The paper discusses a fast implementation of the conjugate gradient iterative method with E-field multilevel preconditioner applied to solving real symmetric and sparse systems obtained with vector finite element method. In order to accelerate computations, a graphics processing unit (GPU) was used and significant speed-up (2.61 fold) was achieved comparing to a central processing unit (CPU) based approach. These results indicate that performance of electromagnetic simulations can be significantly improved thereby enabling full wave optimization of microwave components in more manageable time.
(A. Dziekonski, A. Lamecki and M. Mrozowski: “GPU Acceleration of Multilevel Solvers for Analysis of Microwave Components With Finite Element Method”, IEEE Microwave and Wireless Components Letters 21(1) pp.1-3, Jan. 2011. [DOI])
Proteins, nucleic acids, and small molecules form a dense network of molecular interactions in a cell. The architecture of molecular networks can reveal important principles of cellular organization and function, similarly to the way that protein structure tells us about the function and organization of a protein. Protein complexes are groups of proteins that interact with each other at the same time and place, forming a single multimolecular machine. Functional modules, in contrast, consist of proteins that participate in a particular cellular process while binding each other at a different time and place.
A protein-protein interaction network is represented as proteins are nodes and interactions between proteins are edges. Protein complexes and functional modules can be identified as highly interconnected subgraphs and computational methods are now inevitable to detect them from protein interaction data. In addition, High-throughput screening techniques such as yeast two-hybrid screening enable identification of detailed protein-protein interactions map in multiple species. As the interaction dataset increases, the scale of interconnected protein networks increases exponentially so that the increasing complexity of network gives computational challenges to analyze the networks. Read the rest of this entry »
GMAC is a user-level library that implements an Asymmetric Distributed Shared Memory model to be used by CUDA programs. An ADSM model builds a global memory space that allows CPU code to transparently access data hosted in accelerators’ (GPUs’) memories. Moreover, the coherency of the data is automatically handled by the library. This removes the necessity for manual memory transfers (cudaMemcpy) between the host and GPU memories. Furthermore, GMAC assigns a different “virtual GPU” to each host thread, and the virtual GPUs are evenly mapped to physical GPUs. This is especially useful for multi-GPU programs since each host thread can access the memory of all GPUs and simple GPU-to-GPU transfers can be performed with simple memcpy calls. Read the rest of this entry »
We examine the problem of segmenting foreground objects in live video when background scene textures change over time. In particular, we formulate background subtraction as minimizing a penalized instantaneous risk functional yielding a local on-line discriminative algorithm that can quickly adapt to temporal changes. We analyze the algorithms convergence, discuss its robustness to non-stationarity, and provide an efficient non-linear extension via sparse kernels. To accommodate interactions among neighboring pixels, a global algorithm is then derived that explicitly distinguishes objects versus background using maximum a posteriori inference in a Markov random field (implemented via graph-cuts). By exploiting the parallel nature of the proposed algorithms, we develop an implementation that can run efficiently on the highly parallel Graphics Processing Unit (GPU). Empirical studies on a wide variety of datasets demonstrate that the proposed approach achieves quality that is comparable to state-of-the-art off-line methods, while still being suitable for real-time video analysis (75 fps on a mid-range GPU).
(Li Cheng, M. Gong, D. Schuurmans, and T. Caelli: “Real-time Discriminative Background Subtraction”. IEEE Transactions on Image Processing, 2011, to appear. [DOI] [Sources & Info])
The Parallel Processing for Imaging Applications conference, part of IS&T/SPIE’s Electronic Imaging conference, was held on January 24–25 in San Francisco. The conference had a large number of GPU papers (SPIE digital library link):
We implemented a GPU based parallel code to perform Monte Carlo simulations of the two dimensional q-state Potts model. The algorithm is based on a checkerboard update scheme and assigns independent random number generators to each thread (one thread per spin). The implementation allows to simulate systems up to ~10^9 spins with an average time per spin flip of 0.147ns on the fastest GPU card tested, representing a speedup up to 155x, compared with an optimized serial code running on a standard CPU. The possibility of performing high speed simulations at large enough system sizes allowed us to provide a positive numerical evidence about the existence of metastability on very large systems based on Binder’s criterion, namely, on the existence or not of specific heat singularities at spinodal temperatures different of the transition one.
(Ezequiel E. Ferrero, Juan Pablo De Francesco, Nicolás Wolovick and Sergio A. Cannas: “q-state Potts model metastability study using optimized GPU-based Monte Carlo algorithms”. [arXiv:1101.0876] [code and additional information])
Although trivial background subtraction (BGS) algorithms (e.g. frame differencing, running average…) can perform quite fast, they are not robust enough to be used in various computer vision problems. Some complex algorithms usually give better results, but are too slow to be applied to real-time systems. We propose an improved version of the Extended Gaussian mixture model that utilizes the computational power of Graphics Processing Units (GPUs) to achieve real-time performance. Experiments show that our implementation running on a low-end GeForce 9600GT GPU provides at least 10x speedup. The frame rate is greater than 50 frames per second (fps) for most of the tests, even on HD video formats.
(Vu Pham, Phong Vo, Vu Thanh Hung and Le Hoai Bac: “GPU Implementation of Extended Gaussian Mixture Model for Background Subtraction”. IEEE International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2010. [DOI] [code and additional information])
We present a fast GPU-based streaming algorithm to perform collision queries between deformable models. Our approach is based on hierarchical culling and reduces the computation to generating different streams. We present a novel stream registration method to compact the streams and efficiently compute the potentially colliding pairs of primitives. We also use a deferred front tracking method to lower the memory overhead. The overall algorithm has been implemented on different GPUs and we have evaluated its performance on non-rigid and deformable simulations. We highlight our speedups over prior GPU-based and CPU-based algorithms. In practice, our algorithm can perform inter-object and intra-object computations on models composed of hundreds of thousands of triangles in tens of milliseconds.
(Min Tang, Dinesh Manocha, Jiang Lin, Ruofeng Tong, Collision-Streams: “Fast GPU-based Collision Detection for Deformable Models”, in Proceedings of ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (i3D 2011), San Fransisco, CA, Feb. 18-20, 2011. http://gamma.cs.unc.edu/CSTREAMS)
The CPU has traditionally been the computational work horse in scientific computing, but we have seen a tremendous increase in the use of accelerators, such as Graphics Processing Units (GPUs), in the last decade. These architectures are used because they consume less power and offer higher performance than equivalent CPU solutions. They are typically also far less expensive, as more CPUs, and even clusters, are required to match their performance. Even though these accelerators are powerful in terms of floating point operations per second, they are considerably more primitive in terms of capabilities. For example, they cannot even open a file on disk without the use of the CPU. Thus, most applications can benefit from using accelerators to perform heavy computation, whilst running complex tasks on the CPU. This use of different compute resources is often referred to as heterogeneous computing, and we explore the use of heterogeneous architectures for scientific computing in this thesis. Through six papers, we present qualitative and quantitative comparisons of different heterogeneous architectures, the use of GPUs to accelerate linear algebra operations in MATLAB, and efficient shallow water simulation on GPUs. Our results show that the use of heterogeneous architectures can give large performance gains.
(André R. Brodtkorb, “Scientific Computing on Heterogeneous Architectures”, Ph.D. thesis, University of Oslo, Faculty of Mathematics and Natural Sciences, 2010, (PDF))
The “GPUs in Databases” workshop is devoted to sharing the knowledge related to applying GPUs in database environments and to discuss possible future development of this application domain. The workshop topics include, but are not limited to:
- GPU based data compression (lossless/lossy compression and decompression, real time compression and decompression of multimedia)
- GPUs in databases and data warehouses (join processing, data indexing, data aggregation, bulk query processing, analytical query processing)
- Data mining using GPUs (classification, frequent itemsets and association rules, frequent subgraphs, sequential patterns, clustering, social networks mining, regression)
- GPUs in streaming databases (query processing in streaming databases, stream compression/decompression)
- Applications of GPUs in bioinformatics
The workshop will take place on September 19th, 2011 and is co-located with ADBIS 2011 in Vienna, Austria. Submissions are due April 5th, 2011. All of accepted submissions will be published in CEUR workshop proceedings and the best papers will also be published in Lecture Notes in Computer Science and Foundations of Computing and Decision Sciences.
More detailed information can be found at the workshop website http://gid2011.cs.put.poznan.pl.