Efficient smart monte carlo based SSTA on graphics processing units with improved resource utilization

2010 ◽  
Author(s):  
Vineeth Veetil ◽  
Yung-Hsu Chang ◽  
Dennis Sylvester ◽  
David Blaauw
Keyword(s):  
Monte Carlo ◽  
Resource Utilization ◽  
Graphics Processing Units ◽  
Graphics Processing

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.

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 MadFlow: automating Monte Carlo simulation on GPU for particle physics processes

2021 ◽  
Vol 81 (7) ◽  
Author(s):  
Stefano Carrazza ◽  
Juan Cruz-Martinez ◽  
Marco Rossi ◽  
Marco Zaro
Keyword(s):  
Monte Carlo ◽  
Particle Physics ◽  
Graphics Processing Units ◽  
Hardware Acceleration ◽  
Leading Order ◽  
Event Simulation ◽  
Multiple Processes ◽  
Analytic Expressions ◽  
Simulation Results ◽  
Graphics Processing

AbstractWe present , a first general multi-purpose framework for Monte Carlo (MC) event simulation of particle physics processes designed to take full advantage of hardware accelerators, in particular, graphics processing units (GPUs). The automation process of generating all the required components for MC simulation of a generic physics process and its deployment on hardware accelerator is still a big challenge nowadays. In order to solve this challenge, we design a workflow and code library which provides to the user the possibility to simulate custom processes through the MadGraph5_aMC@NLO framework and a plugin for the generation and exporting of specialized code in a GPU-like format. The exported code includes analytic expressions for matrix elements and phase space. The simulation is 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. The package also provides an asynchronous unweighted events procedure to store simulation results. Crucially, although only Leading Order is automatized, the library provides all ingredients necessary to build full complex Monte Carlo simulators in a modern, extensible and maintainable way. We show simulation results at leading-order for multiple processes on different hardware configurations.

scholarly journals GPU-accelerated mesh-based Monte Carlo photon transport simulations

2019 ◽  
Author(s):  
Qianqian Fang ◽  
Shijie Yan
Keyword(s):  
Monte Carlo ◽  
Graphics Processing Units ◽  
Heterogeneous Computing ◽  
Wide Field ◽  
Source Codes ◽  
Wide Range ◽  
Complex Source ◽  
Speedup Ratio ◽  
Almost All ◽  
Graphics Processing

AbstractThe mesh-based Monte Carlo (MMC) algorithm is increasingly used as the gold-standard for developing new biophotonics modeling techniques in 3-D complex tissues, including both diffusion-based and various Monte Carlo (MC) based methods. Compared to multi-layered and voxel-based MCs, MMC can utilize tetrahedral meshes to gain improved anatomical accuracy, but also results in higher computational and memory demands. Previous attempts of accelerating MMC using graphics processing units (GPUs) have yielded limited performance improvement and are not publicly available. Here we report a highly efficient MMC – MMCL – using the OpenCL heterogeneous computing framework, and demonstrate a speedup ratio up to 420× compared to state-of-the-art single-threaded CPU simulations. The MMCL simulator supports almost all advanced features found in our widely disseminated MMC software, such as support for a dozen of complex source forms, wide-field detectors, boundary reflection, photon replay and storing a rich set of detected photon information. Furthermore, this tool supports a wide range of GPUs/CPUs across vendors and is freely available with full source codes and benchmark suites at http://mcx.space/#mmc.

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