scholarly journals A hybrid mesh and voxel based Monte Carlo algorithm for accurate and efficient photon transport modeling in complex bio-tissues

2020 ◽  
Author(s):  
Shijie Yan ◽  
Qianqian Fang
Keyword(s):  
Monte Carlo ◽  
Graphics Processing Units ◽  
Light Transport ◽  
Monte Carlo Algorithm ◽  
Brain Atlas ◽  
Simulation Accuracy ◽  
Mc Simulation ◽  
Voxel Data ◽  
Graphics Processing ◽  
Improved Accuracy

AbstractOver the past decade, an increasing body of evidence has suggested that threedimensional (3-D) Monte Carlo (MC) light transport simulations are affected by the inherent limitations and errors of voxel-based domain boundaries. In this work, we specifically address this challenge using a hybrid MC algorithm, namely split-voxel MC or SVMC, that combines both mesh and voxel domain information to greatly improve MC simulation accuracy while remaining highly flexible and efficient in parallel hardware, such as graphics processing units (GPU). We achieve this by applying a marching-cubes algorithm to a pre-segmented domain to extract and encode sub-voxel information of curved surfaces, which is then used to inform ray-tracing computation within boundary voxels. This preservation of curved boundaries in a voxel data structure demonstrates significantly improved accuracy in several benchmarks, including a human brain atlas. The accuracy of the SVMC algorithm is comparable to that of mesh-based MC (MMC), but runs 2x-6x faster and requires only a lightweight preprocessing step. The proposed algorithm has been implemented in our open-source software and is freely available at http://mcx.space.

scholarly journals MadFlow: towards the automation of Monte Carlo simulation on GPU for particle physics processes

2021 ◽  
Vol 251 ◽  
pp. 03022
Author(s):  
Stefano Carrazza ◽  
Juan Cruz-Martinez ◽  
Marco Rossi ◽  
Marco Zaro
Keyword(s):  
Monte Carlo ◽  
Particle Physics ◽  
Graphics Processing Units ◽  
Hardware Acceleration ◽  
Matrix Elements ◽  
Analytic Expressions ◽  
Mc Simulation ◽  
Graphics Processing ◽  
Full Simulation ◽  
New Framework

In this proceedings we present MadFlow, a new framework for the automation of Monte Carlo (MC) simulation on graphics processing units (GPU) for particle physics processes. In order to automate MC simulation for a generic number of processes, we design a program which provides to the user the possibility to simulate custom processes through the Mad-Graph5_aMC@NLO framework. The pipeline includes a first stage where the analytic expressions for matrix elements and phase space are generated and exported in a GPU-like format. The simulation is then performed using the VegasFlow and PDFFlow libraries which deploy automatically the full simulation on systems with different hardware acceleration capabilities, such as multi-threading CPU, single-GPU and multi-GPU setups. We show some preliminary results for leading-order simulations on different hardware configurations.

scholarly journals Quasi-Regression Monte-Carlo Method for Semi-Linear PDEs and BSDEs

Proceedings ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 44 ◽  
Author(s):  
Emmanuel Gobet ◽  
José Germán López Salas ◽  
Carlos Vázquez
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Differential Equations ◽  
Discrete Time ◽  
Graphics Processing Units ◽  
Monte Carlo Algorithm ◽  
High Dimensions ◽  
Many Core ◽  
Graphics Processing ◽  
Linear Pdes

In this work we design a novel and efficient quasi-regression Monte Carlo algorithm in order to approximate the solution of discrete time backward stochastic differential equations (BSDEs), and we analyze the convergence of the proposed method. With the challenge of tackling problems in high dimensions we propose suitable projections of the solution and efficient parallelizations of the algorithm taking advantage of powerful many core processors such as graphics processing units (GPUs).

Development of a hardware-accelerated simulation kernel for ultra-high vacuum with Nvidia RTX GPUs

2021 ◽  
pp. 109434202110566
Author(s):  
Pascal R Bähr ◽  
Bruno Lang ◽  
Peer Ueberholz ◽  
Marton Ady ◽  
Roberto Kersevan
Keyword(s):  
Ray Tracing ◽  
Graphics Processing Units ◽  
High Vacuum ◽  
Simulation Software ◽  
Ultra High Vacuum ◽  
Particle Accelerators ◽  
Accelerated Simulation ◽  
Mc Simulation ◽  
Graphics Processing ◽  
Simulation Unit

Molflow+ is a Monte Carlo (MC) simulation software for ultra-high vacuum, mainly used to simulate pressure in particle accelerators. In this article, we present and discuss the design choices arising in a new implementation of its ray-tracing–based simulation unit for Nvidia RTX Graphics Processing Units (GPUs). The GPU simulation kernel was designed with Nvidia’s OptiX 7 API to make use of modern hardware-accelerated ray-tracing units, found in recent RTX series GPUs based on the Turing and Ampere architectures. Even with the challenges posed by switching to 32 bit computations, our kernel runs much faster than on comparable CPUs at the expense of a marginal drop in calculation precision.

scholarly journals XVA PRINCIPLES, NESTED MONTE CARLO STRATEGIES, AND GPU OPTIMIZATIONS

2018 ◽  
Vol 21 (06) ◽  
pp. 1850030 ◽  
Author(s):  
LOKMAN A. ABBAS-TURKI ◽  
STÉPHANE CRÉPEY ◽  
BABACAR DIALLO
Keyword(s):  
Monte Carlo ◽  
Interest Rate ◽  
Outer Layer ◽  
Graphics Processing Units ◽  
Lower Layer ◽  
Credit Derivatives ◽  
Square Root ◽  
Root Number ◽  
Graphics Processing ◽  
Gpu Implementation

We present a nested Monte Carlo (NMC) approach implemented on graphics processing units (GPUs) to X-valuation adjustments (XVAs), where X ranges over C for credit, F for funding, M for margin, and K for capital. The overall XVA suite involves five compound layers of dependence. Higher layers are launched first, and trigger nested simulations on-the-fly whenever required in order to compute an item from a lower layer. If the user is only interested in some of the XVA components, then only the sub-tree corresponding to the most outer XVA needs be processed computationally. Inner layers only need a square root number of simulation with respect to the most outer layer. Some of the layers exhibit a smaller variance. As a result, with GPUs at least, error-controlled NMC XVA computations are doable. But, although NMC is naively suited to parallelization, a GPU implementation of NMC XVA computations requires various optimizations. This is illustrated on XVA computations involving equities, interest rate, and credit derivatives, for both bilateral and central clearing XVA metrics.

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