81 research outputs found
Vector computers, Monte Carlo simulation, and regression analysis: An introduction (Version 2)
Monte Carlo Technique;Supercomputer;computer science
Supercomputers, Monte Carlo simulation and regression analysis
Monte Carlo Technique;Supercomputer;computer science
Results from Shell Model Monte Carlo Studies
We review results obtained using Shell Model Monte Carlo (SMMC) techniques.
These methods reduce the imaginary-time many-body evolution operator to a
coherent superposition of one-body evolutions in fluctuating one-body fields;
the resultant path integral is evaluated stochastically. After a brief review
of the methods, we discuss a variety of nuclear physics applications. These
include studies of the ground-state properties of pf-shell nuclei, Gamow-Teller
strength distributions, thermal and rotational pairing properties of nuclei
near N=Z, -soft nuclei, and -decay in ^{76}Ge. Several
other illustrative calculations are also reviewed. Finally, we discuss
prospects for further progress in SMMC and related calculations
GeantV: Results from the prototype of concurrent vector particle transport simulation in HEP
Full detector simulation was among the largest CPU consumer in all CERN
experiment software stacks for the first two runs of the Large Hadron Collider
(LHC). In the early 2010's, the projections were that simulation demands would
scale linearly with luminosity increase, compensated only partially by an
increase of computing resources. The extension of fast simulation approaches to
more use cases, covering a larger fraction of the simulation budget, is only
part of the solution due to intrinsic precision limitations. The remainder
corresponds to speeding-up the simulation software by several factors, which is
out of reach using simple optimizations on the current code base. In this
context, the GeantV R&D project was launched, aiming to redesign the legacy
particle transport codes in order to make them benefit from fine-grained
parallelism features such as vectorization, but also from increased code and
data locality. This paper presents extensively the results and achievements of
this R&D, as well as the conclusions and lessons learnt from the beta
prototype.Comment: 34 pages, 26 figures, 24 table
Gradient-Based Markov Chain Monte Carlo for MIMO Detection
Accurately detecting symbols transmitted over multiple-input multiple-output
(MIMO) wireless channels is crucial in realizing the benefits of MIMO
techniques. However, optimal MIMO detection is associated with a complexity
that grows exponentially with the MIMO dimensions and quickly becomes
impractical. Recently, stochastic sampling-based Bayesian inference techniques,
such as Markov chain Monte Carlo (MCMC), have been combined with the gradient
descent (GD) method to provide a promising framework for MIMO detection. In
this work, we propose to efficiently approach optimal detection by exploring
the discrete search space via MCMC random walk accelerated by Nesterov's
gradient method. Nesterov's GD guides MCMC to make efficient searches without
the computationally expensive matrix inversion and line search. Our proposed
method operates using multiple GDs per random walk, achieving sufficient
descent towards important regions of the search space before adding random
perturbations, guaranteeing high sampling efficiency. To provide augmented
exploration, extra samples are derived through the trajectory of Nesterov's GD
by simple operations, effectively supplementing the sample list for statistical
inference and boosting the overall MIMO detection performance. Furthermore, we
design an early stopping tactic to terminate unnecessary further searches,
remarkably reducing the complexity. Simulation results and complexity analysis
reveal that the proposed method achieves near-optimal performance in both
uncoded and coded MIMO systems, adapts to realistic channel models, and scales
well to large MIMO dimensions.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
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On the suitability of the connection machine for direct particle simulation
The algorithmic structure was examined of the vectorizable Stanford particle simulation (SPS) method and the structure is reformulated in data parallel form. Some of the SPS algorithms can be directly translated to data parallel, but several of the vectorizable algorithms have no direct data parallel equivalent. This requires the development of new, strictly data parallel algorithms. In particular, a new sorting algorithm is developed to identify collision candidates in the simulation and a master/slave algorithm is developed to minimize communication cost in large table look up. Validation of the method is undertaken through test calculations for thermal relaxation of a gas, shock wave profiles, and shock reflection from a stationary wall. A qualitative measure is provided of the performance of the Connection Machine for direct particle simulation. The massively parallel architecture of the Connection Machine is found quite suitable for this type of calculation. However, there are difficulties in taking full advantage of this architecture because of lack of a broad based tradition of data parallel programming. An important outcome of this work has been new data parallel algorithms specifically of use for direct particle simulation but which also expand the data parallel diction
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