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Register a new userMCBooster: a library for fast Monte Carlo generation of phase-space decays on massively parallel platforms
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MCBooster is a header-only, C++11-compliant library that provides routines to generate and perform calculations on large samples of phase space Monte Carlo events. To achieve superior performance, MCBooster is capable to perform most of its calculations in parallel using CUDA- and OpenMP-enabled devices. MCBooster is built on top of the Thrust library and runs on Linux systems. This contribution summarizes the main features of MCBooster. A basic description of the user interface and some examples of applications are provided, along with measurements of performance in a variety of environments
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We present a highly scalable Monte Carlo (MC) three-dimensional photon transport simulation platform designed for heterogeneous computing systems. Through the development of a massively parallel MC algorithm using the Open Computing Language (OpenCL) framework, this research extends our existing graphics processing unit (GPU)-accelerated MC technique to a highly scalable vendor-independent heterogeneous computing environment, achieving significantly improved performance and software portability. A number of parallel computing techniques are investigated to achieve portable performance over a wide range of computing hardware. Furthermore, multiple thread-level and device-level load-balancing strat- egies are developed to obtain efficient simulations using multiple central processing units (CPUs) and GPUs.
Hydra is a header-only, templated and C++11-compliant framework designed to perform the typical bottleneck calculations found in common HEP data analyses on massively parallel platforms. The framework is implemented on top of the C++11 Standard Library and a variadic version of the Thrust library and is designed to run on Linux systems, using OpenMP, CUDA and TBB enabled devices. This contribution summarizes the main features of Hydra. A basic description of the overall design, functionality and user interface is provided, along with some code examples and measurements of performance.
The GooFit Framework is designed to perform maximum-likelihood fits for arbitrary functions on various parallel back ends, for example a GPU. We present an extension to GooFit which adds the functionality to perform time-dependent amplitude analyses of pseudoscalar mesons decaying into four pseudoscalar final states. Benchmarks of this functionality show a significant performance increase when utilizing a GPU compared to a CPU. Furthermore, this extension is employed to study the sensitivity on the $D^0 - bar{D}^0$ mixing parameters $x$ and $y$ in a time-dependent amplitude analysis of the decay $D^0 rightarrow K^+pi^-pi^+pi^-$. Studying a sample of 50 000 events and setting the central values to the world average of $x = (0.49 pm0.15) %$ and $y = (0.61 pm0.08) %$, the statistical sensitivities of $x$ and $y$ are determined to be $sigma(x) = 0.019 %$ and $sigma(y) = 0.019 %$.
An implementation of the Monte Carlo (MC) phase space generators coupled with adaptive MC integration/simulation program FOAM is presented. The first program is a modification of the classic phase space generator GENBOD interfaced with the adaptive sampling integrator/generator FOAM. On top of this tool the algorithm suitable for generation of the phase space for an reaction with two leading particles is presented (double-peripheral process with central production of particles). At the same time it serves as an instructive example of construction of a self-adaptive phase space generator/integrator with a modular structure for specialized particle physics calculations.
Monte Carlo generation of high energy particle collisions is a critical tool for both theoretical and experimental particle physics, connecting perturbative calculations to phenomenological models, and theory predictions to full detector simulation. The generation of minimum bias events can be particularly computationally expensive, where non-perturbative effects play an important role and specific processes and fiducial regions can no longer be well defined. In particular scenarios, particle guns can be used to quickly sample kinematics for single particles produced in minimum bias events. CIMBA (Cubic Interpolation for Minimum Bias Approximation) provides a comprehensive package to smoothly sample predefined kinematic grids, from any general purpose Monte Carlo generator, for all particles produced in minimum bias events. These grids are provided for a number of beam configurations including those of the Large Hadron Collider.
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