Execution time, optimization

Execution times for the overall ammonia plant model, of which the C02 capture system is a small part, are on the order of 30 s for the parameter estimation case, and less than a minute for an Optimize case. The model consists of over 65,000 variables, 60,000 equations, and over 300,000 nonzero Jacobian elements (partial derivates of the equation residuals with respect to variables). This problem size is moderate for RTO applications since problems over four times as large have been deployed on many occasions. Residuals are solved to quite tight tolerances, with the tolerance for the worst scaled residual set at approximately 1.0 x 10 9 or less. A scaled residual is the residual equation imbalance times its Lagrange multiplier, a measure of its importance. Tight tolerances are required to assure that all equations (residuals) are solved well, even when they involve, for instance, very small but important numbers such as electrolyte molar balances. 

The selection of an optimal spectral data evaluation algorithm is essential for satisfactory system performance, but is usually not easily predictable. Apart from the chemometrical performance, the execution time of the algorithm is crucial for realtime systems. As the execution time depends mainly on the number of mathematical operations of the algorithm, expressed by the run-time complexity, a mathematically simpler method involving fewer operations is often preferable to a (potentially) more powerful method that takes longer to calculate. 

This simple modified Amdahl s law illustrates the incentives for optimal load balancing. The case(0,3000,0) corresponds to a hypothetical situation in which there is no serial execution time and no overhead for communication. The deviation from linear speed-up in this case (about 10%) is due only to load imbalance. 

The structure in Fig. 1 is essentially a cascade design. This structure is appropriate because of the differences in the plant dynamics and disturbance frequencies associated with the decisions at each level. Control responds to rapid disturbances and requires execution periods on the order of 1 sec. Real time optimization typically responds to disturbances occurring every few hours or slower, and it requires tens of minutes to compute. The higher levels respond to disturbances occurring every few days. While the cascade... 

We can apply existing technology in solving the OPT optimization problem. Although finding maximum-entropy solutions in general is costly, the experiments described in Sarma et al. (2008) show that the execution time is reasonable for a one-time process. 

There still remain some open questions which could be mentioned partially only in this paper. One is to deal with the number of imknown parameters. The number of parameters in geochemical and biogeochemical models is often very high, so that even modem parameter estimation tools, like PEST, have problems to handle the problem. There are problems with the execution time of these models and with their convergence towards an optimal solution in the parameter space ... 

Each algorithm has its own influential parameters that affect its performance in terms of solution quality and computational time. In order to increase the performance of the FA and GA, it is necessary to provide the adjustments of the parameters depending on the problem domain. With the appropriate choice of the algorithm settings the accuracy of the decisions and the execution time can be optimized. Parameters of the FA and GA are tuned on the basis of a large number of pre-tests according to the parameter identification problem, considered here. 

In a previous work [19] we showed through empirical tests that there are optimal configurations regarding these two variables. In order to find these configurations we can apply an optimization technique, such as the Nelder-Mead method [20] to approximate the optimal values of the two parameters jumpahead distance and workahead size given the objective function is the performance of the application. The performance we measure in terms of average execution time between two samplings. 

All the arrays listed in table 2 are scheduled with the time-minimal schedule hence their execution time 5n — 2 is optimal A natural question arises what is the minimal number of processors that a time-minimal systolic solution... 

Both linear and piece-wise linear mappings can be derived in a systematic way, and methods exist to optimize given criteria within a constraint on execution time (for latency-limited applications), the period (for throughput-limited processing), or the number of processors (for area-bound applications). 

Quantum-chemical calculation of molecules o-allyloksistyrene, p-ally-loksistyrene, trans-izosafrol by method MNDO was executed for the first time. Optimized geometric and electronic structures of these compound was received. Acid power of molecules o-allyloksistyrene, p-allyloksisty-rene, trans-izosafrol was theoretically evaluated (pKa=32). These compound pertain to class of very weak H-acids (pKa>14). 

If configuration decision variables n are made at execution time, then the selection variables can assume values either 1 or 0. In this case, it is suggested that design time evaluation of the impact of execution time decisions should be performed by means of robust optimization. 

RISC Reduced instruction set computer (RISC) is a type of microprocessor architecture that utilizes a small, highly-optimized set of instructions in one cycle execution time. So, RISC processors have clock per cycle instruction. A few characteristic features shall include but are not limited to ... 

Optimizer The middle part of a compiler, it rewrites the program in an attempt to improve its runtime behavior. Optimizers usually consist of several distinct passes of analysis and transformation. The usual goal of an optimizer is to decrease the program s execution time some optimizers try to create smaller programs as well. 

The second row was obtained with the FDLS algorithm to obtain the shortest execution time for different allocations. For the 17 and 21 c-step allocations the results were already optimal so the time could not be reduced. However, for the 19 c-step allocation (2 adders and 2 multiplies), the FDLS algorithm produced a schedule requiring one c-step less. This is also an ( timal result with respect to functional unit cost. CPU times were significantly faste than those for the FDS algorithm and varied between one and two minutes. 

The operations have to be mapped onto components in the final circuit. Components are adders or ALUs etc. During the allocation task the number and the types of components are selected which determines the maximum degree of parallelism. Because scheduling optimizes the execution time on the basis of given lesources, the allocation controls the areaAime trade-off between sequential or parallel designs. 

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