  Comprehensive evaluation is one of the basis of experimental science. In
High-Performance Graph Processing, a thorough evaluation of contributions
becomes more achievable by supporting common input formats over different
frameworks. However, each framework creates its specific format, which may not
support reading large-scale real-world graph datasets. This shows a demand for
high-performance libraries capable of loading graphs to (i) accelerate
designing new graph algorithms, (ii) to evaluate the contributions on a wide
range of graph algorithms, and (iii) to facilitate easy and fast comparison
over different graph frameworks.
  To that end, we present ParaGrapher, a high-performance API and library for
loading large-scale and compressed graphs. ParaGrapher supports different types
of requests for accessing graphs in shared- and distributed-memory and
out-of-core graph processing. We explain the design of ParaGrapher and present
a performance model of graph decompression, which is used for evaluation of
ParaGrapher over three storage types. Our evaluation shows that by
decompressing compressed graphs in WebGraph format, ParaGrapher delivers up to
3.2 times speedup in loading and up to 5.2 times speedup in end-to-end
execution in comparison to the binary and textual formats.
  ParaGrapher is available online on
https://blogs.qub.ac.uk/DIPSA/ParaGrapher/.
