Graphics processing unit-accelerated Monte Carlo simulation of polarized light in complex three-dimensional media

Significance: Monte Carlo (MC) methods have been applied for studying interactions between polarized light and biological tissues, but most existing MC codes supporting polarization modeling can only simulate homogeneous or multi-layered domains, resulting in approximations when handling realistic tissue structures. Aim: Over the past decade, the speed of MC simulations has seen dramatic improvement with massively-parallel computing techniques. Developing hardware-accelerated MC simulation algorithms that can accurately model polarized light inside 3-D heterogeneous tissues can greatly expand the utility of polarization in biophotonics applications. Approach: Here we report a highly efficient polarized MC algorithm capable of modeling arbitrarily complex media defined over a voxelated domain. Each voxel of the domain can be associated with spherical scatters of various radii and densities. The Stokes vector of each simulated photon packet is updated through photon propagation, creating spatially resolved polarization measurements over the detectors or domain surface. Results: We have implemented this algorithm in our widely disseminated MC simulator, Monte Carlo eXtreme (MCX). It is validated by comparing with a reference CPU-based simulator in both homogeneous and layered domains, showing excellent agreement and a 931-fold speedup. Conclusion: The polarization-enabled MCX (pMCX) offers biophotonics community an efficient tool to explore polarized light in bio-tissues, and is freely available at http://mcx.space/.

Circular Interval-valued Computers and Simulation of (Red-green) Turing Machines

Interval-valued computing is a kind of massively parallel computing. It operates on specific subsets of the interval [0,1) – unions of subintervals. They serve as basic data units and are called interval-values. It was established in [9], by a rather simple observation, that interval-valued computing, as a digital computing model, has computing power equivalent to Turing machines. However, this equivalence involves an unlimited number of interval-valued variables. In [14], the equivalence with Turing machines is established using a simulation that uses only a fixed number of interval-valued variables and this number depends only on the number of states of the Turing machine – in a logarithmic way. The simulation given there allows us to extend interval-valued computations into infinite length to capture the computing power of red-green Turing machines. In this extension of [14], based on the quasi-periodic techniques used in the simulations in that paper, a reformulation of the interval-valued computations is given, named circular interval-valued computers. This reformulation enforces the finiteness of the number of used interval-valued variables by building the finiteness into the syntax rules.

Impact of HPC and Automated CFD Simulation Processes on Virtual Product Development—A Case Study

High-performance computing (HPC) enables both academia and industry to accelerate simulation-driven product development processes by providing a massively parallel computing infrastructure. In particular, the automation of high-fidelity computational fluid dynamics (CFD) analyses aided by HPC systems can be beneficial since computing time decreases while the number of significant design iterations increases. However, no studies have quantified these effects from a product development point of view yet. This article evaluates the impact of HPC and automation on product development by studying a formula student racing team as a representative example of a small or medium-sized company. Over several seasons, we accompanied the team, and provided HPC infrastructure and methods to automate their CFD simulation processes. By comparing the team’s key performance indicators (KPIs) before and after the HPC implementation, we were able to quantify a significant increase in development efficiency in both qualitative and quantitative aspects. The major aerodynamic KPI increased up to 115%. Simultaneously, the number of expedient design iterations within one season increased by 600% while utilizing HPC. These results prove the substantial benefits of HPC and automation of numerical-intensive simulation processes for product development.

HARDWARE IMPLEMENTATION DESIGN OF A SPIKING NEURON

The hardware implementation of an artificial neuron is the key problem of the design of neuromorphic chips which are new promising architectural solutions for massively parallel computing. In this paper an analog neuron circuit design is presented to be used as a building element of spiking neuron networks. The design of the neuron is performed at the transistor level based on Leaky Integrate-and-Fire neuron implementation model. The neuron is simulated using EDA tool to verify the design. Signal waveforms at key nodes of the neuron are obtained and neuron functionality is demonstrated.

Data-driven prediction and origin identification of epidemics in population networks

Effective intervention strategies for epidemics rely on the identification of their origin and on the robustness of the predictions made by network disease models. We introduce a Bayesian uncertainty quantification framework to infer model parameters for a disease spreading on a network of communities from limited, noisy observations; the state-of-the-art computational framework compensates for the model complexity by exploiting massively parallel computing architectures. Using noisy, synthetic data, we show the potential of the approach to perform robust model fitting and additionally demonstrate that we can effectively identify the disease origin via Bayesian model selection. As disease-related data are increasingly available, the proposed framework has broad practical relevance for the prediction and management of epidemics.

A Comparison of Unsteady RANS and DES for Simulating an Axial Compressor Stage

Computational fluid dynamics (CFD) of industrial-scale, axial compressor geometries has traditionally been performed using steady state methods such as the mixing plane approach. With the surge in the development of large-scale, massively-parallel computing platforms, fully 3D unsteady approaches are rapidly growing in popularity. The fully 3D, unsteady approach involves building a full 3D domain for each blade row, and then coupling the stationary and rotating domains using a sliding interface. In the literature, there are various methods for solving this 3D unsteady problem, such as the Unsteady Reynolds Averaged Navier-Stokes (URANS) and the Detached Eddy Simulation (DES) methods. While these methods are well documented for a variety of real-world problems, there have been limited efforts to compare the effectiveness of these methods for fully 3D, unsteady turbomachinery problems. In this study, the first stage of an industrial-scale axial compressor was simulated using: i) the URANS approach, and ii) the DES approach. The compressor geometry consisted of an inlet housing, inlet guide vanes (IGV), a rotor, and a stator. The RANS model for both simulations was the k-epsilon model. For both of these cases, sliding mesh interfaces were located between the IGV and rotor, and between the rotor and stator. The results of the URANS and DES approaches were time-averaged and their predictions were compared. Throughout the study, our goal was to provide important insights into the performance of the URANS and DES approaches, and to highlight the essential differences.

Towards real-time DNA biometrics using GPU-accelerated processing

Decoding the human genome in the past decades has brought into focus a computationally intensive operation through DNA profiling. The typical search space for these kinds of problems is extremely large and requires specialized hardware and algorithms to perform the necessary sequence analysis. In this paper, we propose an innovative and scalable approach to exact multi-pattern matching of nucleotide sequences by harnessing the massively parallel computing power found in commodity graphical processing units. Our approach places careful consideration on preprocessing of DNA datasets and runtime performance, while exploiting the full capabilities of the heterogeneous platform it runs on. Finally, we evaluate our models against real-world DNA sequences.

Review of smoothed particle hydrodynamics: towards converged Lagrangian flow modelling

This paper presents a review of the progress of smoothed particle hydrodynamics (SPH) towards high-order converged simulations. As a mesh-free Lagrangian method suitable for complex flows with interfaces and multiple phases, SPH has developed considerably in the past decade. While original applications were in astrophysics, early engineering applications showed the versatility and robustness of the method without emphasis on accuracy and convergence. The early method was of weakly compressible form resulting in noisy pressures due to spurious pressure waves. This was effectively removed in the incompressible (divergence-free) form which followed; since then the weakly compressible form has been advanced, reducing pressure noise. Now numerical convergence studies are standard. While the method is computationally demanding on conventional processors, it is well suited to parallel processing on massively parallel computing and graphics processing units. Applications are diverse and encompass wave–structure interaction, geophysical flows due to landslides, nuclear sludge flows, welding, gearbox flows and many others. In the state of the art, convergence is typically between the first- and second-order theoretical limits. Recent advances are improving convergence to fourth order (and higher) and these will also be outlined. This can be necessary to resolve multi-scale aspects of turbulent flow.