1,425 research outputs found
FiCoS: A fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks.
Mathematical models of biochemical networks can largely facilitate the comprehension of the mechanisms at the basis of cellular processes, as well as the formulation of hypotheses that can be tested by means of targeted laboratory experiments. However, two issues might hamper the achievement of fruitful outcomes. On the one hand, detailed mechanistic models can involve hundreds or thousands of molecular species and their intermediate complexes, as well as hundreds or thousands of chemical reactions, a situation generally occurring in rule-based modeling. On the other hand, the computational analysis of a model typically requires the execution of a large number of simulations for its calibration, or to test the effect of perturbations. As a consequence, the computational capabilities of modern Central Processing Units can be easily overtaken, possibly making the modeling of biochemical networks a worthless or ineffective effort. To the aim of overcoming the limitations of the current state-of-the-art simulation approaches, we present in this paper FiCoS, a novel "black-box" deterministic simulator that effectively realizes both a fine-grained and a coarse-grained parallelization on Graphics Processing Units. In particular, FiCoS exploits two different integration methods, namely, the Dormand-Prince and the Radau IIA, to efficiently solve both non-stiff and stiff systems of coupled Ordinary Differential Equations. We tested the performance of FiCoS against different deterministic simulators, by considering models of increasing size and by running analyses with increasing computational demands. FiCoS was able to dramatically speedup the computations up to 855×, showing to be a promising solution for the simulation and analysis of large-scale models of complex biological processes
Status and Future Perspectives for Lattice Gauge Theory Calculations to the Exascale and Beyond
In this and a set of companion whitepapers, the USQCD Collaboration lays out
a program of science and computing for lattice gauge theory. These whitepapers
describe how calculation using lattice QCD (and other gauge theories) can aid
the interpretation of ongoing and upcoming experiments in particle and nuclear
physics, as well as inspire new ones.Comment: 44 pages. 1 of USQCD whitepapers
Plasma propulsion simulation using particles
This perspective paper deals with an overview of particle-in-cell / Monte
Carlo collision models applied to different plasma-propulsion configurations
and scenarios, from electrostatic (E x B and pulsed arc) devices to
electromagnetic (RF inductive, helicon, electron cyclotron resonance)
thrusters, with an emphasis on plasma plumes and their interaction with the
satellite. The most important elements related to the modeling of plasma-wall
interaction are also presented. Finally, the paper reports new progress in the
particle-in-cell computational methodology, in particular regarding
accelerating computational techniques for multi-dimensional simulations and
plasma chemistry Monte Carlo modules for molecular and alternative propellan
DeepFlame: A deep learning empowered open-source platform for reacting flow simulations
In this work, we introduce DeepFlame, an open-source C++ platform with the
capabilities of utilising machine learning algorithms and pre-trained models to
solve for reactive flows. We combine the individual strengths of the
computational fluid dynamics library OpenFOAM, machine learning framework
Torch, and chemical kinetics program Cantera. The complexity of cross-library
function and data interfacing (the core of DeepFlame) is minimised to achieve a
simple and clear workflow for code maintenance, extension and upgrading. As a
demonstration, we apply our recent work on deep learning for predicting
chemical kinetics (Zhang et al. Combust. Flame vol. 245 pp. 112319, 2022) to
highlight the potential of machine learning in accelerating reacting flow
simulation. A thorough code validation is conducted via a broad range of
canonical cases to assess its accuracy and efficiency. The results demonstrate
that the convection-diffusion-reaction algorithms implemented in DeepFlame are
robust and accurate for both steady-state and transient processes. In addition,
a number of methods aiming to further improve the computational efficiency,
e.g. dynamic load balancing and adaptive mesh refinement, are explored. Their
performances are also evaluated and reported. With the deep learning method
implemented in this work, a speed-up of two orders of magnitude is achieved in
a simple hydrogen ignition case when performed on a medium-end graphics
processing unit (GPU). Further gain in computational efficiency is expected for
hydrocarbon and other complex fuels. A similar level of acceleration is
obtained on an AI-specific chip - deep computing unit (DCU), highlighting the
potential of DeepFlame in leveraging the next-generation computing architecture
and hardware
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