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Quantum Graph Analysis

Maunz, Peter L.W.; Sterk, Jonathan D.; Lobser, Daniel; Parekh, Ojas D.; Ryan-Anderson, Ciaran

In recent years, advanced network analytics have become increasingly important to na- tional security with applications ranging from cyber security to detection and disruption of ter- rorist networks. While classical computing solutions have received considerable investment, the development of quantum algorithms to address problems, such as data mining of attributed relational graphs, is a largely unexplored space. Recent theoretical work has shown that quan- tum algorithms for graph analysis can be more efficient than their classical counterparts. Here, we have implemented a trapped-ion-based two-qubit quantum information proces- sor to address these goals. Building on Sandia's microfabricated silicon surface ion traps, we have designed, realized and characterized a quantum information processor using the hyperfine qubits encoded in two 171 Yb + ions. We have implemented single qubit gates using resonant microwave radiation and have employed Gate set tomography (GST) to characterize the quan- tum process. For the first time, we were able to prove that the quantum process surpasses the fault tolerance thresholds of some quantum codes by demonstrating a diamond norm distance of less than 1 . 9 x 10 [?] 4 . We used Raman transitions in order to manipulate the trapped ions' motion and realize two-qubit gates. We characterized the implemented motion sensitive and insensitive single qubit processes and achieved a maximal process infidelity of 6 . 5 x 10 [?] 5 . We implemented the two-qubit gate proposed by Molmer and Sorensen and achieved a fidelity of more than 97 . 7%.

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TYPE SAND Report YEAR 2015
DOIOSTI

Micro-fabricated ion traps for Quantum Information Processing; Highlights and lessons learned

Maunz, Peter L.W.; Blume-Kohout, Robin; Blain, Matthew G.; Benito, Francisco; Berry, Christopher; Clark, Craig R.; Clark, Susan M.; Colombo, Anthony; Dagel, Amber L.; Fortier, Kevin; Haltli, Raymond A.; Heller, Edwin J.; Lobser, Daniel; Mizrahi, Jonathan; Nielsen, Erik N.; Resnick, Paul; Rembetski, John F.; Rudinger, Kenneth M.; Scrymgeour, David A.; Sterk, Jonathan D.; Tabakov, Boyan; Tigges, Chris P.; Van Der Wall, Jay W.; Stick, Daniel L.

Abstract not provided.

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TYPE Conference Poster YEAR 2015
OSTI

Task Parallel Incomplete Cholesky Factorization using 2D Partitioned-Block Layout

Kim, Kyungjoo; Rajamanickam, Sivasankaran; Stelle, George W.; Edwards, Harold C.; Olivier, Stephen L.

We introduce a task-parallel algorithm for sparse incomplete Cholesky factorization that utilizes a 2D sparse partitioned-block layout of a matrix. Our factorization algorithm follows the idea of algorithms-by-blocks by using the block layout. The algorithm-byblocks approach induces a task graph for the factorization. These tasks are inter-related to each other through their data dependences in the factorization algorithm. To process the tasks on various manycore architectures in a portable manner, we also present a portable tasking API that incorporates different tasking backends and device-specific features using an open-source framework for manycore platforms i.e., Kokkos. A performance evaluation is presented on both Intel Sandybridge and Xeon Phi platforms for matrices from the University of Florida sparse matrix collection to illustrate merits of the proposed task-based factorization. Experimental results demonstrate that our task-parallel implementation delivers about 26.6x speedup (geometric mean) over single-threaded incomplete Choleskyby- blocks and 19.2x speedup over serial Cholesky performance which does not carry tasking overhead using 56 threads on the Intel Xeon Phi processor for sparse matrices arising from various application problems.

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TYPE Other Report YEAR 2015
DOIOSTI

Parallel scaling analysis for explicit solid dynamics in ALEGRA

Niederhaus, John H.J.; Drake, Richard R.; Luchini, Christopher B.

Weak scaling studies were performed for the explicit solid dynamics component of the ALEGRA code on two Cray supercomputer platforms during the period 2012-2015, involving a production-oriented hypervelocity impact problem. Results from these studies are presented, with analysis of the performance, scaling, and throughput of the code on these machines. The analysis demonstrates logarithmic scaling of the average CPU time per cycle up to core counts on the order of 10,000. At higher core counts, variable performance is observed, with significant upward excursions in compute time from the logarithmic trend. However, for core counts less than 10,000, the results show a 3 × improvement in simulation throughput, and a 2 × improvement in logarithmic scaling. This improvement is linked to improved memory performance on the Cray platforms, and to significant improvements made over this period to the data layout used by ALEGRA.

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TYPE SAND Report YEAR 2015
DOIOSTI

Time series discord detection in medical data using a parallel relational database

Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

Woodbridge, Diane M.; Wilson, Andrew T.; Bays, Nathan R.; Goldstein, Richard H.

Recent advances in sensor technology have made continuous real-time health monitoring available in both hospital and non-hospital settings. Since data collected from high frequency medical sensors includes a huge amount of data, storing and processing continuous medical data is an emerging big data area. Especially detecting anomaly in real time is important for patients' emergency detection and prevention. A time series discord indicates a subsequence that has the maximum difference to the rest of the time series subsequences, meaning that it has abnormal or unusual data trends. In this study, we implemented two versions of time series discord detection algorithms on a high performance parallel database management system (DBMS) and applied them to 240 Hz waveform data collected from 9,723 patients. The initial brute force version of the discord detection algorithm takes each possible subsequence and calculates a distance to the nearest non-self match to find the biggest discords in time series. For the heuristic version of the algorithm, a combination of an array and a trie structure was applied to order time series data for enhancing time efficiency. The study results showed efficient data loading, decoding and discord searches in a large amount of data, benefiting from the time series discord detection algorithm and the architectural characteristics of the parallel DBMS including data compression, data pipe-lining, and task scheduling.

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TYPE Conference Poster YEAR 2015
DOIOSTIScopus

Peridynamic Multiscale Finite Element Methods

Costa, Timothy; Bond, Stephen D.; Littlewood, David J.; Moore, Stan G.

The problem of computing quantum-accurate design-scale solutions to mechanics problems is rich with applications and serves as the background to modern multiscale science research. The prob- lem can be broken into component problems comprised of communicating across adjacent scales, which when strung together create a pipeline for information to travel from quantum scales to design scales. Traditionally, this involves connections between a) quantum electronic structure calculations and molecular dynamics and between b) molecular dynamics and local partial differ- ential equation models at the design scale. The second step, b), is particularly challenging since the appropriate scales of molecular dynamic and local partial differential equation models do not overlap. The peridynamic model for continuum mechanics provides an advantage in this endeavor, as the basic equations of peridynamics are valid at a wide range of scales limiting from the classical partial differential equation models valid at the design scale to the scale of molecular dynamics. In this work we focus on the development of multiscale finite element methods for the peridynamic model, in an effort to create a mathematically consistent channel for microscale information to travel from the upper limits of the molecular dynamics scale to the design scale. In particular, we first develop a Nonlocal Multiscale Finite Element Method which solves the peridynamic model at multiple scales to include microscale information at the coarse-scale. We then consider a method that solves a fine-scale peridynamic model to build element-support basis functions for a coarse- scale local partial differential equation model, called the Mixed Locality Multiscale Finite Element Method. Given decades of research and development into finite element codes for the local partial differential equation models of continuum mechanics there is a strong desire to couple local and nonlocal models to leverage the speed and state of the art of local models with the flexibility and accuracy of the nonlocal peridynamic model. In the mixed locality method this coupling occurs across scales, so that the nonlocal model can be used to communicate material heterogeneity at scales inappropriate to local partial differential equation models. Additionally, the computational burden of the weak form of the peridynamic model is reduced dramatically by only requiring that the model be solved on local patches of the simulation domain which may be computed in parallel, taking advantage of the heterogeneous nature of next generation computing platforms. Addition- ally, we present a novel Galerkin framework, the 'Ambulant Galerkin Method', which represents a first step towards a unified mathematical analysis of local and nonlocal multiscale finite element methods, and whose future extension will allow the analysis of multiscale finite element methods that mix models across scales under certain assumptions of the consistency of those models.

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TYPE SAND Report YEAR 2015
DOIOSTI

Optimizing the Performance of Reactive Molecular Dynamics Simulations for Multi-core Architectures

IEEE Transactions on Parallel and Distributed Systems

Shan, Tzu-Ray; Aktulga, Hasan M.; Knight, Chris; Coffman, Paul; Jiang, Wei

Hybrid parallelism allows high performance computing applications to better leverage the increasing on-node parallelism of modern supercomputers. In this paper, we present a hybrid parallel implementation of the widely used LAMMPS/ReaxC package, where the construction of bonded and nonbonded lists and evaluation of complex ReaxFF interactions are implemented efficiently using OpenMP parallelism. Additionally, the performance of the QEq charge equilibration scheme is examined and a dual-solver is implemented. We present the performance of the resulting ReaxC-OMP package on a state-of-the-art multi-core architecture Mira, an IBM BlueGene/Q supercomputer. For system sizes ranging from 32 thousand to 16.6 million particles, speedups in the range of 1.5-4.5x are observed using the new ReaxC-OMP software. Sustained performance improvements have been observed for up to 262,144 cores (1,048,576 processes) of Mira with a weak scaling efficiency of 91.5% in larger simulations containing 16.6 million particles.

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TYPE Journal Article YEAR 2015
OSTI
Results 5051–5100 of 9,998
Results 5051–5100 of 9,998
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