9,704 research outputs found
Free energy reconstruction from steered dynamics without post-processing
Various methods achieving importance sampling in ensembles of nonequilibrium
trajectories enable to estimate free energy differences and, by
maximum-likelihood post-processing, to reconstruct free energy landscapes.
Here, based on Bayes theorem, we propose a more direct method in which a
posterior likelihood function is used both to construct the steered dynamics
and to infer the contribution to equilibrium of all the sampled states. The
method is implemented with two steering schedules. First, using non-autonomous
steering, we calculate the migration barrier of the vacancy in Fe-alpha.
Second, using an autonomous scheduling related to metadynamics and equivalent
to temperature-accelerated molecular dynamics, we accurately reconstruct the
two-dimensional free energy landscape of the 38-atom Lennard-Jones cluster as a
function of an orientational bond-order parameter and energy, down to the
solid-solid structural transition temperature of the cluster and without
maximum-likelihood post-processing.Comment: Accepted manuscript in Journal of Computational Physics, 7 figure
The Blacklisting Memory Scheduler: Balancing Performance, Fairness and Complexity
In a multicore system, applications running on different cores interfere at
main memory. This inter-application interference degrades overall system
performance and unfairly slows down applications. Prior works have developed
application-aware memory schedulers to tackle this problem. State-of-the-art
application-aware memory schedulers prioritize requests of applications that
are vulnerable to interference, by ranking individual applications based on
their memory access characteristics and enforcing a total rank order.
In this paper, we observe that state-of-the-art application-aware memory
schedulers have two major shortcomings. First, such schedulers trade off
hardware complexity in order to achieve high performance or fairness, since
ranking applications with a total order leads to high hardware complexity.
Second, ranking can unfairly slow down applications that are at the bottom of
the ranking stack. To overcome these shortcomings, we propose the Blacklisting
Memory Scheduler (BLISS), which achieves high system performance and fairness
while incurring low hardware complexity, based on two observations. First, we
find that, to mitigate interference, it is sufficient to separate applications
into only two groups. Second, we show that this grouping can be efficiently
performed by simply counting the number of consecutive requests served from
each application.
We evaluate BLISS across a wide variety of workloads/system configurations
and compare its performance and hardware complexity, with five state-of-the-art
memory schedulers. Our evaluations show that BLISS achieves 5% better system
performance and 25% better fairness than the best-performing previous scheduler
while greatly reducing critical path latency and hardware area cost of the
memory scheduler (by 79% and 43%, respectively), thereby achieving a good
trade-off between performance, fairness and hardware complexity
Optimal Operation of a Northwest Grid of Saudi Arabia Including Renewable Resources
The use of fossil fuel, which has been one of the major sources of energy of the modern world, has led to environmental concerns. One solution to these issues is the application of renewable energy, which can also address the fluctuation in fuel prices. The Kingdom of Saudi Arabia (KSA) faces a demand of energy expected to exceed 120 GW by 2032.
The government is taking appropriate actions, introducing sustainable renewable energy not only to meet the demand with clean energy sources but also to reduce the Kingdom’s consumption of fuel and gas. KSA, which has a high irradiation rate especially in the northwest area, Tabuk Region, plans to invest 41 GW maximum of solar power. In light of this decision, this research will present a comprehensive study of PV penetration up to peak output of 40 MW with battery storage to the isolated northwest grid, Tabuk Grid, as a first stage development.
However, the increase of grid-connected photovoltaic (PV) in the presence of nonlinear loads, and the growth of power electronic applications produce harmonics in the power system. These harmonics may distort the current and voltage waveforms which impact the power quality and affect the operation of all electric devices.
Renewable energy systems nowadays are sufficiently developed to be widely used for environmental and economic dispatch (ED) concerns. However, renewable energy that are not geographically distributed present a considerable challenge with respect to variability and availability. One of the solutions for addressing the challenge of solar variability is to use battery storage, which has been found to be effective when working in parallel with PV in peak load shaving. Time shifting renewable energy generation through the use of Battery Energy Storage Systems (BESS) can reduce the operating cost. Many studies have been focused on optimal operation with PV and battery storage. However, while achieving this optimal operation for the generators is necessary, it does not ensure secure operation of power systems. Therefore, validating secure operation with optimal generation scheduling is important. Furthermore, disregarding the battery life in optimal power scheduling creates an unrealistic scenario since replacing the battery is costly.
In this research, a comprehensive study of a 40 MW PV penetration with battery storage to the Tabuk Grid is presented. The study includes complete simulation and analysis of the PV integration with storage. Moreover, a power quality study for the PV farm is conducted, one that included nonlinear loads to enhance the analysis regarding harmonics penetration. In addition, this research presents an optimal generation scheduling considering renewable energy sources, the BESS, battery life and short term outages. This will enables the system to respond and resolve outages quickly without affecting the optimal operation. The feasibility of the proposed approach is demonstrated on Tabuk system – an isolated northwest grid in Saudi Arabia
Parallel Algorithms for Time and Frequency Domain Circuit Simulation
As a most critical form of pre-silicon verification, transistor-level circuit simulation
is an indispensable step before committing to an expensive manufacturing process.
However, considering the nature of circuit simulation, it can be computationally
expensive, especially for ever-larger transistor circuits with more complex device models.
Therefore, it is becoming increasingly desirable to accelerate circuit simulation.
On the other hand, the emergence of multi-core machines offers a promising solution
to circuit simulation besides the known application of distributed-memory clustered
computing platforms, which provides abundant hardware computing resources. This
research addresses the limitations of traditional serial circuit simulations and proposes
new techniques for both time-domain and frequency-domain parallel circuit
simulations.
For time-domain simulation, this dissertation presents a parallel transient simulation
methodology. This new approach, called WavePipe, exploits coarse-grained
application-level parallelism by simultaneously computing circuit solutions at multiple
adjacent time points in a way resembling hardware pipelining. There are two
embodiments in WavePipe: backward and forward pipelining schemes. While the
former creates independent computing tasks that contribute to a larger future time
step, the latter performs predictive computing along the forward direction. Unlike
existing relaxation methods, WavePipe facilitates parallel circuit simulation without jeopardizing convergence and accuracy. As a coarse-grained parallel approach, it requires
low parallel programming effort, furthermore it creates new avenues to have a
full utilization of increasingly parallel hardware by going beyond conventional finer
grained parallel device model evaluation and matrix solutions.
This dissertation also exploits the recently developed explicit telescopic projective
integration method for efficient parallel transient circuit simulation by addressing the
stability limitation of explicit numerical integration. The new method allows the
effective time step controlled by accuracy requirement instead of stability limitation.
Therefore, it not only leads to noticeable efficiency improvement, but also lends itself
to straightforward parallelization due to its explicit nature.
For frequency-domain simulation, this dissertation presents a parallel harmonic
balance approach, applicable to the steady-state and envelope-following analyses of
both driven and autonomous circuits. The new approach is centered on a naturally-parallelizable
preconditioning technique that speeds up the core computation in harmonic
balance based analysis. The proposed method facilitates parallel computing
via the use of domain knowledge and simplifies parallel programming compared with
fine-grained strategies. As a result, favorable runtime speedups are achieved
Management of Islanded Operation of Microgirds
Distributed generations with continuously growing penetration levels offer potential solutions to energy security and reliability with minimum environmental impacts. Distributed Generations when connected to the area electric power systems provide numerous advantages. However, grid integration of distributed generations presents several technical challenges which has forced the systems planners and operators to account for the repercussions on the distribution feeders which are no longer passive in the presence of distributed generations.
Grid integration of distributed generations requires accurate and reliable islanding detection methodology for secure system operation. Two distributed generation islanding detection methodologies are proposed in this dissertation. First, a passive islanding detection technique for grid-connected distributed generations based on parallel decision trees is proposed. The proposed approach relies on capturing the underlying signature of a wide variety of system events on a set of critical system parameters and utilizes multiple optimal decision tress in a parallel network for classification of system events. Second, a hybrid islanding detection method for grid-connected inverter based distributed generations combining decision trees and Sandia frequency shift method is also proposed. The proposed method combines passive and active islanding detection techniques to aggregate their individual advantages and reduce or eliminate their drawbacks.
In smart grid paradigm, microgrids are the enabling engine for systematic integration of distributed generations with the utility grid. A systematic approach for controlled islanding of grid-connected microgrids is also proposed in this dissertation. The objective of the proposed approach is to develop an adaptive controlled islanding methodology to be implemented as a preventive control component in emergency control strategy for microgrid operations.
An emergency power management strategy for microgrid autonomous operation subsequent to inadvertent islanding events is also proposed in this dissertation. The proposed approach integrates microgrid resources such as energy storage systems, demand response resources, and controllable micro-sources to layout a comprehensive power management strategy for ensuring secure and stable microgrid operation following an unplanned islanding event.
In this dissertation, various case studies are presented to validate the proposed methods. The simulation results demonstrate the effectiveness of the proposed methodologies
Modelling Load Balancing and Carrier Aggregation in Mobile Networks
In this paper, we study the performance of multicarrier mobile networks.
Specifically, we analyze the flow-level performance of two inter-carrier load
balancing schemes and the gain engendered by Carrier Aggregation (CA). CA is
one of the most important features of HSPA+ and LTE-A networks; it allows
devices to be served simultaneously by several carriers. We propose two load
balancing schemes, namely Join the Fastest Queue (JFQ) and Volume Balancing
(VB), that allow the traffic of CA and non-CA users to be distributed over the
aggregated carriers. We then evaluate the performance of these schemes by means
of analytical modeling. We show that the proposed schemes achieve quasi-ideal
load balancing. We also investigate the impact of mixing traffic of CA and
non-CA users in the same cell and show that performance is practically
insensitive to the traffic mix.Comment: 8 pages, 6 figures, submitted to WiOpt201
Speculative Segmented Sum for Sparse Matrix-Vector Multiplication on Heterogeneous Processors
Sparse matrix-vector multiplication (SpMV) is a central building block for
scientific software and graph applications. Recently, heterogeneous processors
composed of different types of cores attracted much attention because of their
flexible core configuration and high energy efficiency. In this paper, we
propose a compressed sparse row (CSR) format based SpMV algorithm utilizing
both types of cores in a CPU-GPU heterogeneous processor. We first
speculatively execute segmented sum operations on the GPU part of a
heterogeneous processor and generate a possibly incorrect results. Then the CPU
part of the same chip is triggered to re-arrange the predicted partial sums for
a correct resulting vector. On three heterogeneous processors from Intel, AMD
and nVidia, using 20 sparse matrices as a benchmark suite, the experimental
results show that our method obtains significant performance improvement over
the best existing CSR-based SpMV algorithms. The source code of this work is
downloadable at https://github.com/bhSPARSE/Benchmark_SpMV_using_CSRComment: 22 pages, 8 figures, Published at Parallel Computing (PARCO
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