Yulin Shi1, Alexander V Veidenbaum2, Alex Nicolau2, Xiangmin Xu3. 1. Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, CA 92697-1275, United States. 2. Department of Computer Science, University of California, Irvine, CA 92697-3435, United States. 3. Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, CA 92697-1275, United States; Department of Biomedical Engineering, University of California, Irvine, CA 92697-2715, United States; Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92697-2625, United States. Electronic address: xiangmin.xu@uci.edu.
Abstract
BACKGROUND: Modern neuroscience research demands computing power. Neural circuit mapping studies such as those using laser scanning photostimulation (LSPS) produce large amounts of data and require intensive computation for post hoc processing and analysis. NEW METHOD: Here we report on the design and implementation of a cost-effective desktop computer system for accelerated experimental data processing with recent GPU computing technology. A new version of Matlab software with GPU enabled functions is used to develop programs that run on Nvidia GPUs to harness their parallel computing power. RESULTS: We evaluated both the central processing unit (CPU) and GPU-enabled computational performance of our system in benchmark testing and practical applications. The experimental results show that the GPU-CPU co-processing of simulated data and actual LSPS experimental data clearly outperformed the multi-core CPU with up to a 22× speedup, depending on computational tasks. Further, we present a comparison of numerical accuracy between GPU and CPU computation to verify the precision of GPU computation. In addition, we show how GPUs can be effectively adapted to improve the performance of commercial image processing software such as Adobe Photoshop. COMPARISON WITH EXISTING METHOD(S): To our best knowledge, this is the first demonstration of GPU application in neural circuit mapping and electrophysiology-based data processing. CONCLUSIONS: Together, GPU enabled computation enhances our ability to process large-scale data sets derived from neural circuit mapping studies, allowing for increased processing speeds while retaining data precision.
BACKGROUND: Modern neuroscience research demands computing power. Neural circuit mapping studies such as those using laser scanning photostimulation (LSPS) produce large amounts of data and require intensive computation for post hoc processing and analysis. NEW METHOD: Here we report on the design and implementation of a cost-effective desktop computer system for accelerated experimental data processing with recent GPU computing technology. A new version of Matlab software with GPU enabled functions is used to develop programs that run on Nvidia GPUs to harness their parallel computing power. RESULTS: We evaluated both the central processing unit (CPU) and GPU-enabled computational performance of our system in benchmark testing and practical applications. The experimental results show that the GPU-CPU co-processing of simulated data and actual LSPS experimental data clearly outperformed the multi-core CPU with up to a 22× speedup, depending on computational tasks. Further, we present a comparison of numerical accuracy between GPU and CPU computation to verify the precision of GPU computation. In addition, we show how GPUs can be effectively adapted to improve the performance of commercial image processing software such as Adobe Photoshop. COMPARISON WITH EXISTING METHOD(S): To our best knowledge, this is the first demonstration of GPU application in neural circuit mapping and electrophysiology-based data processing. CONCLUSIONS: Together, GPU enabled computation enhances our ability to process large-scale data sets derived from neural circuit mapping studies, allowing for increased processing speeds while retaining data precision.
Authors: Gordon M G Shepherd; Armen Stepanyants; Ingrid Bureau; Dmitri Chklovskii; Karel Svoboda Journal: Nat Neurosci Date: 2005-05-08 Impact factor: 24.884
Authors: James P Clement; Massimiliano Aceti; Thomas K Creson; Emin D Ozkan; Yulin Shi; Nicholas J Reish; Antoine G Almonte; Brooke H Miller; Brian J Wiltgen; Courtney A Miller; Xiangmin Xu; Gavin Rumbaugh Journal: Cell Date: 2012-11-09 Impact factor: 41.582
Authors: Felix Franke; David Jäckel; Jelena Dragas; Jan Müller; Milos Radivojevic; Douglas Bakkum; Andreas Hierlemann Journal: Front Neural Circuits Date: 2012-12-20 Impact factor: 3.492