Memory limits

Program RW INTS This program is designed to read the one- and two- electron AO integrals (in Dirac <12 12> convention) from use r input and put them out to disk in canonical order. There are no memory limitations associated with program RW INTS. 

The program uses dynamic memory allocation within a memory limit that must be set manually if the default is insufficient. The program does store data in scratch files, but the size of these files has been kept to a minimum. The output is neatly formatted, but designed for wide carriage printers. 

Likewise, efficient interface reconstruction algorithms and mixed cell thermodynamics routines have been developed to make three-dimensional Eulerian calculations much more affordable. In general, however, computer speed and memory limitations still prevent the analyst from doing routine three-dimensional calculations with the resolution required to be assured of numerically converged solutions. As an example. Fig. 9.29 shows the setup for a test involving the oblique impact of a copper ball on a hardened steel target... 

The pump maintenance step that was omitted was in a long sequence of task steps carried out from memory. Memory limitations would mean that there was a high probability that the step would be omitted at some stage. The work was not normally checked, so the probability of recovery was low. 

Time and memory limit parameters to limit the solution time and system memory used. 

Yet another technique has been to interview children or adults (such as teachers, Sch fer Smith, 1996) directly, either using questionnaires (Smith et al., 1992), or interviews (Boulton, 1992a). Clearly, people s answers may not be accurate in terms of what we observe, but this method should inform us about what they think and perceive. This has it s own intrinsic interest, and also interest in terms of discrepancies between beliefs and behaviour. In the case of older children, especially adolescents, we may get uniquely useful insights, while bearing in mind the possible distortions due to selective perception and memory, limited insight to motivation, and social desirability in responses, that bear on verbal report data (Boulton, 1992a). 

However, the increase in the sample size N will depend on computational time and available computer memory. In our particular case studies, we run into memory limitations when we increase the sample size N beyond 2000 samples. Table 7.3 shows the solution of the single refinery problem using the SAA scheme with N = 2000 and N = 20000. The proposed approach required 553 CPU s to converge to the optimal solution. 

The problem was solved for different sample sizes N and N to illustrate the variation of optimality gap confidence intervals, while fixing the number of replications I to 30. The replication number R need not be very large to get an insight into Vn variability. Table 9.3 shows different confidence interval values of the optimality gap when the sample size of N assumes values of 1000, 2000, and 3000 while varying N from 5000, 10 000, to 20 000 samples. The sample sizes N and N were limited to these values due to increasing computational effort. In our case study, we ran into memory limitations when N and N values exceeded 3000 and 20 000, respectively. The solution of the three refineries network and the PVC complex using the SAA scheme with N = 3000 and N = 20000 required 1114 CPU s to converge to the optimal solution. 

Program memory limits usually 640K usually > 1 GByte... 

Program memory limits (bytes) virtual, very large segmentation supported virtual, very large virtual, demand paged... 

The Ohmic model memory kernel admits an infinitely short memory limit y(t) = 2y5(t), which is obtained by taking the limit a>c —> oo in the memory kernel y(t) = yG)ce c [this amounts to the use of the dissipation model as defined by Eq. (23) for any value of ]. Note that the corresponding limit must also be taken in the Langevin force correlation function (29). In this limit, Eq. (22) reduces to the nonretarded Langevin equation ... 

For simplicity, we restrict ourselves from now on to the short-memory limit coc —> oo. Eq. (110) then simplifies to... 

Figure 3. In the short-memory limit (rac —> oo), the quantum time-dependent diffusion coefficient DP - 00 piotte(j as a function of yt for several different bath temperatures on both sides of Tc (full lines) yrth = 0.25(T — 2TC, classical regime) yt — 0.5 (T = Tc, crossover) yrth = 1 (T = Tc/2, quantum regime) yt = +oo (T = 0). The corresponding curves for the classical diffusion coefficient Dcl( ) are plotted in dotted lines in the same figure.

In the infinitely short memory limit coc —> oo, one recovers as expected for Xxx( 0 the expression (79) corresponding to the nonretarded Langevin model. 

Since the displacement response function does not depend on the bath temperature [Eq. (115) or (116)], Eq. (126) displays the above quoted property that, at any fixed time t,D(t) is a monotonic increasing function of the temperature. In the infinitely short memory limit, taking into account the corresponding expression (79) of %xx, one gets from Eq. (126)... 

Figure 5. In the short-memory limit (tDc —> oo), the ratio PeffOMw)/P, at bath temperature T = 2TC (classical regime), plotted as a function of yx for various values of ytw (full lines) ytw — 0.01 ytw — 0.1 ytw — 1 ytw — 10. The corresponding curves for the classical violation factor X(x,tw) = P f(x,fw)/P are plotted in dotted lines on the same figure.

Let us now restrict the study to the particular case of the Ohmic model in the short memory limit —> oo. Equation (130) then reads... 

To understand the physical consequences of modulation, we make the assumption of being able to generate time series with no computer time and computer memory limitation. Of course, this is an ideal condition, and in practice we shall have to deal with the numerical limits of the mathematical recipe that we adopt here to understand modulation. The reader might imagine that we have a box with infinitely many labelled balls. The label of any ball is a given number X. There are many balls with the same X, so as to fit the probability density of Eq. (281). We randomly draw the balls from the box, and after reading the label we place the ball back in the box. Of course, this procedure implies that we are working with discrete rather than continuous numbers. However, we make the assumption that it is possible to freely increase the ball number so as to come arbitrarily close to the continuous prescription of Eq. (281). 

SAX parsers also never return back to already processed data or look ahead for some value needed for current processing. The computation is therefore very fast, and the best parsers can read many megabytes of data in a few seconds, but there is a price to be paid for this speed and absence of memory limits. Programmers are required to create all necessary coding structures by themselves from information sent by SAX parsers, and this often requires a nontrivial programming effort. If ultimate efficiency is not a necessity, it is usually better to let a generic piece of software create more elaborate structures in computer memory—a data model. 

Because the FID signal decays due to spin-spin relaxation, there is a time limit beyond which further monitoring of the FID provides more noise than signal. The usual compromise is to have a short enough dwell time to cover the spectral width and a long enough acquisition time to provide the desired resolution, consistent with computer memory limitations. 

Any real understanding of the success of DOS after 1987 requires knowledge of Windows. In the early years of its existence, Microsoft s DOS gained great acceptance and became a standard as a PC operating system. Even so, as computers became more powerful and programs more complex, the limitations of the DOS command-line interface were becoming apparent (as well as the aforementioned conventional memory limitation). 

With version 3 (OS/2 Warp), IBM created a multitasking, 32-bit OS that required a 386 but preferred a 486. Warp also required a ridiculous 4MB of RAM just to load. With a graphical interface and the ability to do a great deal of self-configuration, the Warp OS was a peculiar cross between DOS and a Macintosh. Warp featured true preemptive multitasking, did not suffer from the memory limitations of DOS, and had a desktop similar to the Macintosh. 

The virtual memory settings (see Figure 14.14) tell you how much hard drive space is allocated to the system as a swap file. For a review of what virtual memory is, return to Part II, Chapter 13, Windows 95/98. Windows 2000 recommends a particular virtual memory level, but you can add to or subtract from this as you need. Often, certain applications (SQL Server for instance) will need to have Windows 2000 Professional s virtual memory limit raised in order to work properly. Graphics and CAD applications also require raising the virtual memory level, but if this is the case, the setup instructions for the application will generally tell you what modifications need to be made. 

The algorithm of Knowles and Handy is described as vectorized because each of the three major operations (105)-(107) may be written as an operation performed on an entire vector at once. This is very beneficial for vector supercomputers, which actually perform such operations a vector at a time and give substantial increases in speed. To illustrate, consider Fig. 6, which shows the Knowles-Handy algorithm for the formation of D, eq. (105). Due to memory limitations, operations are performed for a block of strings at a time. In the first half of Fig. 6, the operations in the innermost loop are identical but independent of each other for different K. In the second half of the algorithm, the same applies to Ka hence, this operation can be performed for a... 

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