Aloe Vera Stroblowski

Graphics Processing Units (GPU) are commonly used to power video game software. However, they are also finding use for a more diverse array of scientific research as well. A GPU conference has recently taken place discussing some of the applications of this technology (see PDF 8.1 MB). Here are a few excerpts about the GPU brain projects. One deals with the connectome (circuit diagram).

Determining the detailed connections in brain circuits is a fundamental unsolved problem in neuroscience. Understanding this circuitry will enable brain scientists to confirm or refute existing models, develop new ones, and come closer to an understanding of how the brain works. Prof. Jeff Lichtman and Center for Brain Science (CBS) at Harvard launched the Connectome Project three years ago to determine the complete, detailed wiring diagrams of neural circuits from sequential high-resolution images of the central nervous system using electron microscopy (EM). These high-resolution, large-scale EM datasets pose very challenging computational problems for 3D segmentation and visualization in terms of developing suitable algorithms, coping with the ever-increasing data sizes, and maintaining interactive performance.

Visual recognition software is another area that this tech could speed up performance.

Nicolas Pinto is a second-year PhD Student in Computational Neuroscience at MIT. He is currently a member of the DiCarlo Lab and the Sinha Lab at MIT, and the Visual Neuroscience Group at Harvard. His research interests lie at the intersection of Brain and Computer Sciences. The overarching goal of his research is to dramatically accelerate the development of computational theories of how the visual cortex accomplishes object recognition. In addition to advancing our understanding of how the brain works by generating new experimentally testable hypotheses, this approach also holds great promise for the development of new artificial vision systems. A key innovation in his work is the ability to leverage the computational power of disruptive technologies like NVIDIA’s GPUs to provide new insights into this fundamental problem.

A Harvard researcher has recently talked about how these new methods will enable us to answer many of the big questions. From the Big Bang (and even before then) to the evolution of humans, computing power will truly help us understand almost any question imaginable. Better supercomputers may lead to complete and detailed simulations of living tissue. Researchers are developing multi-scale modeling from bio-molecules to organs (see PDF). With the help of these virtual models we will essentially be able to reprogram our own brain and body matter. Are we headed toward ageless bodies and superhappy minds? Only time will tell what new avenues this kind of processing power will open up.

See also GPU-Based Petascale Visual Computing for Analysis of Neural Circuitry (PDF).

Labels: GPU

I mentioned previously about a GPU conference that discussed the implications of the technology for doing scientific research. There are certain classes of problems that GPU's are especially suited for and they offer a speed up when compared to CPU's. As an example, researchers have recently developed a relatively inexpensive 13 gpu "supercomputer" with about 12 teraflops of computing power for scientific problems. GPU's have been rivaling the complexity of intel's most advanced technology and 3 billion transistor gpu chips will probably hit the market shortly. Nvidia also believes that they can reach 10 billion transistors easily. With this speed up of processing power coupled with machine learning, we will be able to learn more about the brain than ever before. While it's probably somewhat facile to make a blanket statement that computing power is increasing exponentially, there are still some interesting exponential trends in the field that will likely continue for at least the next 5 or ten years.

Nvidia has put media from that 2009 conference online and several of them are related to neuroscience. The company Evolved Machines is "pioneering the reverse engineering of brain circuitry to build intelligent machines". An audio talk can be found here (6.1 MB).

"Reconstructing the Brain: Extracting Neural Circuitry with CUDA and MPI" is a 37.6 MB video presentation (download here). The following is an excerpt about that video;

In this talk we will present our insights and lessons learned in using CUDA to reconstruct neural connections in high-resolution EM data. We will present technical details and non-trivial issues regarding the implementation of NeuroTrace, our system for semi-automatic segmentation and interactive visualization of terabytes of EM image data. The segmentation method is based on a sequence of 2D level set segmentations of cell membranes integrated with an image correspondence energy for robust transition between consecutive slices and a weighted path extrapolation method to trace a 3D centerline of a neural pathway along non-axis aligned slices.

Optimizing Ion Channel Kinetics Using A Massively Parallel Genetic Algorithm on the GPU (26.4 MB video presentation);

Voltage-gated ion channels effect the integration of information in many neurons. Some neurons express over 10 voltage-gated channels that turn information processing into a highly non-linear affair.

The currently popular analysis techniques suffer from various shortcomings that limit the ability of the researcher to rapidly produce physiologically relevant models of voltage-gated ion channels.

To solve this computational bottleneck we have been converting our optimization algorithm to work on a GPU using CUDA. We have succeeded to parallelize the process on a GTX 295 giving a speed increase of roughly X100 over that of the CPU.

Medical Image Registration with CUDA (37.6 MB video presentation);

Speedups of up to 750 times were obtained as compared to code in daily use at Addenbrookes Hospital and Bio-Medical Campus. Some very recent results are shown in the figures. This work is of direct application in both research and clinical practice. A particular application is voxel based MRI morphometry in humans and in animal brains.

High-Throughput Science (keynote speech);

How did the universe start? How is the brain wired? How does matter interact at the quantum level? These are some of the great scientific challenges of our times, and answering them requires bigger scientific instruments, increasingly precise imaging equipment and ever-more complex computer simulations.

The rest of the presentations can be found here. They cover a wide variety of topics.

Labels: GPU