drivanov commented on a change in pull request #16218: Improving performance of
argmax operator
URL: https://github.com/apache/incubator-mxnet/pull/16218#discussion_r327781264
##########
File path: src/operator/tensor/broadcast_reduce_op.h
##########
@@ -556,6 +556,179 @@ inline bool ReduceAxesOpForwardStorage(const
nnvm::NodeAttrs& attrs,
return dispatched;
}
+
+using namespace mshadow_op::isnan_typed;
+
+struct argmax {
+ template<typename DType>
+ MSHADOW_XINLINE static void Map(index_t i, const int nWorkers,
+ const DType *in_data, DType *out_data,
+ uint32_t nSteps, uint32_t step, size_t shift,
+ void *pIdxStorage, bool use_uint16) {
+ // i - index of launched thread
+ // nWorkers - number of threads, assigned to work on one row/column
+ // iw - index of current thread among workers assigned to the same vector
+ int iw = 0;
+ const DType *pCurr = in_data;
+ if (nWorkers > 1) {
+ // in - the vector number which current thread is assigned to
+ const auto in = i / nWorkers;
+ iw = i % nWorkers;
+ pCurr += in % step + shift * (in / step) + iw * step;
+ nSteps = (nSteps + nWorkers - 1 - iw) / nWorkers;
+ step *= nWorkers;
+ } else {
+ pCurr += i % step + shift * (i / step);
+ }
+
+ uint32_t maxIdx = 0;
Review comment:
@apeforest: I could do that, but are you sure that you really need this?
According to following comments from `config.mk`
```
# Use int64_t type to represent the total number of elements in a tensor
# This will cause performance degradation reported in issue #14496
# Set to 1 for large tensor with tensor size greater than INT32_MAX i.e.
2147483647
# Note: the size of each dimension is still bounded by INT32_MAX
USE_INT64_TENSOR_SIZE = 1
```
**the size of each dimension is still bounded by INT32_MAX**. I think, even
`uint32_t` currently used here should be more than enough. Or am I missing
something?
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