CUDA Spotlight: Bob Zigon

Bob Zigon

GPU-Accelerated Life Sciences

This week's Spotlight is on Bob Zigon of Beckman Coulter. Beckman Coulter develops, manufactures and markets products that simplify and automate complex biomedical testing.

As a member of the research group, Bob is responsible for investigating technologies that affect the flow cytometry, particle characterization, analytical ultracentrifugation and automation business units.

This interview is part of the CUDA Spotlight Series.

Q & A with Bob Zigon

NVIDIA: Bob, what is your role at Beckman Coulter?
Bob: For the last year I’ve been working on a prototype of a new CUDA-based application that will calculate the molar mass, gross shape and size distribution of protein samples by way of analytical ultracentrifugation (AUC). The application is currently 120 times faster than existing software.

For the six years prior to that, my team and I implemented three versions Kaluza, a CUDA-based application used in flow cytometry. Our goal was to build an application that would allow cancer researchers to interact with 800 megabyte leukemic data sets 200 times faster than existing software. By the time we shipped our third version, we were executing 400 times faster on a Fermi card than our competitors.

CUDA-based application

NVIDIA: Describe the users of your technology.
Bob: In AUC, molecular biologists use our technology to characterize biological macromolecules and viral particles. Pharmaceutical companies use AUC in conjunction with nanoparticles to target the delivery of therapeutic drugs to diseased cells.

Our flow cytometry customers, on the other hand, are divided into two groups: Research and Clinical. Our research customers are typically universities, biotech and pharmaceutical companies interested in studying leukemia and lymphoma. Our clinical customers are mainly hospitals.

NVIDIA: Why is it important to accelerate data analysis for life science instruments?
Bob: Our flow cytometry instruments can characterize the proteins on the surface of one million white blood cells in less than five minutes.

The subsequent analysis of the data can take days or weeks. During this time, a researcher will move the mouse (with existing software) and wait as long as seven minutes for the user interface to update because of the computations required. During that period of time, people can become distracted. They essentially forget the question they were trying to answer.

This is unacceptable.

Objectivity and an unbiased approach are critical to good science. In the last seven years, I have seen researchers avoid asking certain questions because they knew it would take too long to get an answer.

This is where I start to blame the software. The software’s performance is so slow that it causes researchers to behave differently. The software essentially introduces a bias into a process that is supposed to be bias free. My goal is to change that.

NVIDIA: What is it about GPUs that make them attractive for life science data analysis?
Bob: Wow, that’s easy. When the flow cytometry software is properly written, a result can be returned ten times a second. The user no longer needs to wait seven minutes for a result. When results take only 100 milliseconds, researchers can ask more “What if…" questions. The researchers need to move effortlessly through data sets. Blindingly fast software empowers them to ask and explore the harder questions involving cancer in an unbiased way.

NVIDIA: What role does CUDA play in your work?
Bob: CUDA and Tesla are disruptive technologies. When they are applied to our problems we are capable of returning answers to clinicians and researchers in a fraction of a second. This causes people to change the way they interact with the data. I’ve seen this behavioral change repeatedly over the last three years. Instead of looking at the data from 100,000 white blood cells, researchers can now manipulate five million cells.

For example, most of this data is viewed on a logarithmic scale. The problem with the log operator is that it can’t be applied to negative numbers. Our industry has known about an alternate scale known as the biexponential transform for years. It produces superior results but is roughly 10 times more expensive to compute.

I have had leukemia scientists call me on the telephone laughing and cheering because they are now manipulating five million cells with the biexponential transform in real time, thanks to GPU computing! We’ve had episodes like this dozens of times now. It is really satisfying to know you have gone far beyond the bounds of software development to build a product that exceeds the customers’ expectations.

NVIDIA: What's your favorite feature in CUDA? What would you like to see in the future?
Bob: First and foremost, CUDA is easy to use. It gives you the ability to reason about difficult problems using scalar-like code. That being said, there is still a lot of computer science you need to learn to extract every bit of performance from the hardware, but tenfold improvements are fairly easy to achieve.

My favorite feature these days is the Version 3 pre-release of Nsight Visual Studio Edition. The NVIDIA tools group listened to what the CUDA developers were saying they needed. I regularly use the Version 3 pre-release on a 30” monitor. You can generate a lot of critical insight into the behavior of your app when you have a gazillion pixels to view.

As far as new features are concerned, I would like to see Tesla cards with 32GB or 64GB of RAM.
The 64-bit CUDA 5.0 environment is stable and begs on a daily basis to manipulate monster data structures.

NVIDIA: How did you become interested in the fields of AUC and flow cytometry?
Bob: Well, seven years ago Beckman Coulter was outsourcing the development of its flow cytometry software to a company in England. One of the vice presidents decided that Beckman Coulter needed to own that domain knowledge. I had just finished the development of an informatics-related project with good success. The VP asked me if I wanted to create the new group in Indianapolis and lead the development. I thought about it for a day and then accepted the position.

AUC was a little different. After being in flow cytometry for six years, I was looking for something different to do. I was offered a position in research so I could affect more product lines. Since I had used CUDA to change the way science is performed in flow cytometry, I wanted to make a similar sort of impact in a different domain.

When I discovered the enormous computational challenges associated with AUC, I started digging deeper to understand how it is used by our customers. I discovered AUC applications that were running on supercomputers at the University of Texas for up to three days at a time. Now we are working on duplicating the functionality of the supercomputer using half a dozen Tesla K20s in order to return the results in several seconds.

Bio for Bob Zigon

Bob is a graduate of Purdue University with degrees in Computer Science and Mathematics. He has worked at Beckman Coulter for 11 years and is currently a senior member of the Life Science Research Group. His interests include high performance computing, numerical analysis and information retrieval theory.

Relevant Links
Beckman Coulter:
Flow Cytometry:
Analytical Ultracentrifugation:

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