Improve/optimize mGPU scaling via batched prefetching and sorting changes#499
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Signed-off-by: Matthew Cong <mcong@nvidia.com>
Signed-off-by: Matthew Cong <mcong@nvidia.com>
Signed-off-by: Matthew Cong <mcong@nvidia.com>
Signed-off-by: Matthew Cong <mcong@nvidia.com>
Signed-off-by: Matthew Cong <mcong@nvidia.com>
Signed-off-by: Matthew Cong <mcong@nvidia.com>
Signed-off-by: Matthew Cong <mcong@nvidia.com>
Signed-off-by: Matthew Cong <mcong@nvidia.com>
Signed-off-by: Matthew Cong <mcong@nvidia.com>
Signed-off-by: Matthew Cong <mcong@nvidia.com>
Signed-off-by: Matthew Cong <mcong@nvidia.com>
Signed-off-by: Matthew Cong <mcong@nvidia.com>
Signed-off-by: Matthew Cong <mcong@nvidia.com>
Signed-off-by: Matthew Cong <mcong@nvidia.com>
Signed-off-by: Matthew Cong <mcong@nvidia.com>
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Signed-off-by: Matthew Cong <mcong@nvidia.com>
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harrism
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Looks good. I'd like you to document the two new utility functions before merging.
Signed-off-by: Matthew Cong <mcong@nvidia.com>
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cudaMemPrefetchAsynccalls into a singlecudaMemPrefetchBatchAsynccall in order to amortize OS overheadDeviceRadixSorttoDeviceMergeSortfor mGPU. In mGPU, the performance advantage of radix sort is outweighed by the additional cost of allocating separate input and output buffers.