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Execution Model - SLING user documentation

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Graphics processing units with many SPs are ideal for accelerating data-parallel problems. Typical data-parallel problems involve computations with large vectors/matrices or images, where the same operation is performed on thousands or even millions of data simultaneously. To take advantage of such massive parallelism offered to us by GPUs, we need to divide our programs into thousands of threads. As a rule, a thread on a GPU performs a sequence of operations on a particular data (for example, one element of the matrix), and this sequence of operations is usually independent of the same operations performed by other threads on other data. The program written in this way is transferred to a GPU, where the internal thread schedulers assign threads to SMs and SPs. An execution model provides a view of how a particular computing architecture executes instructions. The CUDA execution model exposes an abstract view of the GPU parallel architecture, allowing you to reason about thread concurr

Execution model 1 Graphics processing units with many SPs are ideal for accelerating data-parallel problems . Typical data-parallel problems involve computations with large vectors/matrices or images, where the same operation is performed on thousands or even millions of data simultaneously. To take advantage of such massive parallelism offered to us by GPUs, we need to divide our programs into thousands of threads. As a rule, a thread on a GPU performs a sequence of operations on a particular data (for example, one element of the matrix), and this sequence of operations is usually independent

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