Calculate conditional mean

开发者_StackOverflow社区I'm new to cuda programming and am interested in implementing an algorithm that when coded serially calculates two or more means from a vector in one pass. What would be an efficient scheme for doing something like this in cuda?

There are two vectors of length N, element values and an indicator values identifying which subset each element belongs to.

Is there an efficient way to do this in one pass or should this be done in M passes, where M is the number of means to be calcuated and use a vector of index keys for the element values of each subset?

You can achieve this with one pass over the data with a single call to thrust::reduce_by_key. In particular, look at the "summary statistics" example, which computes several statistical properties of a single vector at once. You could generalize this method to reduce_by_key which computes reductions over many sub-vectors in parallel. Your "indicator values" would provide be the "keys" reduce_by_key uses to determine which sub-vector each element belongs to.

Partition each vector into smaller vectors and use threads to sum required elements of each sub vector. Then combine the sums and generate the global means. I would try to generate the M means at the same time rather than do M passes.