Massively parallel architectures

Carpenter, G. A., and Grossberg, S., A massively parallel architecture for a self-organizing neural pattern recognition machine, Comput. Vis. Graphics Image Process 37,54 (1987b). 

The use of computational chemistry to address issues relative to process design was discussed in an article. The need for efficient software for massively parallel architectures was described. Methods to predict the electronic structure of molecules are described for the molecular orbital and density functional theory approaches. Two examples of electronic stracture calculations are given. The first shows that one can now make extremely accurate predictions of the thermochemistry of small molecules if one carefully considers all of the details such as zero-point energies, core-valence corrections, and relativistic corrections. The second example shows how more approximate computational methods, still based on high level electronic structure calculations, can be used to address a complex waste processing problem at a nuclear production facility (Dixon and Feller, 1999). 

An implementation of the configuration-selecting multireference configuration-interaction method on massively parallel architectures 95... 

Further enhancements in capabilities of component placement equipment can be realized in massively parallel architectures. The attributes of this approach are listed in Table 40.6. Multiple placement modules are capable of picking up, inspecting, and placing components at the appropriate location on the circuit board. The circuit board is stepped by an indexing conveyor to locate the placement site precisely under the overhead component. 

Such problem tailoring requires some familiarity with the algorithmic modules. It also demands knowledge of the theoretical and practical strengths and weaknesses of the different minimization methods. With rapidly growing improvements in high-performance super and massively parallel machines,15 16 application-tailored software may be even more important in combination with parallel architectures whose design is motivated by specific applications. 

Hinton, G. E. (1987). Learning translation invariant recognition in a massively parallel network. In PARLE Parallel Architecture and Languages (ed Goos, G. Hartmanis, J.), pp. 1-13. Springer-Verlag, Berlin. 

Finally, CFD for reacting flows are performed today on massively parallel machines these architectures coupled with centered schemes for turbulent flows lead to an additional t3rpe of instability linked to the growth of rounding errors and to a new t3q>e of instability where the... 

At present, we are somewhere in-between these points the barrier to utilizing GPU hardware for general purpose computation has been reduced by the introduction of general purpose GPU programming models such as NVIDIA s Compute Unified Device Architecture (CUDA) [15] and AMD s Stream [20]. However, algorithmic paradigm shifts are often required in existing codes to maximize such performance offered by the massively parallel GPU hardware. 

Perhaps the most dramatic changes in the MCSCF method, and indeed in electronic structure methods in general, will result from the new computer architectures that are becoming available to computational chemists. Not only the supercomputers with about 10 words of memory and 10 arithmetic operations per second capability, but also the massively parallel computers with hundreds or thousands of computational units operating simultaneously may come to play important roles in modern computational chemistry. 

The constant increase of computing power at a reasonable price allows now complex simulations of protein behaviour on massively parallel computing architecture. It will thus be conceivable to study the dynamic properties of supramolecular assemblies in the near future. 

M. A. Cohen and S. Grossberg, Appl. Opt., 26, 1866 (1987). Masking Fields A Massively Parallel Neural Architecture for Learning, Recognizing, and Predicting Multiple Groupings of Patterned Data. 

A number of papers have reported on CFD modeling with the commercial ANSYS/FLUENT software [13]. Several terms must be defined in conjunction with massive parallel processing prior to discussing different HPC applications. Cortex is a process that provides the user interface and graphics for FLUENT, that is, the graphic user interface (GUI) of ANSYS/FLUENT. ANSYS/FLUENT interacts with the host processor, which organizes and delegates the numerical tasks to different nodes. Each node possesses its own random-access memory (RAM), that is, 24 GB per node and 3 GB per core for the JuRoPA architecture. The host sends a request to the nodes, which store their results directly in parallel on the hard disk. 

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