ALLREDUCE ENHANCED DIRECT MEMORY ACCESS FUNCTIONALITY
Systems, apparatuses, and methods for performing an allreduce operation on an enhanced direct memory access (DMA) engine are disclosed. A system implements a machine learning application which includes a first kernel and a second kernel. The first kernel corresponds to a first portion of a machine l...
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| creator | Vishnu, Abhinav Greathouse, Joseph Lee |
| description | Systems, apparatuses, and methods for performing an allreduce operation on an enhanced direct memory access (DMA) engine are disclosed. A system implements a machine learning application which includes a first kernel and a second kernel. The first kernel corresponds to a first portion of a machine learning model while the second kernel corresponds to a second portion of the machine learning model. The first kernel is invoked on a plurality of compute units and the second kernel is converted into commands executable by an enhanced DMA engine to perform a collective communication operation. The first kernel is executed on the plurality of compute units in parallel with the enhanced DMA engine executing the commands for performing the collective communication operation. As a result, the allreduce operation may be executed in parallel on the enhanced DMA engine to the compute units. |
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A system implements a machine learning application which includes a first kernel and a second kernel. The first kernel corresponds to a first portion of a machine learning model while the second kernel corresponds to a second portion of the machine learning model. The first kernel is invoked on a plurality of compute units and the second kernel is converted into commands executable by an enhanced DMA engine to perform a collective communication operation. The first kernel is executed on the plurality of compute units in parallel with the enhanced DMA engine executing the commands for performing the collective communication operation. 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A system implements a machine learning application which includes a first kernel and a second kernel. The first kernel corresponds to a first portion of a machine learning model while the second kernel corresponds to a second portion of the machine learning model. The first kernel is invoked on a plurality of compute units and the second kernel is converted into commands executable by an enhanced DMA engine to perform a collective communication operation. The first kernel is executed on the plurality of compute units in parallel with the enhanced DMA engine executing the commands for performing the collective communication operation. As a result, the allreduce operation may be executed in parallel on the enhanced DMA engine to the compute units.</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS |
| title | ALLREDUCE ENHANCED DIRECT MEMORY ACCESS FUNCTIONALITY |
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