CUDA Spotlight: Debbie Bard
This week's Spotlight is on Dr. Debbie Bard, a cosmologist at the Kavli Institute for Particle Astrophysics and Cosmology (KIPAC).
KIPAC members work in the Physics and Applied Physics Departments at Stanford University and at the SLAC National Accelerator Laboratory.
To handle the massive amounts of data involved in cosmological measurements, Debbie and her colleagues Matt Bellis (now an assistant professor at Siena College) and Mark Allen (now a data scientist at Chegg) teamed up to explore the potential of GPU computing and CUDA.
They concluded that "GPUs are a useful tool for cosmological calculations, allowing calculations to be made one or two orders of magnitude faster." Their results were presented in a paper titled Cosmological Calculations on the GPU, which appeared earlier this year in Astronomy and Computing.
This interview is part of the CUDA Spotlight Series.
Q & A with Debbie Bard
NVIDIA: Debbie, tell us a bit about yourself.
NVIDIA: What are you primarily focused on now?
NVIDIA: What are some of the challenges?
Approximation functions such as tree codes can be very useful, but they inevitably introduce uncertainties and potential systematic errors which ultimately limits the accuracy of our measurements. We will have such large volumes of data (and therefore such great statistical accuracy) that even small sources of bias and uncertainty will make a real difference to our results. The challenge is therefore to find a way to calculate these statistics to full precision in a reasonable time frame, which is where GPUS come in handy.
LSST will produce so much data that astronomers simply will not be able to do science in the way they have done before. The algorithms and data analysis techniques that have worked so far will simply not scale to a dataset of tens of billions of astronomical objects, each observed hundreds of times over the ten year LSST survey.
We need to start now to develop data analysis pipelines and algorithms that will work with LSST data, so that we're ready for when the telescope starts taking data. A large part of that involves running simulations of the sky, and of the telescope, so that we have appropriate data to test our algorithms with.
Rendering of the LSST, an 8.4-meter ground-based telescope that will survey the entire visible sky every week from a mountaintop in Chile.Courtesy of LSST Corp./NOAO
NVIDIA: What role does GPU computing play in your work?
However, to make this comparison we need to make simulated datasets based on the theory, and compare the statistics of these simulations to our data. So we need to calculate the relevant statistics over many simulations, as well as on data. Without GPUs, I would not be able to do this within a reasonable time frame. I've been able to use GPUs to calculate my statistics in a couple of minutes, whereas it would take hours on the CPU.
We write a lot of histogramming in shared memory, so we use a lot of atomic addition. With large volumes of data, we take advantage of streaming functionality on the GPU by chunking our data into subsets, and streaming the data transfer and calculation of these chunks in parallel.
NVIDIA: Describe your hardware/software system.
We are currently adding a Tesla K40 to the system. During the holidays, I'm looking forward to designing a new algorithm that will take advantage of the kernels-launching-kernels capability (dynamic parallelism).
NVIDIA: What's the "next big thing" in cosmology research?
In terms of data, the next big thing is the Dark Energy Survey (DES), which has just gotten started. The measurements DES will make of galaxies will allow us to start to understand the statistics that describe the structure of the universe. LSST will start taking data in about eight years, and that will give us another order of magnitude more data. In the next decade we're really going to start to constrain our theories of what the universe is made of, and how it's evolved. Provided, of course, that we can process the enormous quantity of complex data these surveys will provide.
NVIDIA: How did you become interested in cosmology?
Bio for Debbie Bard
I grew up in the UK, and from a young age I was fascinated by science and physics. I studied physics at university, and did my PhD and four years as a post-doc working in particle physics. I've been working on cosmology at SLAC for three and a half years now, where my interests include understanding Dark Energy using gravitational lensing, and developing data analysis algorithms for LSST.
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