After querying a dataset of 114,875,956,837 particles for those with energy values less than 1.5, FastQuery identifies 57,740,614 particles, which are mapped on this plot. Image by Oliver Rubel, Berkeley Lab.
research tools like supercomputers, particle colliders, and telescopes
are generating so much data, so quickly, many scientists fear that soon
they will not be able to keep up with the deluge.
instruments are capable of answering some of our most fundamental
scientific questions, but it is all for nothing if we can’t get a handle
on the data and make sense of it,” says Surendra Byna of the Lawrence
Berkeley National Laboratory’s (Berkeley Lab’s) Scientific Data
why Byna and several of his colleagues from the Berkeley Lab’s
Computational Research Division teamed up with researchers from the
University of California, San Diego (UCSD); Los Alamos National
Laboratory; Tsinghua University; and Brown University to develop novel
software strategies for storing, mining, and analyzing massive
datasets—more specifically, for data generated by a state-of-the-art
plasma physics code called VPIC.
the team ran VPIC on the Department of Energy’s National Energy
Research Scientific Computing Center’s (NERSC’s) Cray XE6 “Hopper”
supercomputer, they generated a 3D magnetic
reconnection data set of a trillion particles. VPIC simulated the process
in thousands of time-steps, periodically writing a massive 32 TB file
to disk at specified times.
their tools, the researchers wrote each 32 TB file to disk in about 20
minutes, at a sustained rate of 27 GB/s. By applying an enhanced version
of the FastQuery tool, the team indexed this massive dataset in about
10 minutes, then queried the dataset in three seconds for interesting
features to visualize.
is the first time anyone has ever queried and visualized 3D particle
datasets of this size,” says Homa Karimabadi, who leads the space
physics group at UCSD.
The problem with trillion particle datasets
reconnection is a process where the magnetic topology in a plasma (a
gas made up of charged particles) is rearranged, leading to an explosive
release of energy in form of plasma jets, heated plasma, and energetic
particles. Reconnection is the mechanism behind the aurora borealis
(a.k.a. northern lights) and solar flares, as well as fractures in
Earth’s protective magnetic field—fractures that allow energetic solar
particles to seep into our planet’s magnetosphere and wreak havoc in
electronics, power grids, and space satellites.
to Karimabadi, one of the major unsolved mysteries in magnetic
reconnection is the conditions and details of how energetic particles
are generated. But until recently, the closest that any researcher has
come to studying this is by looking at 2D simulations. Although these
datasets are much more manageable, containing at most only billions of
particles, Karimabadi notes that lingering magnetic reconnection
questions cannot be answered with 2D particle simulations alone. In
fact, these datasets leave out a lot of critical information.
answer these questions we need to take into full account additional
effects such as flux rope interactions and resulting turbulence that
occur in 3D simulations,” says Karimabadi. “But as we add another
dimension, the number of particles in our simulations grows from
billions to trillions. And it is impossible to pull up a
trillion-particle dataset on your computer screen; it would just fill up
your screen with black dots.”
Hopper Supercomputer at NERSC. Photo by Roy Kaltschmidt, Berkeley Lab.
address the challenges of analyzing 3D particle data, Karimabadi and a
team of astrophysicists joined forces with the ExaHDF5 team, a
Department of Energy funded collaboration to develop high performance
I/O and analysis strategies for future exascale computers. Prabhat, of
Berkeley Lab’s Visualization Group, leads the ExaHDF5 team.
A scalable storage approach sets foundation for a successful search
to Byna, VPIC accurately models the complexities of magnetic
reconnection at this scale by breaking down the “big picture” into
distinct pieces, each of which are assigned, using Message Passing
Interface (MPI), to a group of processors to compute. These groups, or
MPI domains, work independently to solve their piece of the problem. By
subdividing the work, researchers can simultaneously employ hundreds of
thousands of processors to simulate a massive and complex phenomenon
like magnetic reconnection.
the original implementation of VPIC, each MPI domain generates a binary
file once it finishes processing its assigned piece of the problem.
This ensures that the data is written efficiently.
But according to Byna, this approach, called file-per-process,
has a number of limitations. One major limitation is that the number of
files generated for large-scale scientific simulations, like magnetic
reconnection, can become unwieldy. In fact, his team’s largest VPIC run
on Hopper contained about 20,000 MPI domains—that’s 20,000 binary files
per time-step. And because most analysis tools cannot easily read
binary files, another post-processing step would have been required to
re-factor the data into a format that these tools can open.
takes a really long time to perform a simple Linux search of a
20,000-file directory; and since the data is not stored in standard data
formats, such as HDF5, existing data management and visualization tools
cannot directly work with the binary file,” says Byna. “Ultimately,
these limitations become a bottleneck to scientific analysis and
by incorporating H5Part code into the VPIC codebase, Byna and his
colleagues managed to overcome all of these challenges. H5Part is an
easy-to-use veneer layer on top of HDF5, that allows for the management
and analysis of extremely large particle and block-structured data sets.
to Prabhat, this easy modification to the code-base creates one shared
HDF5 file per time-step, instead of 20,000 independent binary files.
Because most visualization and analysis tools can use HDF5 files, this
approach eliminates the need to re-format the data. With the latest
performance enhancements implemented by the ExaHDF5 team, VPIC was able
to write each 32 TB time-step to disk at a sustained rate of 27 GB/s.
is quite an achievement when you consider that the theoretical peak I/O
for the machine is about 35 GB/s,” says Prabhat. “Very few production
I/O frameworks and scientific applications can achieve that level of
Mining a trillion particle dataset with FastQuery
this torrent of information has been generated and stored, the next
challenge that researchers face is how to make sense of it. On this
front, ExaHDF5 team members Jerry Chou and John Wu implemented an
enhanced version of FastQuery,
an indexing and querying tool. Using this technique, they indexed the
trillion-particle, 32 TB dataset in about 10 minutes, and queried the
massive dataset for particles of interest in approximately three
seconds. This was the first time anybody has successfully queried a
trillion-particle dataset this quickly.
team was able to accelerate FastQuery’s indexing and query capabilities
by implementing a hierarchical load-balancing strategy that involves a
hybrid of MPI and Pthreads. At the MPI level, FastQuery breaks up the
large dataset into multiple fixed-size sub-arrays. Each sub-array is
then assigned to a set of compute nodes, or MPI domains, which is where
the indexing and querying occurs.
This is a visualization of the 1 trillion-electron dataset at timestep 1905. All of the particles with energy > 1.3 are shown in grey, while particles with energy > 1.5 are shown in color. A total of 164,856,597 particles with energy > 1.3 and 423,998 particles with energy > 1.5 appear to be accelerated preferentially along the direction of the mean magnetic field corresponding to formation of four jets. Image by Oliver Rubel, Berkeley Lab.
load-balancing flexibility happens within these MPI domains, where the
work is dynamically pooled among threads—which are the smallest unit of
processing that can be scheduled by an operating system. When
constructing the indexes, the threads build bitmaps on the sub-arrays
and store them into the same HDF5 file. When evaluating a query, the
processors apply the query to each sub-array and return results.
FastQuery is built on the FastBit bitmap indexing technology, Byna
notes that researchers can search their data based on an arbitrary range
of conditions that is defined by available data values. This
essentially means that a researcher can now feasibly search a trillion
particle dataset and sift out electrons by their energy values.
to Prabhat, this unique querying capability also serves as the basis
for successfully visualizing the data. Because typical computer
displays contain on the order of a few million pixels, it is simply
impossible to render a dataset with trillions of particles. So to
analyze their data, researchers must reduce the number of particles in
their dataset before rendering. The scientists can now achieve this by
using the FastQuery tool to identify the particles of interest to
our VPIC runs typically generate two types of data—grid and particle—we
never did a whole lot with the particle data because it was really hard
to extract information from a trillion particle dataset, and there was
no way to sift out the useful information,” says Karimabadi.
with the new query-based visualization techniques, Karimabadi and his
team were finally able to verify the localization behavior of energetic
particles, gain insights into the relationship between the structure of
the magnetic field and energetic particles, and investigate the
agyrotropic distribution of particles near the reconnection hot-spot in a
3D trillion particle dataset.
have hypothesized about these phenomena in the past, but it was only
the development and application of these new analysis tools that enabled
us to unlock the scientific discoveries and insights,” says Karimabadi. “With these new tools, we can now go back to our archive of particle
datasets and look at the physics questions that we couldn’t get at
of today’s simulation codes generate datasets on the order of tens of
millions to a few billon particles, so a trillion-particle dataset—that
is, a million-million particles—poses unprecedented data management
challenges,” says Prabhat. “In this work, we have demonstrated that the
HDF5 I/O middleware and the FastBit indexing technology can handle
these truly massive datasets and operate at scale on current petascale
according to Prabhat, exascale platforms will produce even larger
datasets in the near future, and researchers need to come up with novel
techniques and usable software that can facilitate scientific discovery
going forward. He notes that one of the primary goals of the ExaHDF5
team is to scale the widely used HDF5 I/O middleware to operate on
modern petascale and future exascale platforms.
addition to Byna, Chou, Karimabadi, Prabhat and Wu, other members of
this collaboration include Oliver Rubel, William Daughton, Vadim
Roytershteyn, Wes Bethel, Mark Howison, Ke-Jou Hsu, Kuan-Wu Lin, Arie
Shoshani and Andrew Uselton. The team also acknowledges critical support
provided by NERSC consultants, NERSC system staff, and Cray engineers
in assisting with their large-scale runs.