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Natural resources of the Mekong River are essential to livelihood of tens of millions of people. Previous studies highlighted that upstream hydro-infrastructure developments impact flow regime, sediment and nutrient transport, bed and bank stability, fish productivity, biodiversity and biology of the basin. Here, we show that tidal amplification and saline water intrusion in the Mekong Delta develop with alarming paces. While offshore M2 tidal amplitude increases by 1.2–2 mm yr−1 due to sea level rise, tidal amplitude within the delta is increasing by 2 cm yr−1 and salinity in the channels is increasing by 0.2–0.5 PSU yr−1. We relate these changes to 2–3 m bed level incisions in response to sediment starvation, caused by reduced upstream sediment supply and downstream sand mining, which seems to be four times more than previous estimates. The observed trends cannot be explained by deeper channels due to relative sea level rise; while climate change poses grave natural hazards in the coming decades, anthropogenic forces drive short-term trends that already outstrip climate change effects. Considering the detrimental trends identified, it is imperative that the Mekong basin governments converge to effective transboundary management of the natural resources, before irreversible damage is made to the Mekong and its population.

We present aPDE-constrained optimization algorithm which is designed for parallel scalability on distributed-memory architectures with thousands of cores. The method is based on aline-search interior-point algorithm for large-scale continuous optimization, it is matrix-free in that it does not require the factorization of derivative matrices. Instead, it uses anew parallel and robust iterative linear solver on distributed-memory architectures. We will show almost linear parallel scalability results for the complete optimization problem, which is anew emerging important biomedical application and is related to antenna identification in hyperthermia cancer treatment planning

New features and enhancements for the SPIKE banded solver are presented. Among all the SPIKE algorithm versions, we focus our attention on the recursive SPIKE technique which provides the best trade-off between generality and parallel efficiency, but was known for its lack of flexibility. Its application was essentially limited to power of two number of cores/processors. This limitation is successfully addressed in this paper. In addition, we present a new transpose solve option, a standard feature of most numerical solver libraries which has never been addressed by the SPIKE algorithm so far. A pivoting recursive SPIKE strategy is finally presented as an alternative to non-pivoting scheme for systems with large condition numbers. All these new enhancements participate to create a feature complete SPIKE algorithm and a new black-box SPIKE-OpenMP package that significantly outperforms the performance and scalability obtained with other state-of-the-art banded solvers.

Using the trace minimization algorithm, we carried out an exact calculation of entanglement in a 19-site two-dimensional transverse Ising model. This model consists of a set of localized spin-1/2 particles in a two-dimensional triangular lattice coupled through exchange interaction J and subject to an external magnetic field of strength h. We demonstrate, for such a class of two-dimensional magnetic systems, that entanglement can be controlled and tuned by varying the parameter lambda = h/J in the Hamiltonian and by introducing impurities into the systems. Examining the derivative of the concurrence as a function of lambda shows that the system exhibits a quantum phase transition at about lambda(c) = 3.01, a transition induced by quantum fluctuations at the absolute zero of temperature.

The emergence of multicore architectures and highly scalable platforms motivates the development of novel algorithms and techniques that emphasize concurrency and are tolerant of deep memory hierarchies, as opposed to minimizing raw FLOP counts. While direct solvers are reliable, they are often slow and memory-intensive for large problems. Iterative solvers, on the other hand, are more efficient but, in the absence of robust preconditioners, lack reliability. While preconditioners based on incomplete factorizations ( whenever they exist) are effective for many problems, their parallel scalability is generally limited. In this paper, we advocate the use of banded preconditioners instead and introduce a reordering strategy that enables their extraction. In contrast to traditional bandwidth reduction techniques, our reordering strategy takes into account the magnitude of the matrix entries, bringing the heaviest elements closer to the diagonal, thus enabling the use of banded preconditioners. When used with effective banded solvers-in our case, the Spike solver-we show that banded preconditioners (i) are more robust compared to the broad class of incomplete factorization-based preconditioners, (ii) deliver higher processor performance, resulting in faster time to solution, and (iii) scale to larger parallel configurations. We demonstrate these results experimentally on a large class of problems selected from diverse application domains.

Using the trace minimization algorithm, we carried out an exact calculation of entanglement in a 19-site two-dimensional transverse Ising model. This model consists of a set of localized spin-1/2 particles in a two-dimensional triangular lattice coupled through exchange interaction J and subject to an external magnetic field of strength h. We demonstrate, for such a class of two-dimensional magnetic systems, that entanglement can be controlled and tuned by varying the parameter lambda = h/J in the Hamiltonian and by introducing impurities into the systems. Examining the derivative of the concurrence as a function of lambda shows that the system exhibits a quantum phase transition at about lambda(c) = 3.01, a transition induced by quantum fluctuations at the absolute zero of temperature.

The emergence of multicore architectures and highly scalable platforms motivates the development of novel algorithms and techniques that emphasize concurrency and are tolerant of deep memory hierarchies, as opposed to minimizing raw FLOP counts. While direct solvers are reliable, they are often slow and memory-intensive for large problems. Iterative solvers, on the other hand, are more efficient but, in the absence of robust preconditioners, lack reliability. While preconditioners based on incomplete factorizations ( whenever they exist) are effective for many problems, their parallel scalability is generally limited. In this paper, we advocate the use of banded preconditioners instead and introduce a reordering strategy that enables their extraction. In contrast to traditional bandwidth reduction techniques, our reordering strategy takes into account the magnitude of the matrix entries, bringing the heaviest elements closer to the diagonal, thus enabling the use of banded preconditioners. When used with effective banded solvers-in our case, the Spike solver-we show that banded preconditioners (i) are more robust compared to the broad class of incomplete factorization-based preconditioners, (ii) deliver higher processor performance, resulting in faster time to solution, and (iii) scale to larger parallel configurations. We demonstrate these results experimentally on a large class of problems selected from diverse application domains.

Using the trace minimization algorithm, we carried out an exact calculation of entanglement in a 19-site two-dimensional transverse Ising model. This model consists of a set of localized spin-1/2 particles in a two-dimensional triangular lattice coupled through exchange interaction J and subject to an external magnetic field of strength h. We demonstrate, for such a class of two-dimensional magnetic systems, that entanglement can be controlled and tuned by varying the parameter lambda = h/J in the Hamiltonian and by introducing impurities into the systems. Examining the derivative of the concurrence as a function of lambda shows that the system exhibits a quantum phase transition at about lambda(c) = 3.01, a transition induced by quantum fluctuations at the absolute zero of temperature.

The emergence of multicore architectures and highly scalable platforms motivates the development of novel algorithms and techniques that emphasize concurrency and are tolerant of deep memory hierarchies, as opposed to minimizing raw FLOP counts. While direct solvers are reliable, they are often slow and memory-intensive for large problems. Iterative solvers, on the other hand, are more efficient but, in the absence of robust preconditioners, lack reliability. While preconditioners based on incomplete factorizations ( whenever they exist) are effective for many problems, their parallel scalability is generally limited. In this paper, we advocate the use of banded preconditioners instead and introduce a reordering strategy that enables their extraction. In contrast to traditional bandwidth reduction techniques, our reordering strategy takes into account the magnitude of the matrix entries, bringing the heaviest elements closer to the diagonal, thus enabling the use of banded preconditioners. When used with effective banded solvers-in our case, the Spike solver-we show that banded preconditioners (i) are more robust compared to the broad class of incomplete factorization-based preconditioners, (ii) deliver higher processor performance, resulting in faster time to solution, and (iii) scale to larger parallel configurations. We demonstrate these results experimentally on a large class of problems selected from diverse application domains.

Using the trace minimization algorithm, we carried out an exact calculation of entanglement in a 19-site two-dimensional transverse Ising model. This model consists of a set of localized spin-1/2 particles in a two-dimensional triangular lattice coupled through exchange interaction J and subject to an external magnetic field of strength h. We demonstrate, for such a class of two-dimensional magnetic systems, that entanglement can be controlled and tuned by varying the parameter lambda = h/J in the Hamiltonian and by introducing impurities into the systems. Examining the derivative of the concurrence as a function of lambda shows that the system exhibits a quantum phase transition at about lambda(c) = 3.01, a transition induced by quantum fluctuations at the absolute zero of temperature.

The emergence of multicore architectures and highly scalable platforms motivates the development of novel algorithms and techniques that emphasize concurrency and are tolerant of deep memory hierarchies, as opposed to minimizing raw FLOP counts. While direct solvers are reliable, they are often slow and memory-intensive for large problems. Iterative solvers, on the other hand, are more efficient but, in the absence of robust preconditioners, lack reliability. While preconditioners based on incomplete factorizations ( whenever they exist) are effective for many problems, their parallel scalability is generally limited. In this paper, we advocate the use of banded preconditioners instead and introduce a reordering strategy that enables their extraction. In contrast to traditional bandwidth reduction techniques, our reordering strategy takes into account the magnitude of the matrix entries, bringing the heaviest elements closer to the diagonal, thus enabling the use of banded preconditioners. When used with effective banded solvers-in our case, the Spike solver-we show that banded preconditioners (i) are more robust compared to the broad class of incomplete factorization-based preconditioners, (ii) deliver higher processor performance, resulting in faster time to solution, and (iii) scale to larger parallel configurations. We demonstrate these results experimentally on a large class of problems selected from diverse application domains.

This report describes in detail studies developing and evaluating sparse linear systems on scalable architectures, with emphasis on preconditioned iterative solvers. The study was motivated primarily by the lack of robustness of Krylov subspace iterative schemes with generic, black-box, pre-conditioners such as approximate (or incomplete) LU-factorizations. In this report the authors advocate the use of banded pre-conditioners after suitable reordering of the sparse linear systems. The choice of the reordering scheme is based on: (1) minimizing the bandwidth, and (2) bringing as many of the largest elements of the coefficient matrix as possible to a narrow central band., Prepared in cooperation with Case Western Reserve University, Cleveland, OH. Sponsored in part by Defense Advanced Research Projects Agency (DARPA). The original document contains color images.

The purpose of the research was to study robust iterative methods for solving spare (block tridiagonal) nonsymmetric linear systems in a parallel computing environment. A new method was developed which uses block-row symmetric successive overrelaxation (SSOR) with conjugate gradient (CG) acceleration. The method is robust, with convergence assured even for poorly conditioned systems, and the method is easily implemented in a parallel environment. The method transforms a nonsymmetric system with an arbitrary eigenvalue distribution into a symmetric one with eigenvalue restricted to the interval (0,1). Research included testing of the algorithms on an Alliant FX/8 multiprocessor where it was demonstrated that the methods is very robust and performs better than standard existing methods.

The most used methods for solving algebraic eigen-value problems are discussed in detail in this report. These methods are Jacobi's, Householder's and QR-algorithms. Attempts are made to adapt or even modify the algorithm to take advantage of parallel computations. (Author)

We present aPDE-constrained optimization algorithm which is designed for parallel scalability on distributed-memory architectures with thousands of cores. The method is based on aline-search interior-point algorithm for large-scale continuous optimization, it is matrix-free in that it does not require the factorization of derivative matrices. Instead, it uses anew parallel and robust iterative linear solver on distributed-memory architectures. We will show almost linear parallel scalability results for the complete optimization problem, which is anew emerging important biomedical application and is related to antenna identification in hyperthermia cancer treatment planning