GPU accelerated MCMC for modeling terrorist activity

In radiation transport calculations, the effects of material temperature on neutron/nucleus interactions must be taken into account through Doppler broadening adjustments to the microscopic cross section data. Historically, Monte Carlo transport simulations have accounted for this temperature dependence by interpolating among precalculated Doppler broadened cross sections at a variety of temperatures. More recently, there has been much interest in on-the-fly Doppler broadening methods, where reference data is broadened on-demand during particle transport to any temperature. Unfortunately, Doppler broadening operations are expensive on traditional central processing unit (CPU) architectures, making on-the-fly Doppler broadening unaffordable without approximations or complex data preprocessing. This work considers the use of graphics processing unit (GPU)s, which excel at parallel data processing, for on-the-fly Doppler broadening in continuous-energy Monte Carlo simulations. Two methods are considered for the broadening operations – a GPU implementation of the standard SIGMA1 algorithm and a novel vectorized algorithm that leverages the convolution properties of the broadening operation in an attempt to expose additional parallelism. Numerical results demonstrate that similar cross section lookup throughput is obtained for on-the-fly broadening on a GPU as cross section lookup throughput with precomputed data on a CPU, implying that offloading Doppler broadening operations to a GPU may enable on-the-fly temperature treatment of cross sections without a noticeable reduction in cross section processing performance in Monte Carlo transport codes.

Airport air traffic is one of the most important and hardest one among all the airport data forecasts. In this paper, the Markov Chain Monte Carlo (MCMC) method of applied theory of statistics has been introduced into the aviation sector, and the discussion on airport air traffic forecast has been conducted taking Shanghai airport as the application background. MCMC method is designed to solve the problem parameters expectations where some baysian inference can not be directly calculated. This paper conducts the iterative computation using WinBUGS, EViews is used to estimate the initial iteration parameter of the regression model, and it is on this basis that the regression equation under the MCMC method is obtained.

The use of graphics processing units (GPUs) in simulation applications offers a significant speed gain as compared to computations on central processing units (CPUs). Many simulation methods require a large number of independent random variables generated at each step. We present two approaches for implementation of random number generators (RNGs) on a GPU. In the one-RNG-per-thread approach, one RNG produces a stream of random numbers in each thread of execution, whereas the one-RNG-for-all-threads method builds on the ability of different threads to communicate, thus, sharing random seeds across an entire GPU device. We used these approaches to implement Ran2, Hybrid Taus, and Lagged Fibonacci algorithms on a GPU. We profiled the performance of these generators in terms of the computational time, memory usage, and the speedup factor (CPU time/GPU time). These generators have been incorporated into the program for Langevin simulations of biomolecules fully implemented on the GPU. The ∼250-fold computational speedup on the GPU allowed us to carry out single-molecule dynamic force measurements in silico to explore the mechanical properties of the bacteriophage HK97 in the experimental subsecond time scale. We found that the nanomechanical response of HK97 depends on the conditions of force application, including the rate of change and geometry of the mechanical perturbation. Hence, using the GPU-based implementation of RNGs, presented here, in conjunction with Langevin simulations, makes it possible to directly compare the results of dynamic force measurements in vitro and in silico.

Graphical Processing Units (GPU) have become a vital source for data processing in many advanced or intelligent technologies. They have proven to be faster and more efficient than the Central Processing Unit (CPU) in computing large data sets. The automotive industry has adopted GPUs for Advanced Driver Assistance Systems (ADAS), but other systems like electric vehicles and autonomous vehicles will also employ GPUs soon. Although GPUs are faster at computing large data sets, a substantial increase in core temperature is challenging during the process. This review paper primarily focuses on active and passive cooling methods for GPUs in the automotive industry. The paper commences by addressing the role and need for GPUs in the automotive industry and explores the challenges they face and the factors that lead to an increase in temperature. The paper further reviews various cooling methods that original equipment manufacturers (OEM) use in the automotive industry. The outcomes of the review are a detailed understanding of the use of GPUs in automotive, a brief interpretation of heating issues in GPUs and what causes them, a comprehensive understanding of the cooling methods used to reduce the core temperature, and a perception of an anticipated technology to resolve the heating problem.

Angular radial transform (ART) is a shape descriptor in MPEG-7 for image representation and image analysis. For real world applications, execution efficiency is always a significant challenge. With widespread use of Graphics Processing Unit (GPU) in image processing tasks, this paper presents GPU based angular radial transform (GART). Proposed parallel GART is based on mathematical properties of ART and optimization techniques of GPU. Optimal parameter selections for GPU execution are also evaluated. In our experiments, with same computation result GART is over 1800 times faster than ART. Wide range of applications that using ART will inspired from this work.

Data in a data center are stored dispersively. The data-oriented task computing disperses big data analysis tasks to different computing nodes. The extensive use of graphics processing unit (GPU) makes it urgent and important to study how to reasonably assign heterogeneous resources to different computing frameworks. We investigate the existing big data computing framework and the GPU computing. Based on the existing cluster resource management model and the GPU management model, we propose a hybrid heterogeneous resource management model that combines CPU resources with GPU resources. The computing nodes manage local resources and implement tasks; the resource management center concertedly manage various computing frameworks. We design and implement a hybrid domain resource sharing and allocation algorithm, which allocates the hybrid domain resources to computing frameworks according to the coordinated use of them so as to fairly share the hybrid domain resources among various computing frameworks and prevent the CPU from too many tasks but the GPU or CPU from resource "hunger". The experimental results show that the allocation algorithm can increase the use of heterogeneous resources and the number of completed tasks by around 15%.

We present a case study on the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods. Graphics cards, containing multiple Graphics Processing Units (GPUs), are self-contained parallel computational devices that can be housed in conventional desktop and laptop computers and can be thought of as prototypes of the next generation of many-core processors. For certain classes of population-based Monte Carlo algorithms they offer massively parallel simulation, with the added advantage over conventional distributed multicore processors that they are cheap, easily accessible, easy to maintain, easy to code, dedicated local devices with low power consumption. On a canonical set of stochastic simulation examples including population-based Markov chain Monte Carlo methods and Sequential Monte Carlo methods, we find speedups from 35- to 500-fold over conventional single-threaded computer code. Our findings suggest that GPUs have the potential to facilitate the growth of statistical modeling into complex data-rich domains through the availability of cheap and accessible many-core computation. We believe the speedup we observe should motivate wider use of parallelizable simulation methods and greater methodological attention to their design. This article has supplementary material online.

Network Function Virtualization (NFV) replaces the hardware that supports packet processing in network operation from specific- by general-purpose ones, reducing costs and bringing more flexibility and agility to the network operation. However, this shift can cause performance losses due to the non-optimal packet processing capabilities of the general-purpose hardware. Moreover, supporting the line rate of optical network channels with Virtualized Network Functions (VNFs) is a challenging task. This work analyzes the benefits of using Graphics Processing Units (GPUs) to support the execution of a Deep Packet Inspection (DPI) VNF towards supporting the line rate of an optical channel. The use of GPUs in VNFs has a great potential to increase throughput, but the delay incurred might be an issue for some functions. Our simulation was performed using an Intrusion Detection Systems (IDS) which performs DPI deployed as a VNF under real-world traffic scaled to high bit rates. Results show that the packet processing speedup achieved by using GPUs can reach up to 19 times, at the expense of a higher packet delay.

Polar Fourier analysis can extract orthogonal and rotation invariant features and demonstrate superior performances in many image processing tasks. For real world applications, execution efficiency is always a significant challenge. With widespread use of Graphics Processing Unit (GPU), this research presents GPU accelerated polar Fourier analysis. Proposed parallel algorithm is based on mathematical properties of polar Fourier analysis and optimization techniques of GPU. Optimal parameter selections for GPU execution are also evaluated. In our experiments, with same computation result proposed method is over 170 times faster. Wide range of applications that using polar Fourier analysis will inspired from this work.

Current data centers leverage virtual machines (VMs) in order to efficiently use hardware resources. VMs allow reducing equipment acquisition costs as well as decreasing overall energy consumption. However, although VMs have noticeably evolved to make a smart use of the underlying hardware, the use of GPUs (Graphics Processing Units) for General Purpose computing (GPGPU) is still not efficiently supported. This concern might be addressed by remote GPU virtualization solutions, which may provide VMs with GPUs located in a remote node, detached from the host where the VMs are being executed. This chapter presents an in-depth analysis about how to provide GPU access to applications running inside VMs. This analysis is complemented with experimental results which show that the use of remote GPU virtualization is an effective mechanism to provide GPU access to applications with negligible overheads. Finally, the approach is presented in the context of cloud federations for providing GPGPU as a Service.

In addition to the intended use of graphics processing units (GPU) to accelerate computer games, their potential has become apparent for scientific computations in the recent years. Molecular modeling and molecular design are only few examples of numerous research areas that are significantly benefiting from novel developments of hardware and software platforms. For example, the impact of high computational power of GPUs has been demonstrated in molecular dynamics (MD) simulations or quantum chemical (QC) calculations. Thus far, several MD programs have been adapted to GPU computing, including NAMD/VMD, GROMACS, AMBER, etc. In addition, modeling tools intended for molecular design based on receptor-ligand interactions, such as molecular docking or core hopping protocols have recently been updated for GPU environment. The tremendous increase in the computing power facilitated by the integration of GPUs and the availability of GPU-based systems could accelerate material research on nanoscale. The price/performance ratio of GPU-based systems supports the development of custom-made protocols for efficient modeling of biomolecular systems and nanostructures. GPU-related molecular modeling tools will also accelerate the combined quantum chemical/molecular mechanics (QC/MM) methodologies. An overview of the performance of NVIDIA Tesla GPU-based system built for high-performance and high-throughput computing aimed for biomolecular modeling and nanodesign is presented.

There is great interest in the use of graphics processing units (GPU)for general purpose applications because the highly parallel architectures used in GPUs offer the potential for huge performance increases. The use of GPUs in image analysis applications has been under investigation for a number of years. This article describes modifications to the InsightToolkit (ITK) that provide a simple architecture for transparent use of GPU enabled filters and examples of how to write GPU enabled filters using the NVIDIA CUDA tools.This work was performed between late 2009 and early 2010 and is being published as modifications to ITK 3.20. It is hoped that publication will help inform development of more general GPU support in ITK 4.0 and facilitate experimentation by users requiring functionality of 3.20 or wishing to pursue CUDA based developments.

The conventional Markov chain Monte Carlo (MCMC) method is limited to the selected shape and size of proposal distribution and is not easy to start when the initial proposal distribution is far away from the target distribution. To overcome these drawbacks of the conventional MCMC method, two useful improvements in MCMC method, adaptive Metropolis (AM) algorithm and delayed rejection (DR) algorithm, are attempted to be combined. The AM algorithm aims at adapting the proposal distribution by using the generated estimators, and the DR algorithm aims at enhancing the efficiency of the improved MCMC method. Based on the improved MCMC method, a Bayesian amplitude versus offset (AVO) inversion method on the basis of the exact Zoeppritz equation has been developed, with which the P- and S-wave velocities and the density can be obtained directly, and the uncertainty of AVO inversion results has been estimated as well. The study based on the logging data and the seismic data demonstrates the feasibility and robustness of the method and shows that all three parameters are well retrieved. So the exact Zoeppritz-based nonlinear inversion method by using the improved MCMC is not only suitable for reservoirs with strong-contrast interfaces and long-offset ranges but also it is more stable, accurate and anti-noise.

As alluded to in the chapter “Linkage Analysis of Qualitative Traits”, neither the Elston–Steward algorithm nor the Lander–Green approach is amenable to genetic data from large complex pedigrees and a large number of markers. In such cases, Monte Carlo estimation methods provide a viable alternative to the exact solutions. Two types of Monte Carlo methods have been developed for linkage analysis, haplotype inference, and other kinds of genetic analysis. They are Markov chain Monte Carlo (MCMC) methods and Monte Carlo methods that are based on independent samples. Approaches based on Markov chain Monte Carlo methods are more widely applicable; there is practically no limit on the size or complexity of the pedigrees, nor on the number of markers to be considered simultaneously. In this chapter, we will review the basic principles of MCMC methods for multipoint linkage analysis with extended pedigrees. Both simulations and application to data from the Framingham study will be used to compare and contrast three MCMC software packages: LOKI, MORGAN, and SIMWALK.

Monte Carlo simulations (MCS) represent a fundamental approach to modelling the photon interactions in positron emission tomography (PET). A variety of PET-dedicated MCS tools are available to assist and improve PET imaging applications. Of these, GATE has evolved into one of the most popular software for PET MCS because of its accuracy and flexibility. However, simulations are extremely time-consuming. The use of graphics processing units (GPU) has been proposed as a solution to this, with reported acceleration factors about 400–800. These factors refer to GATE benchmarks performed on a single CPU core. Consequently, CPU-based MCS can also be easily accelerated by one order of magnitude or beyond when exploiting multi-threading on powerful CPUs. Thus, CPU-based implementations become competitive when further optimisations can be achieved. In this context, we have developed a novel, CPU-based software called the PET physics simulator (PPS), which combines several efficient methods to significantly boost the performance. PPS flexibly applies GEANT4 cross-sections as a pre-calculated database, thus obtaining results equivalent to GATE. This is demonstrated for an elaborated PET scanner with 3-layer block detectors. All code optimisations yield an acceleration factor of ≈20 (single core). Multi-threading on a high-end CPU workstation (96 cores) further accelerates the PPS by a factor of 80. This results in a total speed-up factor of ≈1600, which outperforms comparable GPU-based MCS by a factor of ≳2. Optionally, the proposed method of coincidence multiplexing can further enhance the throughput by an additional factor of ≈15. The combination of all optimisations corresponds to an acceleration factor of ≈24 000. In this way, the PPS can simulate complex PET detector systems with an effective throughput of 106 photon pairs in less than 10 milliseconds.