It is shown that an understanding of some key biological processes such as the thermodynamics of enzyme-ligand binding or the selectivity of ion-channels is ultimately dependent on an understanding of the details of i...
It is shown that an understanding of some key biological processes such as the thermodynamics of enzyme-ligand binding or the selectivity of ion-channels is ultimately dependent on an understanding of the details of ion hydration. Therefore, a model for calculating the hydration free energy of ions in aqueous solvent is presented. The model is used to first calculate the proton hydration free energy, ΔGhyd(H+), in an effort to resolve the uncertainty concerning its exact value. In the model we define ΔGhyd(H+) as the free energy change associated with the following process: ΔG{H+(gas)+[H2O]n(aq)→H+[H2O]n)(aq)}, where the solvent is represented by a neutral n-water cluster embedded in a dielectric continuum and the solvated proton is represented by a protonated n-water cluster also in the continuum. All solvated species are treated as quantum mechanical solutes (B3LYP, MP2, MP4, CCSD(T)) coupled to a dielectric continuum using a self consistent reaction field (SCRF) cycle. An investigation of the behavior of ΔGhyd(H+) as the number of explicit waters of hydration is increased reveals convergence by n=4. The converged value is −262.23 kcal/mol and is independent of the ab initio method used. These results indicate that the first hydration shell of the proton is composed of at least 4 water molecules. The result strongly suggests that the proton hydration free energy is at the far lower end of the range of values obtained from the literature. The methodology is then used to calculate the hydration free energies of other ions relative to that of the proton. These include cationic forms of the alkali earth elements Li, Na, and K, and anionic forms of the halogens F, Cl, and Br. The relative ion hydration free energy is defined as Δ[ΔGhyd(Z±)]=G(Z±[H2O]n(aq))−G(H+[H2O]n(aq))−G(Z±(gas))−G(H+(gas))), where the solvated ions are represented by ion-water clusters coupled to a dielectric continuum using a self-consistent reaction field (SCRF) cycle. An investigation of the beh
This paper provides a deeper insight into the synthesis mechanism of VHDL tools. It examines three methods of writing VHDL code, and each of the three models finite state machines in a different way. There can be sign...
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This paper provides a deeper insight into the synthesis mechanism of VHDL tools. It examines three methods of writing VHDL code, and each of the three models finite state machines in a different way. There can be significant reductions in the VLSI area and improvements in performance by adopting a certain modeling style, but this is at the cost of writing low level VHDL code, thereby undermining the purpose of VHDL as the design, entry medium. However, there is a simpler approach, which is demonstrated by a software tool called vtvt which allows writing VHDL code at high level and optimizes for area and performance without the burden of writing and maintaining low level code.
An effective parallelization of finite volume computations for heat transfer application using unstructured triangular meshes and mesh partitioning algorithms are presented. The mesh partitioning software (METIS) is e...
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An effective parallelization of finite volume computations for heat transfer application using unstructured triangular meshes and mesh partitioning algorithms are presented. The mesh partitioning software (METIS) is employed to create nearly load balanced subdomains and to minimize the interprocessor communications. Efficient data structures are developed to handle the neighboring element information at the interfaces of all subdomains and a simple strategy to overlap the computations to communications has been implemented for improving the performance of the program. The explicit time integration method is used and the results for the rectangular domain and L-shaped domain have been presented. The code (PHEAT2D) is written in Fortran90 and MPI for message passing is used. The algorithm is tested on distributed memory MIMD machine, PARAM OPENFRAME.
Global operation and system control are important issues in massively parallel systems. The paper discusses virtual control networks (VCNs), which are substitutes for the current dedicated control networks. First we i...
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Global operation and system control are important issues in massively parallel systems. The paper discusses virtual control networks (VCNs), which are substitutes for the current dedicated control networks. First we introduce a new mechanism called Virtual Control Channel (VCC) used to conduct control information over data network links. The network nodes have control finite state machines (CFSMs) and a VCN is composed of CFSMs. The VCC performs the role of a connection wire between CFSMs. The mechanisms are applied to the RWC-1 machine. The simulation results reveal that reduction and broadcasting operations are efficiently executed on VCNs by exploiting the tree structure.
We present new local-memory multiprocessor algorithms for solving sparse triangular systems of equations that arise in the context of Cholesky factorization. Unlike in the existing algorithms, we use the notion of the...
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We present new local-memory multiprocessor algorithms for solving sparse triangular systems of equations that arise in the context of Cholesky factorization. Unlike in the existing algorithms, we use the notion of the elimination tree and achieve significant improvement in the performance of both the forward and backward substitution phases. Our algorithms also incorporate the generalization of an important technique of Li and Coleman that gave rise to the best performance for dense triangular system solution.
作者:
GINSBERG, MHigh-End Computing Section
EDS Advanced Computing Center General Motors Research and Development Center 30500 Mound Road P.O. Box 9055 Warren MI 48090-9055 USA
Supercomputing and visualization facilities are used in the automotive industry to produce large-scale computer simulations of physical phenomena related to the efficient design, analysis, and manufacture of cars and ...
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Supercomputing and visualization facilities are used in the automotive industry to produce large-scale computer simulations of physical phenomena related to the efficient design, analysis, and manufacture of cars and trucks. Such tools are an economic necessity for creating very high-quality vehicles that can be rapidly brought from concept to production in an increasingly competitive worldwide automotive market. This paper focuses attention on current and future directions for supercomputing and scientific visualization in an automotive applications environment. Extensive references to automotive simulation work on vector/parallel computers at General Motors are given in the Appendix. The interactions of supercomputers and visualization tools are discussed along with potential bottlenecks which must be overcome.
作者:
SCHEININE, ALParallel Computing Group
Center for Advanced Studies Research and Development in Sardinia via Nazario Sauro 10 I-09123 Cagliari Italy
An overview is given of parallel computing work being done at CRS4 (Centro di Ricerca, Sviluppo e Studi Superiori in Sardegna). Parallel computation projects include: parallelization of a simulation of the interaction...
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An overview is given of parallel computing work being done at CRS4 (Centro di Ricerca, Sviluppo e Studi Superiori in Sardegna). Parallel computation projects include: parallelization of a simulation of the interaction of high energy particles with matter (GEANT), domain decomposition for numerical solution of partial differential equations, seismic migration for oil prospecting, finite-element structural analysis, parallel molecular dynamics, a C++ library for distributed processing of specific functions, and real-time visualization of a computer simulation that runs as distributed processes.
A software package that allows one to carry out multiple alignment of protein and nucleic acid sequences of almost unlimited length and number of sequences is developed on C-DAC parallel computer-a transputer-based ma...
The parallel implementation of the revised simplex algorithm (RSA) using eta-factorization holds the promise of significant improvement in the execution time by virtue of the existence of a high degree of parallelism ...
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The parallel implementation of the revised simplex algorithm (RSA) using eta-factorization holds the promise of significant improvement in the execution time by virtue of the existence of a high degree of parallelism in the computation within an iteration of the algorithm. However, the scheme employed to partition key data structures in a distributed memory parallel processor has a great impact on the achievable performance. The paper explores the trade-offs between block-row and block-column partitioning schemes for the matrix of constraint coefficients vis-a-vis the communication overheads and granularity of parallel computations. The results of an approximate analysis of the compute-communication balance are compared with measurements from practical implementation of the partitioning schemes on C-DAC's PARAM 8000 distributed memory parallel processor.< >
The notion of an elimination tree plays a very important role in the parallel algorithms for sparse Cholesky decomposition, symbolic factorization and in determining the mapping of columns of the matrix to processors....
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The notion of an elimination tree plays a very important role in the parallel algorithms for sparse Cholesky decomposition, symbolic factorization and in determining the mapping of columns of the matrix to processors. In this paper, we present a parallel algorithm to compute the elimination tree and simultaneously carry out symbolic factorization on a local memory multiprocessor. An existing parallel algorithm for symbolic factorization [5] requires the computation of elimination tree separately. In our algorithm, we use a tree defined on the given matrix, called false elimination tree, and convert it into the actual elimination tree. In the process, we also compute the structure of the columns of the factor matrix. Using the new parallel algorithm on grid problems, we found that it performs 2 to 3 times faster compared to the total time taken for sequential computation of the elimination tree and the parallel computation of symbolic factorization using [5]. Also, our algorithm is the first parallel algorithm for elimination tree computation that gives a speed-up.
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