all right thank you all for coming and I really want to say a thank you to
AGU you and of course to NASA for having me and for sponsoring this whole
competition. I'm really excited to share this presentation which is going to talk
about a GPU acceleration for data visualization of NASA Earth science data
and also for machine learning
earth scientists today have a sort of problem
that they never really thought they'd have which is simply having too much
data right now there are about 50 NASA or science satellites in orbit each
producing terabytes of data every single day and I think by 2022 the amount of
new data that's actually being produced is going to increase to about a hundred
and fifty terabytes a day and obviously even though having more data is great it
doesn't really mean very much unless people can understand it people can find
patterns in it and people are able to really understand what their data says
the process of actually visualizing data which I think is essential to the
process of understanding data is already quite complicated, because this data is
so big there's a lot that has to go into being able to make it small enough and
manageable enough to visualize. Originally when you have data it's
unstructured, it needs to be gridded, it needs to be projected onto a map and you
need to generate these tile pyramids which are basically lower resolution
versions of the original data which can be viewed on a web browser and
manipulated this process is great but it's quite slow it takes a lot of
storage and it's not interactive meaning that if you want to change details of
the data if you want to view it using a different color map, a different scale,
you would like to apply algorithms or resample it, that becomes very very
difficult, at the same time there are a lot of questions that you'd like to ask
about your data they can be more quantitative in nature as opposed to
visualization, this can include seeing time series or exploratory data analysis
with clustering or even applying machine learning algorithms and because data can
be so large right now because this data can be gigabytes and gigabytes these
algorithms tend to be very slow if you want to see a time series
that spans years and years it can take hours to compute and I think that one of
the most essential things for really being able to understand data is having
a kind of intuitive data exploration process where as a scientist you form
questions about your data. You say is there a trend here in this small part of
the world, do I see patterns, do I see trends and you can ask those questions
then have them answer it intuitively you can explore and manipulate in a really
intuitive and flexible and powerful way
right now current methods are all CPU based which means that they use this
very traditional method of doing computation that iterates over every
single data point in an image or in a data set and applies some kind of
algorithm. GPUs are a new kind of hardware which allow you to apply
algorithms in parallel across all of the data points in your data set that, means
that if you want to apply a color map or reproject your data or ask questions
like what is the mean or the average or how does this change over time all of
that can be done almost instantaneously because the computations are actually
performed in parallel on all of your data, these technologies are widely used
today in machine learning and in computer graphics but they have not been
adopted as widely in the earth science community
Fundamentally that's what this
presentation is about it is presenting a new GPU visualization prototype a kind
of software library that allows you to work directly with raw earth science
data. You have new data that has just come down from a satellite its
unstructured it hasn't been gridded, it hasn't been processed and you can simply
load that and grid the data, you can project it, you can rescale it, you can
choose for instance a logarithmic scale instead of a linear scale, you can apply
recent machine learning algorithms for visualization or find data analysis and
correlation and all of this can be done extremely quickly and extremely flexibly
using this new technology questions that currently with technologies like the
science data analytics platform or with NASA Gibbs things that are
either impossible or take a very long time can be done almost instantly and
that that speed is what makes I think data exploration really powerful and
really easy to do
here's an example using NASA MIR surface temperature
data all of this data is generated dynamically on the fly at multiple
resolutions and you can view it in a web browser
so these multi resolution tiles which before would have to be pre generated
and pre-processed taking hours and additional storage can be generated in
real time on the GPU tiles can also be generated directly from l1 or l2 data
meaning that it doesn't even need to be pre processed and that means that you
can actually reduce the amount of storage that you need, here for instance
you can view this in a logarithmic scale which is something that's completely
impossible now with data that has already been gridded and cut applied a
color map has already been implied because you're actually working with
binned data instead of the raw scientific data so by working directly
with raw data you're actually able to work with full scientific fidelity while
you're building these visualizations, here's another example where you're
actually just able to zoom in in a region of interest and because you're
working directly with the raw data you can view for instance the mean, the max
the standard deviation and the variance of the region that you're viewing in the
current view, so simply by zooming in on a region of the map you're able to ask
questions about how does the temperature in this region differ from other parts
of the world how does that change, here also for instance you can apply computer
vision filters this is a sobel filter which shows gradients basically regions
in this ocean where there are large temperature gradients and this can be
generated from the raw data just as easily as viewing any other kind of NASA
data so by working directly with raw data, you really have this advantage of
being able to manipulate and visualize your data in real time flexibly and intuitively
as sort of an example of the way that this can be used in earth
science. We're looking at some NASA ocean data we have the MIR surface
temperature and AVHARR temperature anomaly and then a variety
of ECCO products and we can ask questions about these, we can say is there a cor...
other correlations in these products, how do they change over time, are there
variants, are there important differences within different parts of the world and
simply by visualizing it conveniently we can answer these questions almost
instantly here is an example we can ask questions about the El Nino and La Nina
effects, so this is part of this climate cycle in the Southern Pacific Ocean that
occurs off the coast of Ecuador it's a approximately four year cycle
where this band off the coast of South America goes through higher than average
and lower than average temperature gradients, so in 2015 there was a very
intense El Nino period where the temperature was increased by as much as
3 degrees Celsius compared to the average the advantage of this kind of
technology is that you can actually generate this anomaly data that is how
much does the temperature differ from historic patterns without precomputing
it you can simply say look at past data look at the current data and D season or
remove this historic patterns so you can see this dynamic data without actually
computing or storing this anomaly data separately so
here for instance we have
this a AVHRR sea surface temperature data we're off the coast of South
America right now and we can see that all of this data is dynamically
generated so you aren't using gigabytes and gigabytes of additional data and you
can simply zoom in and out dynamically like you could with a NASA product like
world view you can see how it looks you can experiment you can move around the
map and work really flexibly here for instance we're just jumping day after
day all of this imagery is generated immediately within a hundred
milliseconds working from the raw unstructured data, so rather than taking
hours to pre-process and hours to say how am I going to solve this question
with GPUs you're able to actually just ask a question and then have an answer I
think that's really essential for visualization sometimes you also have
more quantitative questions you want to say
how has this changed over time and simply by selecting the region of
interest you immediately have a time series that spans as much as in this
case about six years or four years excuse me you can zoom in and you can
immediately see that in the later part of 2015 in this specific region of
interest you have a dramatic increase in the sea surface temperature anomaly
again all of this is generated just from the raw sea surface temperature data so
we've generated the anomaly data we are able to zoom in and out you're able to
select regions ask questions say ok how actually how dramatic is this El Nino
effect does this occur and you get those answers very quickly and so I think that
this technologies are really a remarkable way of improving the
scientific process
finally because GPUs are most widely used for machine
learning which has become a huge field these days with new breakthroughs being
done everyday in computer vision, anomaly detection, language, video all of these
algorithms are implemented on the GPU and they can be computed extremely
quickly on the GPU and so because the GPU is actually used as part of the
visualization pipeline new algorithms can just be dropped in and out and
applied as part of the process here for instance you have on the leftmost column
this MODIS truecolor data which is optical data taken of the whole world
almost every day, but there are parts of the world where there actually is no
data because a satellite never passed directly over that region and the
remarkable thing is that looking only at the leftmost column you're able to
predict the kinds of values that would occur had you had ax had you had a
satellite over that region and you're able to fill it in as part of the
visualization process so if you want to see just a beautiful view of the entire
world simply tile by tile in less than 100 milliseconds you're able to apply
these state-of-the-art machine learning algorithms fill in these details and
view this the whole world with this inferred data and you can also have more
scientifically relevant applications where you infer missing values in data
you find anomalies, you say if you have access to data
from depths in the ocean you can fill in those gaps by using machine learning and
I think that having this unified pipeline that combines visualization
analysis and machine learning is I think a really essential and powerful
combination of these technologies that makes visualization very useful
finally and very quickly this can all be done in the cloud so you don't actually have to
have a GPU yourself you can simply go to a website and view all of this data
dynamically and in terms of speed and performance this can be just
dramatically faster than anything that exists currently for generating
individual tiles it can be as much as a hundred times faster analytics can be as
much as a thousand times faster using this GPU acceleration so having this
whole pipeline is flexible, it's powerful and I think it allows you to answer
questions that are either very difficult to do or to make observations that you
might never have thought to make because you're able to interact seamlessly
fluidly and answer questions almost as soon as you have them so I want to thank
JPL for having me the summer where I did a lot of this work and then of course
NASA and AGU for this sponsoring this competition and for this award and this
wonderful conference so thank you very much