcsay , Sameer Kumar , Amith R. Mamidala , James A. Marcella , Mark G. Megerian , Douglas R. Miller , Samuel J. Miller , Adam J. Muff , Michael B. Mundy , John K. O'Brien , Kathryn M. O'Brien , Martin Ohmacht , Jeffrey J. Parker , Ruth J. Poole , Joseph D. Ratterman , Valentina Salapura , David L. Satterfield , Robert M. Senger , Brian Smith , Burkhard Steinmacher-Burow , William M. Stockdell , Craig B. Stunkel , Krishnan Sugavanam , Yutaka Sugawara , Todd E. Takken , Barry M. Trager , James L. Van Oosten , Charles D. Wait , Robert E. Walkup , Alfred T. Watson , Robert W. Wisniewski , Peng Wu
IPC分类号： G06F15/76 , G06F9/06
CPC分类号： G06F13/287 , G06F9/06 , G06F9/3004 , G06F9/30047 , G06F9/3885 , G06F12/0811 , G06F12/0831 , G06F12/0862 , G06F12/0864 , G06F12/1027 , G06F15/17381 , G06F15/17387 , G06F15/76 , G06F15/8069 , G06F2212/1016 , G06F2212/602 , G06F2212/6022 , G06F2212/6024 , G06F2212/6032 , Y02D10/13 , Y02D10/14
摘要： A Multi-Petascale Highly Efficient Parallel Supercomputer of 100 petaOPS-scale computing, at decreased cost, power and footprint, and that allows for a maximum packaging density of processing nodes from an interconnect point of view. The Supercomputer exploits technological advances in VLSI that enables a computing model where many processors can be integrated into a single Application Specific Integrated Circuit (ASIC). Each ASIC computing node comprises a system-on-chip ASIC utilizing four or more processors integrated into one die, with each having full access to all system resources and enabling adaptive partitioning of the processors to functions such as compute or messaging I/O on an application by application basis, and preferably, enable adaptive partitioning of functions in accordance with various algorithmic phases within an application, or if I/O or other processors are underutilized, then can participate in computation or communication nodes are interconnected by a five dimensional torus network with DMA that optimally maximize the throughput of packet communications between nodes and minimize latency.
摘要翻译：具有100 petaOPS规模计算的多Petascale高效并行超级计算机，其成本，功耗和占地面积都在降低，并且允许从互连角度来看处理节点的最大封装密度。超级计算机利用了VLSI的技术进步，实现了许多处理器可以集成到单个专用集成电路（ASIC）中的计算模型。每个ASIC计算节点包括利用集成到一个管芯中的四个或更多个处理器的片上系统ASIC，每个处理器具有对所有系统资源的完全访问，并且使得处理器能够对诸如计算或消息传递I / O 并且优选地，根据应用内的各种算法阶段实现功能的自适应分割，或者如果I / O或其他处理器未被充分利用，则可以参与计算或通信节点通过五维环面网络互连使用DMA来最大限度地最大化节点之间的分组通信的吞吐量并最小化等待时间。

4. 发明授权

US09081501B2 Multi-petascale highly efficient parallel supercomputer 有权
标题翻译：多千兆高效并行超级计算机
公开(公告)号：US09081501B2
公开(公告)日：2015-07-14
申请号：US13004007
申请日：2011-01-10
申请人： Sameh Asaad , Ralph E. Bellofatto , Michael A. Blocksome , Matthias A. Blumrich , Peter Boyle , Jose R. Brunheroto , Dong Chen , Chen-Yong Cher , George L. Chiu , Norman Christ , Paul W. Coteus , Kristan D. Davis , Gabor J. Dozsa , Alexandre E. Eichenberger , Noel A. Eisley , Matthew R. Ellavsky , Kahn C. Evans , Bruce M. Fleischer , Thomas W. Fox , Alan Gara , Mark E. Giampapa , Thomas M. Gooding , Michael K. Gschwind , John A. Gunnels , Shawn A. Hall , Rudolf A. Haring , Philip Heidelberger , Todd A. Inglett , Brant L. Knudson , Gerard V. Kopcsay , Sameer Kumar , Amith R. Mamidala , James A. Marcella , Mark G. Megerian , Douglas R. Miller , Samuel J. Miller , Adam J. Muff , Michael B. Mundy , John K. O'Brien , Kathryn M. O'Brien , Martin Ohmacht , Jeffrey J. Parker , Ruth J. Poole , Joseph D. Ratterman , Valentina Salapura , David L. Satterfield , Robert M. Senger , Brian Smith , Burkhard Steinmacher-Burow , William M. Stockdell , Craig B. Stunkel , Krishnan Sugavanam , Yutaka Sugawara , Todd E. Takken , Barry M. Trager , James L. Van Oosten , Charles D. Wait , Robert E. Walkup , Alfred T. Watson , Robert W. Wisniewski , Peng Wu
发明人： Sameh Asaad , Ralph E. Bellofatto , Michael A. Blocksome , Matthias A. Blumrich , Peter Boyle , Jose R. Brunheroto , Dong Chen , Chen-Yong Cher , George L. Chiu , Norman Christ , Paul W. Coteus , Kristan D. Davis , Gabor J. Dozsa , Alexandre E. Eichenberger , Noel A. Eisley , Matthew R. Ellavsky , Kahn C. Evans , Bruce M. Fleischer , Thomas W. Fox , Alan Gara , Mark E. Giampapa , Thomas M. Gooding , Michael K. Gschwind , John A. Gunnels , Shawn A. Hall , Rudolf A. Haring , Philip Heidelberger , Todd A. Inglett , Brant L. Knudson , Gerard V. Kopcsay , Sameer Kumar , Amith R. Mamidala , James A. Marcella , Mark G. Megerian , Douglas R. Miller , Samuel J. Miller , Adam J. Muff , Michael B. Mundy , John K. O'Brien , Kathryn M. O'Brien , Martin Ohmacht , Jeffrey J. Parker , Ruth J. Poole , Joseph D. Ratterman , Valentina Salapura , David L. Satterfield , Robert M. Senger , Brian Smith , Burkhard Steinmacher-Burow , William M. Stockdell , Craig B. Stunkel , Krishnan Sugavanam , Yutaka Sugawara , Todd E. Takken , Barry M. Trager , James L. Van Oosten , Charles D. Wait , Robert E. Walkup , Alfred T. Watson , Robert W. Wisniewski , Peng Wu
IPC分类号： G06F15/173 , G06F9/06 , G06F15/76
CPC分类号： G06F13/287 , G06F9/06 , G06F9/3004 , G06F9/30047 , G06F9/3885 , G06F12/0811 , G06F12/0831 , G06F12/0862 , G06F12/0864 , G06F12/1027 , G06F15/17381 , G06F15/17387 , G06F15/76 , G06F15/8069 , G06F2212/1016 , G06F2212/602 , G06F2212/6022 , G06F2212/6024 , G06F2212/6032 , Y02D10/13 , Y02D10/14
摘要： A Multi-Petascale Highly Efficient Parallel Supercomputer of 100 petaOPS-scale computing, at decreased cost, power and footprint, and that allows for a maximum packaging density of processing nodes from an interconnect point of view. The Supercomputer exploits technological advances in VLSI that enables a computing model where many processors can be integrated into a single Application Specific Integrated Circuit (ASIC). Each ASIC computing node comprises a system-on-chip ASIC utilizing four or more processors integrated into one die, with each having full access to all system resources and enabling adaptive partitioning of the processors to functions such as compute or messaging I/O on an application by application basis, and preferably, enable adaptive partitioning of functions in accordance with various algorithmic phases within an application, or if I/O or other processors are underutilized, then can participate in computation or communication nodes are interconnected by a five dimensional torus network with DMA that optimally maximize the throughput of packet communications between nodes and minimize latency.
摘要翻译：具有100 petaOPS规模计算的多Petascale高效并行超级计算机，其成本，功耗和占地面积都在降低，并且允许从互连角度来看处理节点的最大封装密度。超级计算机利用了VLSI的技术进步，实现了许多处理器可以集成到单个专用集成电路（ASIC）中的计算模型。每个ASIC计算节点包括利用集成到一个管芯中的四个或更多个处理器的片上系统ASIC，每个处理器具有对所有系统资源的完全访问，并且使得处理器能够对诸如计算或消息传递I / O 并且优选地，根据应用内的各种算法阶段实现功能的自适应分割，或者如果I / O或其他处理器未被充分利用，则可以参与计算或通信节点通过五维环面网络互连使用DMA来最大限度地最大化节点之间的分组通信的吞吐量并最小化等待时间。

5. 发明授权

US08904153B2 Vector loads with multiple vector elements from a same cache line in a scattered load operation 有权
标题翻译：在分散加载操作中，来自相同高速缓存行的多个向量元素的向量加载
公开(公告)号：US08904153B2
公开(公告)日：2014-12-02
申请号：US12876321
申请日：2010-09-07
申请人： Alexandre E. Eichenberger , Michael K. Gschwind , Valentina Salapura
发明人： Alexandre E. Eichenberger , Michael K. Gschwind , Valentina Salapura
IPC分类号： G06F9/345 , G06F9/30 , G06F9/38 , G06F15/80
CPC分类号： G06F9/30018 , G06F9/30032 , G06F9/30036 , G06F9/30043 , G06F9/3857 , G06F15/8069
摘要： Mechanisms for performing a scattered load operation are provided. With these mechanisms, an extended address is received in a cache memory of a processor. The extended address has a plurality of data element address portions that specify a plurality of data elements to be accessed using the single extended address. Each of the plurality of data element address portions is provided to corresponding data element selector logic units of the cache memory. Each data element selector logic unit in the cache memory selects a corresponding data element from a cache line buffer based on a corresponding data element address portion provided to the data element selector logic unit. Each data element selector logic unit outputs the corresponding data element for use by the processor.
摘要翻译：提供了执行分散加载操作的机构。利用这些机制，扩展地址被接收在处理器的高速缓冲存储器中。扩展地址具有多个数据元素地址部分，其指定使用单个扩展地址来访问的多个数据元素。多个数据元素地址部分中的每一个被提供给高速缓冲存储器的相应数据元素选择器逻辑单元。高速缓冲存储器中的每个数据元素选择器逻辑单元基于提供给数据元素选择器逻辑单元的相应数据元素地址部分从高速缓存行缓冲器中选择相应的数据元素。每个数据元素选择器逻辑单元输出相应的数据元素供处理器使用。

6. 发明申请

US20120060016A1 Vector Loads from Scattered Memory Locations 审中-公开
标题翻译：矢量负载从分散的内存位置
公开(公告)号：US20120060016A1
公开(公告)日：2012-03-08
申请号：US12876432
申请日：2010-09-07
申请人： Alexandre E. Eichenberger , Michael K. Gschwind , Valentina Salapura
发明人： Alexandre E. Eichenberger , Michael K. Gschwind , Valentina Salapura
IPC分类号： G06F15/76 , G06F9/02
CPC分类号： G06F9/30032 , G06F9/30018 , G06F9/30036 , G06F9/30043
摘要： Mechanisms for performing a scattered load operation are provided. With these mechanisms, a gather instruction is receive in a logic unit of a processor, the gather instruction specifying a plurality of addresses in a memory from which data is to be loaded into a target vector register of the processor. A plurality of separate load instructions for loading the data from the plurality of addresses in the memory are automatically generated within the logic unit. The plurality of separate load instructions are sent, from the logic unit, to one or more load/store units of the processor. The data corresponding to the plurality of addresses is gathered in a buffer of the processor. The logic unit then writes data stored in the buffer to the target vector register.
摘要翻译：提供了执行分散加载操作的机构。利用这些机制，在处理器的逻辑单元中接收收集指令，所述收集指令指定要从中将数据加载到处理器的目标向量寄存器的存储器中的多个地址。在逻辑单元内自动生成用于从存储器中的多个地址加载数据的多个单独的加载指令。多个单独的加载指令从逻辑单元发送到处理器的一个或多个加载/存储单元。对应于多个地址的数据被收集在处理器的缓冲器中。然后，逻辑单元将存储在缓冲器中的数据写入目标向量寄存器。

7. 发明申请

US20120060015A1 Vector Loads with Multiple Vector Elements from a Same Cache Line in a Scattered Load Operation 有权
标题翻译：在分散负载操作中，来自相同高速缓存行的多个向量元素的向量负载
公开(公告)号：US20120060015A1
公开(公告)日：2012-03-08
申请号：US12876321
申请日：2010-09-07
申请人： Alexandre E. Eichenberger , Michael K. Gschwind , Valentina Salapura
发明人： Alexandre E. Eichenberger , Michael K. Gschwind , Valentina Salapura
IPC分类号： G06F9/30 , G06F12/08
CPC分类号： G06F9/30018 , G06F9/30032 , G06F9/30036 , G06F9/30043 , G06F9/3857 , G06F15/8069
摘要： Mechanisms for performing a scattered load operation are provided. With these mechanisms, an extended address is received in a cache memory of a processor. The extended address has a plurality of data element address portions that specify a plurality of data elements to be accessed using the single extended address. Each of the plurality of data element address portions is provided to corresponding data element selector logic units of the cache memory. Each data element selector logic unit in the cache memory selects a corresponding data element from a cache line buffer based on a corresponding data element address portion provided to the data element selector logic unit. Each data element selector logic unit outputs the corresponding data element for use by the processor.
摘要翻译：提供了执行分散加载操作的机构。利用这些机制，扩展地址被接收在处理器的高速缓冲存储器中。扩展地址具有多个数据元素地址部分，其指定使用单个扩展地址来访问的多个数据元素。多个数据元素地址部分中的每一个被提供给高速缓冲存储器的相应数据元素选择器逻辑单元。高速缓冲存储器中的每个数据元素选择器逻辑单元基于提供给数据元素选择器逻辑单元的相应数据元素地址部分从高速缓存线缓冲器中选择相应的数据元素。每个数据元素选择器逻辑单元输出相应的数据元素供处理器使用。

8. 发明授权

US08577950B2 Matrix multiplication operations with data pre-conditioning in a high performance computing architecture 失效
标题翻译：在高性能计算架构中使用数据预处理的矩阵乘法运算
公开(公告)号：US08577950B2
公开(公告)日：2013-11-05
申请号：US12542255
申请日：2009-08-17
申请人： Alexandre E. Eichenberger , Michael K. Gschwind , John A. Gunnels
发明人： Alexandre E. Eichenberger , Michael K. Gschwind , John A. Gunnels
IPC分类号： G06F7/52
CPC分类号： G06F17/16 , G06F9/30014 , G06F9/30029 , G06F9/30032 , G06F9/30036 , G06F9/30043 , G06F9/30072 , G06F9/30094 , G06F9/30109 , G06F9/3013 , G06F9/3885 , G06F9/3887
摘要： Mechanisms for performing matrix multiplication operations with data pre-conditioning in a high performance computing architecture are provided. A vector load operation is performed to load a first vector operand of the matrix multiplication operation to a first target vector register. A load and splat operation is performed to load an element of a second vector operand and replicating the element to each of a plurality of elements of a second target vector register. A multiply add operation is performed on elements of the first target vector register and elements of the second target vector register to generate a partial product of the matrix multiplication operation. The partial product of the matrix multiplication operation is accumulated with other partial products of the matrix multiplication operation.
摘要翻译：提供了用于在高性能计算架构中执行数据预处理的矩阵乘法运算的机制。执行向量加载操作以将矩阵乘法运算的第一向量操作数加载到第一目标向量寄存器。执行加载和拼接操作以加载第二向量操作数的元素并将元素复制到第二目标向量寄存器的多个元素中的每一个。对第一目标向量寄存器的元素和第二目标向量寄存器的元素执行乘法加法运算，以生成矩阵乘法运算的部分乘积。矩阵乘法运算的部分乘积与矩阵乘法运算的其他部分积积累。

9. 发明申请

US20120290816A1 Optimized Scalar Promotion with Load and Splat SIMD Instructions 失效
标题翻译：通过加载和Splat SIMD指令优化标量升级
公开(公告)号：US20120290816A1
公开(公告)日：2012-11-15
申请号：US13555435
申请日：2012-07-23
申请人： Alexandre E. Eichenberger , Michael K. Gschwind , John A. Gunnels
发明人： Alexandre E. Eichenberger , Michael K. Gschwind , John A. Gunnels
IPC分类号： G06F9/30
CPC分类号： G06F8/45
摘要： Mechanisms for optimizing scalar code executed on a single instruction multiple data (SIMD) engine are provided. Placement of vector operation-splat operations may be determined based on an identification of scalar and SIMD operations in an original code representation. The original code representation may be modified to insert the vector operation-splat operations based on the determined placement of vector operation-splat operations to generate a first modified code representation. Placement of separate splat operations may be determined based on identification of scalar and SIMD operations in the first modified code representation. The first modified code representation may be modified to insert or delete separate splat operations based on the determined placement of the separate splat operations to generate a second modified code representation. SIMD code may be output based on the second modified code representation for execution by the SIMD engine.
摘要翻译：提供了在单指令多数据（SIMD）引擎上执行的优化标量代码的机制。可以基于原始代码表示中的标量和SIMD操作的标识来确定矢量操作 - 拼接操作的放置。可以修改原始代码表示以基于所确定的向量操作 - 分组操作的放置来插入向量操作 - 拼接操作以生成第一修改代码表示。可以基于第一修改代码表示中的标量和SIMD操作的标识来确定单独的拼接操作的放置。可以修改第一修改代码表示以基于确定的单独splat操作的布局来插入或删除单独的splat操作以生成第二修改代码表示。可以基于SIMD引擎执行的第二修改代码表示来输出SIMD代码。

10. 发明申请

US20110040821A1 Matrix Multiplication Operations with Data Pre-Conditioning in a High Performance Computing Architecture 失效
标题翻译：在高性能计算架构中使用数据预处理的矩阵乘法运算
公开(公告)号：US20110040821A1
公开(公告)日：2011-02-17
申请号：US12542255
申请日：2009-08-17
申请人： Alexandre E. Eichenberger , Michael K. Gschwind , John A. Gunnels
发明人： Alexandre E. Eichenberger , Michael K. Gschwind , John A. Gunnels
IPC分类号： G06F17/16 , G06F7/52
CPC分类号： G06F17/16 , G06F9/30014 , G06F9/30029 , G06F9/30032 , G06F9/30036 , G06F9/30043 , G06F9/30072 , G06F9/30094 , G06F9/30109 , G06F9/3013 , G06F9/3885 , G06F9/3887
摘要： Mechanisms for performing matrix multiplication operations with data pre-conditioning in a high performance computing architecture are provided. A vector load operation is performed to load a first vector operand of the matrix multiplication operation to a first target vector register. A load and splat operation is performed to load an element of a second vector operand and replicating the element to each of a plurality of elements of a second target vector register. A multiply add operation is performed on elements of the first target vector register and elements of the second target vector register to generate a partial product of the matrix multiplication operation. The partial product of the matrix multiplication operation is accumulated with other partial products of the matrix multiplication operation.
摘要翻译：提供了用于在高性能计算架构中执行数据预处理的矩阵乘法运算的机制。执行向量加载操作以将矩阵乘法运算的第一向量操作数加载到第一目标向量寄存器。执行加载和拼接操作以加载第二向量操作数的元素并将元素复制到第二目标向量寄存器的多个元素中的每一个。对第一目标向量寄存器的元素和第二目标向量寄存器的元素执行乘法加法运算，以生成矩阵乘法运算的部分乘积。矩阵乘法运算的部分乘积与矩阵乘法运算的其他部分积积累。

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