The high
performance computing landscape is shifting from collections of homogeneous nodes
towards heterogeneous systems, in which nodes consist of a combination of
traditional out-of-order execution cores and accelerator devices. Accelerators,
built around GPUs, many-core chips, FPGAs or DSPs, are used to offload
compute-intensive tasks. The advent of this type of systems has brought about a
wide and diverse ecosystem of development platforms, optimization tools and
performance analysis frameworks. This is a review of the state-of-the-art in
performance tools for heterogeneous computing, focusing on the most popular
families of accelerators: GPUs and Intel’s Xeon Phi. We describe current
heterogeneous systems and the development frameworks and tools that can be used
for developing for them. The core of this survey is a review of the performance
models and tools, including simulators, proposed in the literature for these
platforms.
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