1,553 research outputs found
q-State Potts model metastability study using optimized GPU-based Monte Carlo algorithms
We implemented a GPU based parallel code to perform Monte Carlo simulations
of the two dimensional q-state Potts model. The algorithm is based on a
checkerboard update scheme and assigns independent random numbers generators to
each thread. The implementation allows to simulate systems up to ~10^9 spins
with an average time per spin flip of 0.147ns on the fastest GPU card tested,
representing a speedup up to 155x, compared with an optimized serial code
running on a high-end CPU. The possibility of performing high speed simulations
at large enough system sizes allowed us to provide a positive numerical
evidence about the existence of metastability on very large systems based on
Binder's criterion, namely, on the existence or not of specific heat
singularities at spinodal temperatures different of the transition one.Comment: 30 pages, 7 figures. Accepted in Computer Physics Communications.
code available at:
http://www.famaf.unc.edu.ar/grupos/GPGPU/Potts/CUDAPotts.htm
Computational Complexity for Physicists
These lecture notes are an informal introduction to the theory of
computational complexity and its links to quantum computing and statistical
mechanics.Comment: references updated, reprint available from
http://itp.nat.uni-magdeburg.de/~mertens/papers/complexity.shtm
On the Emerging Potential of Quantum Annealing Hardware for Combinatorial Optimization
Over the past decade, the usefulness of quantum annealing hardware for
combinatorial optimization has been the subject of much debate. Thus far,
experimental benchmarking studies have indicated that quantum annealing
hardware does not provide an irrefutable performance gain over state-of-the-art
optimization methods. However, as this hardware continues to evolve, each new
iteration brings improved performance and warrants further benchmarking. To
that end, this work conducts an optimization performance assessment of D-Wave
Systems' most recent Advantage Performance Update computer, which can natively
solve sparse unconstrained quadratic optimization problems with over 5,000
binary decision variables and 40,000 quadratic terms. We demonstrate that
classes of contrived problems exist where this quantum annealer can provide run
time benefits over a collection of established classical solution methods that
represent the current state-of-the-art for benchmarking quantum annealing
hardware. Although this work does not present strong evidence of an irrefutable
performance benefit for this emerging optimization technology, it does exhibit
encouraging progress, signaling the potential impacts on practical optimization
tasks in the future
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