17478869
Teraflop Computing for Nanoscience
Over the last three decades there has been significant progress in the first principles methods for calculating the properties of materials at the quantum level. They have largely been based on the local density approximation (LDA) to density functional theory (DFT). However, nanoscience places new demands on these first principles methods because of the thousands to millions of atoms present in even the simplest of nano-structured materials. Recent advances in the locally self-consistent multiple scattering (LSMS) method are making the direct quantum mechanical simulation of nano-structured materials possible. The LSMS method is an order-N approach to first principles electronic structure calculation. It is highly scalable on massively parallel processing supercomputers, and is suited for performing large unit cell simulations to study the electronic and magnetic properties of materials with complex structure. In this presentation, we show that the LSMS accomplishes the first step towards understanding the electronic and magnetic structure of nano-structured materials with dimension size close to 10 nanometers (nm). As an example, we describe a 16,000 atom calculation of the electronic and magnetic structure calculated for an iron nanoparticle embedded in iron aluminide crystal matrix.
45169888
Multi-teraflops spin dynamics studies of the magnetic structure of FeMn/Co interfaces
8929565
Integrating the QMR Method with First Principles Material Science Application Code
First principles methods are used to aid the material designers and metallurgists in the investigation and design of new materials. These methods suffer from a divergent O(N{sup 3}) scaling which restricts the problem sizes that can be addressed. It will be demonstrated that the O(N{sup 3}) scaling problem can be overcome by employing real-space multiple scattering techniques to calculate the scattering properties, combined with the QMR algorithm. By incorporating the QMR method into our real-space multiple scattering code, we observed O(N) scaling for the large problem sizes of interest. This advancement will provide researchers with the necessary tools to treat large systems.
7145981
High performance first principles method for complex magnetic properties
The understanding of metallic magnetism is of fundamental importance for a wide range of technological applications ranging from thin film disc drive read heads to bulk magnets used in motors and power generation. In this submission for the Gordon Bell Prize we use the power of massively parallel processing (MPP) computers to perform first principles calculations of large system models of non-equilibrium magnetic states in metallic magnets. The calculations are based on a new constrained local moment (CLM) model that places the recently proposed Spin-Dynamics of Antropov et al. [1] on firm theoretical foundations. The equations of constrained local spin density approximation (constrained LSDA) are solved using the massively parallel locally self-consistent multiple scattering (LSMS) method[3] extended to treat general non-collinear arrangements of the magnetic moments [4]. A general algorithm has been developed for self-consistently finding the constraining fields which are introduced into LSDA in order to maintain a prescribed magnetic moment orientation configuration. The existence of CLM states is demonstrated for 1024 atom per unit cell models of Iron above its Curie temperature. The constrained LSMS method we have developed exploits the locality in the physics of the problem to produce an algorithm that has only local and limited communications on parallel computers leading to very good scale-up to large processor counts and linear scaling of the number of operations with the number of atoms in the system. The computationally intensive step of inversion of a dense complex matrix is largely reduced to matrix-matrix multiplies which are implemented in BLAS. Throughout the code attention is paid to minimizing both the total operation count and total execution time, with primacy given to the latter. Full 64-bit arithmetic is used throughout. The code shows near linear scale-up to 1024-processing elements (PE) and attains a performance of 657 Gflops on a Cray T3E1200 LC1024 at a US Government site. Performance figures of 276 Gflops and 329 Gflops have also been obtained on T3E900 and T3E1200 LC512 machines at the National Energy Research Scientific Computing Center (NERSC), and Cray Research respectively. All performance figures include necessary I/O.
12030912
Local optimization of triangular surface meshes for general quadrics in lp norm
For a set S of distinct points in the x-y plane and a 3-dimensional surface with the general quadratic equation f ey dx cxy by ax z + + + + + = 2 2 we study the problem of finding a locally optimal triangulation of S for the linear approximation of the surface under the Lp norm. We show that the Lp norm error is independent of the translation and rotation by 180 degrees of a single triangle in the x-y plane. This observation allows us to substantially simply the local optimization procedure, and in particular allows us to explicitly compute the separation curves. Locally optimal triangulations are important in the data dependent approximation methods. The paper generalizes earlier results by P. Desnogues and O. Devillers (presented at the Canadian Conference on Computational Geometry CCCG ‘95), who studied optimal local triangulations for the unit hyperbolic paraboloid in the L2 norm error.
714502
Face Re-Lighting from a Single Image under Harsh Lighting Conditions
In this paper, we present a new method to change the illumination condition of a face image, with unknown face geometry and albedo information. This problem is particularly difficult when there is only one single image of the subject available and it was taken under a harsh lighting condition. Recent research demonstrates that the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a low-dimensional linear subspace using spherical harmonic representation. However, the approximation error can be large under harsh lighting conditions thus making it difficult to recover albedo information. In order to address this problem, we propose a subregion based framework that uses a Markov Random Field to model the statistical distribution and spatial coherence of face texture, which makes our approach not only robust to harsh lighting conditions, but insensitive to partial occlusions as well. The performance of our framework is demonstrated through various experimental results, including the improvement to the face recognition rate under harsh lighting conditions.
18355899
A microscopic view on community detection in complex networks
The community structure is a basic characteristic of complex networks. A strong community structure has high modularity. It has been proven an NP-Complete problem to identify the community structure with the highest modularity. Many approximate algorithms have been proposed to alleviate the problem. However, they suffer from inefficiency or low quality. In this paper, we propose a two-step method. The first step of our method analyze the vertex similarity of the network, which is a microscopic view. If a pair of vertices are similar enough, they will be put into the same community. The second step of our method focuses on the increment of modularity of the similarity-based communities generated by the first step. If the number of edges between two communities is greater than the expected number based on random choice, the two communities will be merged. The second step is implemented by the CNM algorithm or its improvement CNM+HE'. The similarity-based community remedies the defect on microscope introduced by CNM or CNM+HE'. Our method runs efficiently and finds meaningful communities effectively. We tested the method on more than twenty datasets. The modularity of community structure found by the method is higher than the state-of-the-art algorithm.
9587091
Topology control for delay-constraint data collection in wireless sensor networks
Data collection is one of the most important operations in wireless sensor networks. Many practical applications require the real-time data transmission, such as monitoring, tracking, etc. In this paper, we import and define the topology control problem for delay-constraint data collection (TDDC), and then formalize this problem into an integer programming problem. As NP-Hardness of this problem, we present a load-aware power-increased topology control algorithm (namely LPTC) to heuristically solve the problem. The theoretical analysis shows that this algorithm can reach O(1)-approximation ratio for the linear networks. And we also analyze the impact of the delay-constraint on the worst-case for the planar networks. Moreover, this paper designs two localized algorithms, called as SDEL and DDEL, based on the area division for TDDC problem. The experimental results show that LPTC algorithm can save at least 17% power consumptions compared with HBH algorithm in many situations.
9607875
Using Dataflow Information to Improve Inter-Workflow Instance Concurrency
The control-flow-based design of traditional batch scheduling systems (i.e., Job A must finish before Job B is started) can constrain the concurrency in schedulers for high-performance computing (HPC) workloads. There are two main problems. First, the control-flow graph, representing the workflow, may be inherently limited in its degree of concurrency. Second, if the naming strategy of the input and output files of jobs is simplistic, there may be a filename conflict problem when multiple instances of the same workflow run concurrently.