2,350 research outputs found
High performance Python for direct numerical simulations of turbulent flows
Direct Numerical Simulations (DNS) of the Navier Stokes equations is an
invaluable research tool in fluid dynamics. Still, there are few publicly
available research codes and, due to the heavy number crunching implied,
available codes are usually written in low-level languages such as C/C++ or
Fortran. In this paper we describe a pure scientific Python pseudo-spectral DNS
code that nearly matches the performance of C++ for thousands of processors and
billions of unknowns. We also describe a version optimized through Cython, that
is found to match the speed of C++. The solvers are written from scratch in
Python, both the mesh, the MPI domain decomposition, and the temporal
integrators. The solvers have been verified and benchmarked on the Shaheen
supercomputer at the KAUST supercomputing laboratory, and we are able to show
very good scaling up to several thousand cores.
A very important part of the implementation is the mesh decomposition (we
implement both slab and pencil decompositions) and 3D parallel Fast Fourier
Transforms (FFT). The mesh decomposition and FFT routines have been implemented
in Python using serial FFT routines (either NumPy, pyFFTW or any other serial
FFT module), NumPy array manipulations and with MPI communications handled by
MPI for Python (mpi4py). We show how we are able to execute a 3D parallel FFT
in Python for a slab mesh decomposition using 4 lines of compact Python code,
for which the parallel performance on Shaheen is found to be slightly better
than similar routines provided through the FFTW library. For a pencil mesh
decomposition 7 lines of code is required to execute a transform
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
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