  Parallel computing using accelerators has gained widespread research
attention in the past few years. In particular, using GPUs for general purpose
computing has brought forth several success stories with respect to time taken,
cost, power, and other metrics. However, accelerator based computing has
signifi- cantly relegated the role of CPUs in computation. As CPUs evolve and
also offer matching computational resources, it is important to also include
CPUs in the computation. We call this the hybrid computing model. Indeed, most
computer systems of the present age offer a degree of heterogeneity and
therefore such a model is quite natural.
  We reevaluate the claim of a recent paper by Lee et al.(ISCA 2010). We argue
that the right question arising out of Lee et al. (ISCA 2010) should be how to
use a CPU+GPU platform efficiently, instead of whether one should use a CPU or
a GPU exclusively. To this end, we experiment with a set of 13 diverse
workloads ranging from databases, image processing, sparse matrix kernels, and
graphs. We experiment with two different hybrid platforms: one consisting of a
6-core Intel i7-980X CPU and an NVidia Tesla T10 GPU, and another consisting of
an Intel E7400 dual core CPU with an NVidia GT520 GPU. On both these platforms,
we show that hybrid solutions offer good advantage over CPU or GPU alone
solutions. On both these platforms, we also show that our solutions are 90%
resource efficient on average.
  Our work therefore suggests that hybrid computing can offer tremendous
advantages at not only research-scale platforms but also the more realistic
scale systems with significant performance gains and resource efficiency to the
large scale user community.
