  Modern GPUs are able to perform significantly more arithmetic operations than
transfers of a single word to or from global memory. Hence, many GPU kernels
are limited by memory bandwidth and cannot exploit the arithmetic power of
GPUs. However, the memory locality can be often improved by kernel fusion when
a sequence of kernels is executed and some kernels in this sequence share data.
  In this paper, we show how kernels performing map, reduce or their nested
combinations can be fused automatically by our source-to-source compiler. To
demonstrate the usability of the compiler, we have implemented several BLAS-1
and BLAS-2 routines and show how the performance of their sequences can be
improved by fusions.
  Compared to similar sequences using CUBLAS, our compiler is able to generate
code that is up to 2.61x faster for the examples tested.
