I'd prefer a gpuarray implementation with similar interface as numpy: gpuarray.sort(arr, [axis=-1], [kind='radixsort'], [order='inc'])
Deep Learning folks would need a fast batched version, especially float32 / int32 tensors on GPU. But anyway there should be a general algorithm deals with all cases, never know what kind of model would come up in future. On Thursday, June 15, 2017 at 4:01:00 AM UTC+8, Victor Campmany wrote: > > Hi, > > We are planning to implement a new GPU accelerated sorting algorithm. We'd > like to know which are the most frequent sorting cases that you guys use > and the data sizes you are dealing with. For example, sorting a large 1d > array, sorting a given axis of a tensor or minibatch, or any other type of > sorting you come up with. > -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
