We present FlowPM, a Particle-Mesh (PM) cosmological N-body code implemented in Mesh-TensorFlow for GPU-accelerated, distributed, and differentiable simulations. We implement and validate the accuracy of a novel multi-grid scheme based on multiresolution pyramids to compute large-scale forces efficiently on distributed platforms. We explore the scaling of the simulation on large-scale supercomputers and compare it with corresponding Python based PM code, finding on an average 10x speed-up in terms of wallclock time. We also demonstrate how this novel tool can be used for efficiently solving large scale cosmological inference problems, in particular reconstruction of cosmological fields in a forward model Bayesian framework with hybrid PM an...
During the last decades, Multigrid methods have been extensively used for solving large sparse linea...
We describe a hybrid technique for carrying out large N-Body simulations to study formation and evol...
International audienceRapid advances in deep learning have brought not only myriad powerful neural n...
We present FlowPM, a Particle-Mesh (PM) cosmological N-body code implemented in Mesh-TensorFlow for ...
We provide a novel and efficient algorithm for computing accelerations in theperiodic large-N-body p...
We describe a parallel, cosmological N-body code based on a hybrid scheme using the particle-mesh (P...
International audienceWe present a new scheme to compensate for the small-scales approximations resu...
Although poor for small dynamic scales, the Particle-Mesh (PM) model allows in astrophysics good ins...
We present a parallel implementation of the particle-particle/particle-mesh (P3M) algorithm for dist...
We discuss the cosmological simulation code GADGET-2, a new massively parallel TreeSPH code, capable...
An improved implementation of an N-body code for simulating collisionless cosmological dynamics is p...
The formation of the large-scale structure, the evolution and distribution of galaxies, quasars, and...
We report on improvements made over the past two decades to our adaptive treecode N-body method (HOT...
We report on improvements made over the past two decades to our adaptive treecode N-body method (HOT...
We present Particle-Particle-Particle-Mesh (PPPM) and Tree Particle-Mesh (TreePM) implementations on...
During the last decades, Multigrid methods have been extensively used for solving large sparse linea...
We describe a hybrid technique for carrying out large N-Body simulations to study formation and evol...
International audienceRapid advances in deep learning have brought not only myriad powerful neural n...
We present FlowPM, a Particle-Mesh (PM) cosmological N-body code implemented in Mesh-TensorFlow for ...
We provide a novel and efficient algorithm for computing accelerations in theperiodic large-N-body p...
We describe a parallel, cosmological N-body code based on a hybrid scheme using the particle-mesh (P...
International audienceWe present a new scheme to compensate for the small-scales approximations resu...
Although poor for small dynamic scales, the Particle-Mesh (PM) model allows in astrophysics good ins...
We present a parallel implementation of the particle-particle/particle-mesh (P3M) algorithm for dist...
We discuss the cosmological simulation code GADGET-2, a new massively parallel TreeSPH code, capable...
An improved implementation of an N-body code for simulating collisionless cosmological dynamics is p...
The formation of the large-scale structure, the evolution and distribution of galaxies, quasars, and...
We report on improvements made over the past two decades to our adaptive treecode N-body method (HOT...
We report on improvements made over the past two decades to our adaptive treecode N-body method (HOT...
We present Particle-Particle-Particle-Mesh (PPPM) and Tree Particle-Mesh (TreePM) implementations on...
During the last decades, Multigrid methods have been extensively used for solving large sparse linea...
We describe a hybrid technique for carrying out large N-Body simulations to study formation and evol...
International audienceRapid advances in deep learning have brought not only myriad powerful neural n...