3021341
Parallel multi-teraflops studies of the magnetic structure of FeMn alloys
Massively parallel computers have been used to perform first principles spin dynamics (SD) simulations of the magnetic structure of /spl gamma/-FeMn (iron-manganese). The code uses a novel parallel approach to solve the Kohn-Sham equations obtained from density functional theory (DFT). This approach uses limited local communications avoiding the large global communications found in other DFT based methods. This allows our code to scale efficiently to very large processor counts and large system sizes. Computationally our method can be implemented using mainly BLAS3 routines which typically run at a very high percentage of peak speed on most computers. Our large scale quantum mechanical simulations using this method reveal new details of the orientational configuration of the magnetic moments of /spl gamma/-FeMn that are unobtainable by any other means. The understanding of the magnetic structure of /spl gamma/-FeMn is important in exchange bias, which involves the use of an antiferromagnetic (AFM) layer such as FeMn to pin the orientation of the magnetic moment of a proximate ferromagnetic (FM). Exchange bias is of fundamental importance in magnetic multilayer storage and read head devices. Here the equation of motion of first principles SD is used to perform relaxations of model magnetic structures to the true ground (equilibrium) state. Our code has achieved a maximum execution rate of 2.46 teraflops on the IBM SP at the National Energy Research Scientific Computing Center (NERSC) and 4.65 teraflops on the HP-Compaq machine at the Pittsburgh Supercomputing Center (PSC).
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.
45169888
Multi-teraflops spin dynamics studies of the magnetic structure of FeMn/Co interfaces
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.
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.
3104032
Robot Navigation by Waypoints
In this paper we propose a novel waypoint-based robot navigation method that combines reactive and deliberative actions. The approach uses reactive exploration to generate waypoints that can then be used by a deliberative system to plan future movements through the same environment. The waypoints are used largely to provide the interface between reactive and deliberative navigation and a range of methods could be used for either type of navigation. In the current work, an incremental decision tree method is used to navigate the robot reactively from the specified initial position to its destination avoiding obstacles in its path and a genetic algorithm method is used to perform the deliberative navigation. The new method is shown to have a number of practical advantages. Firstly, in contrast with many deliberative approaches, complete knowledge of the environment is not required, nor is it necessary to make assumptions regarding the geometry of obstacles. Secondly, the presence of a reactive navigator means it is always possible to continue directed movements in unknown or changing environments or when time constraints become particularly demanding. Thirdly, the use of waypoints allows escape from certain obstacle configurations that would normally trap robots navigated under the control of purely reactive methods. In addition, the results presented in this paper from a number of realistic simulated environments show that the adoption of waypoints significantly reduces the time to calculate a deliberative path.
1772294
Scalable real-time system design using preemption thresholds
The maturity of schedulability analysis techniques for fixed-priority preemptive scheduling has enabled the consideration of timing issues at design time using a specification of the tasking architecture and estimates of execution times for tasks. While successful, this approach has limitations since the preemptive multi-tasking model does not scale well for a large number of tasks, and the fixed-priority scheduling theory does not work well with many object-oriented design methods. In this paper, we present an approach that scales well even when the design consists of a large number of concurrent jobs. The approach avoids any unnecessary preemptability in the system, thereby resulting in reduced run-time overheads from preemptions and associated context switches. It also allows significant memory savings by grouping jobs into non-preemptive groups and then sharing the stack space between them. Our approach is based on our earlier work on scheduling using preemption thresholds that allows parametric control over preemptability in a priority-based system. We show that our approach provides significant advantages over one using a fixed-priority preemptive scheduling architecture. The benefits include higher schedulability for small numbers of tasks and lower run-time overheads, and hence better scalability. We develop algorithms that allow design-time consideration of schedulability and automatic synthesis of an implementation model to minimize run-time overheads.
40027798
Multiple Ranker Method in Document Retrieval
In this paper, we propose a multiple-ranker approach to make learning to rank methods more effective for document retrieval application. In traditional learning to rank methods, a ranker is learned from a set of queries together with their corresponding document rankings labeled by experts, and it is then used to predict the document rankings for new queries. But in practice, user queries vary in large diversity, which makes the single ranker learned from a close set of data not representative. The single ranker cannot be guaranteed with the best ranking result for every single query, and this becomes the bottleneck of traditional learning to rank approaches. To address this problem, we propose a multi-ranker approach. We train multiple diverse rankers which can cover diverse categories of queries, instead of an isolated one, and take an ensemble of these rankers for final prediction. We verify the proposed multipleranker approach over real-world datasets. The experimental results indicate that the proposed approach can outperform existing ‘learning to rank’ methods significantly.
7500432
A Method to Organize A Small-World Based Publish/Subscribe Network In Large-Scale Environment
The paper proposes a Small-World Network Publish/ Subscribe (SWN-P/S) model in large-scale environment, which is a novel Event-based Middleware with Small-World Network (SWN) model. Through connecting brokers into a small world network, SWN-P/S achieves both a strong local clustering and a small average path length among vast brokers. These characteristics help to spread information through the system more rapidly. The paper also put forwards a cloud-partition method to organize brokers in terms of clustering coefficient in SWN. The method is useful to reduce the amount of routing information stored in each broker and to accelerate information dissemination. Comparisons of SWN-P/S with other construction ways for Publish/Subscribe Event-based Middleware show that SWN-P/S is more suitable for building large-scale distributed systems.
37274241
Preparation of one-dimensional photonic crystal with variable period by using ultra-high vacuum electron beam evaporation
One-dimensional photonic crystal with variable period has been prepared by using the method of ultra-high vacuum electron beam evaporation. Advantage of this method is gaining variable period easily. The transmittance of the photonic crystal has been also studied.