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4 DISCUSSIONS
From the user’s standpoint, to obtain optimal result
accuracy should always be preferred to speed. Most
of the sequence aligners mentioned above complies
with this principle.
From above result we can see that even though
some sequence alignment algorithms such as
BLAST and MUMmer are not intrinsically suitable
for parallelization, they still get considerable
speedup without loss of accuracy. At the same time,
the performance per watt and price-performance of
GPU is better for most of the sequence aligners.
GPU computing is still a low-cost and energy-
efficient solution for high performance computing.
The programming complexity of CUDA slows
down the popularization of GPU computing in some
extent. But with the release of new NVIDIA GPU
compute architecture and the spread of some parallel
computing standards such as OpenACC (OpenACC,
2012) and OpenHMPP (OpenHMPP, 2012), GPU
has arguably become as easy, if not easier, to
program than multicore CPUs.
From the four factors discussed above we can see
that GPU computing is a sound choice for sequence
alignment. But there are more issues you may care
about. First, we can see that the existing GPU-based
sequence aligners are far from exploiting the
computation capability of GPU, though accelerate
the alignment to some extent. Second, further
development is needed for the usability of GPU-
based aligners. In the result, CUDASW++ is faster
and more accurate than NCBI BLAST. So why not
to choose CUDASW++? Usability is an important
factor that influences the user’s choice. The GPU-
based aligners are mainly developed for academic
research, most of which lacks later maintenance and
upgrade. The features of these GPU-based aligners
are far less than that of CPU-based aligners. The
solution of usability calls for more professional
programmers and algorithm designers to help with
the research of bioinformatics.
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