Computing is at a profound inflection
point, economically and technically.
The end of Dennard scaling and its
implications for continuing semiconductor-
design advances, the shift to
mobile and cloud computing, the explosive
growth of scientific, business,
government, and consumer data and
opportunities for data analytics and
machine learning, and the continuing
need for more-powerful computing
systems to advance science and engineering
are the context for the debate
over the future of exascale computing
and big data analysis. However, certain
things are clear:
Big data and exascale. High-end data
analytics (big data) and high-end computing
(exascale) are both essential
elements of an integrated computing
research-and-development agenda;
neither should be sacrificed or minimized
to advance the other;
Algorithms, software, applications.
Research and development of nextgeneration
algorithms, software, and
applications is as crucial as investment
in semiconductor devices and hardware;
historically the research community
has underinvested in these areas;
Information technology ecosystem.
The global information technology
ecosystem is in flux, with the transition
to a new generation of low-power mobile
devices, cloud services, and rich
data analytics; andPrivate and global research. Privatesector
competition and global-research
collaboration are both necessary
to address design, test, and deploy
exascale-class computing and dataanalysis
capabilities.
There are great opportunities and
great challenges in advanced computing,
in both computation and data
analysis. Scientific discovery via computational
science and data analytics
is truly the “endless frontier” about
which Vannevar Bush spoke so eloquently
in 1945. The challenges are
for all of computer science to sustain
the research, development, and deployment
of the high-performance
computing infrastructure needed to
enable those discoveries