8,393 research outputs found
A Parallel General Purpose Multi-Objective Optimization Framework, with Application to Beam Dynamics
Particle accelerators are invaluable tools for research in the basic and
applied sciences, in fields such as materials science, chemistry, the
biosciences, particle physics, nuclear physics and medicine. The design,
commissioning, and operation of accelerator facilities is a non-trivial task,
due to the large number of control parameters and the complex interplay of
several conflicting design goals. We propose to tackle this problem by means of
multi-objective optimization algorithms which also facilitate a parallel
deployment. In order to compute solutions in a meaningful time frame a fast and
scalable software framework is required. In this paper, we present the
implementation of such a general-purpose framework for simulation-based
multi-objective optimization methods that allows the automatic investigation of
optimal sets of machine parameters. The implementation is based on a
master/slave paradigm, employing several masters that govern a set of slaves
executing simulations and performing optimization tasks. Using evolutionary
algorithms as the optimizer and OPAL as the forward solver, validation
experiments and results of multi-objective optimization problems in the domain
of beam dynamics are presented. The high charge beam line at the Argonne
Wakefield Accelerator Facility was used as the beam dynamics model. The 3D beam
size, transverse momentum, and energy spread were optimized
A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing
The emergence of cloud computing based on virtualization technologies brings
huge opportunities to host virtual resource at low cost without the need of
owning any infrastructure. Virtualization technologies enable users to acquire,
configure and be charged on pay-per-use basis. However, Cloud data centers
mostly comprise heterogeneous commodity servers hosting multiple virtual
machines (VMs) with potential various specifications and fluctuating resource
usages, which may cause imbalanced resource utilization within servers that may
lead to performance degradation and service level agreements (SLAs) violations.
To achieve efficient scheduling, these challenges should be addressed and
solved by using load balancing strategies, which have been proved to be NP-hard
problem. From multiple perspectives, this work identifies the challenges and
analyzes existing algorithms for allocating VMs to PMs in infrastructure
Clouds, especially focuses on load balancing. A detailed classification
targeting load balancing algorithms for VM placement in cloud data centers is
investigated and the surveyed algorithms are classified according to the
classification. The goal of this paper is to provide a comprehensive and
comparative understanding of existing literature and aid researchers by
providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres
NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors
© 2016 Cheung, Schultz and Luk.NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation
Recent Advances in Graph Partitioning
We survey recent trends in practical algorithms for balanced graph
partitioning together with applications and future research directions
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