50,825 research outputs found
Quantum Monte Carlo for large chemical systems: Implementing efficient strategies for petascale platforms and beyond
Various strategies to implement efficiently QMC simulations for large
chemical systems are presented. These include: i.) the introduction of an
efficient algorithm to calculate the computationally expensive Slater matrices.
This novel scheme is based on the use of the highly localized character of
atomic Gaussian basis functions (not the molecular orbitals as usually done),
ii.) the possibility of keeping the memory footprint minimal, iii.) the
important enhancement of single-core performance when efficient optimization
tools are employed, and iv.) the definition of a universal, dynamic,
fault-tolerant, and load-balanced computational framework adapted to all kinds
of computational platforms (massively parallel machines, clusters, or
distributed grids). These strategies have been implemented in the QMC=Chem code
developed at Toulouse and illustrated with numerical applications on small
peptides of increasing sizes (158, 434, 1056 and 1731 electrons). Using 10k-80k
computing cores of the Curie machine (GENCI-TGCC-CEA, France) QMC=Chem has been
shown to be capable of running at the petascale level, thus demonstrating that
for this machine a large part of the peak performance can be achieved.
Implementation of large-scale QMC simulations for future exascale platforms
with a comparable level of efficiency is expected to be feasible
Neuro-memristive Circuits for Edge Computing: A review
The volume, veracity, variability, and velocity of data produced from the
ever-increasing network of sensors connected to Internet pose challenges for
power management, scalability, and sustainability of cloud computing
infrastructure. Increasing the data processing capability of edge computing
devices at lower power requirements can reduce several overheads for cloud
computing solutions. This paper provides the review of neuromorphic
CMOS-memristive architectures that can be integrated into edge computing
devices. We discuss why the neuromorphic architectures are useful for edge
devices and show the advantages, drawbacks and open problems in the field of
neuro-memristive circuits for edge computing
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