Efficiency processor

High-Efficiency Processor with Carbon Monoxide Cleanup ... 

Any large-scale resin-handling system has three basic subsystems, for unloading, storage, and transfer. For a complete system to work at peak efficiency, processors need to write specifications that fiilly account for the unique requirements of each subsystem. The least efficient component, no matter how inconsequential it may seem, will limit the overall efficiency of the entire system. 

The procedure is computationally efficient. For example, for the catalytic subunit of the mammalian cAMP-dependent protein kinase and its inhibitor, with 370 residues and 131 titratable groups, an entire calculation requires 10 hours on an SGI 02 workstation with a 175 MHz MIPS RIOOOO processor. The bulk of the computer time is spent on the FDPB calculations. The speed of the procedure is important, because it makes it possible to collect results on many systems and with many different sets of parameters in a reasonable amount of time. Thus, improvements to the method can be made based on a broad sampling of systems. 

The Fourier sum, involving the three dimensional FFT, does not currently run efficiently on more than perhaps eight processors in a network-of-workstations environment. On a more tightly coupled machine such as the Cray T3D/T3E, we obtain reasonable efficiency on 16 processors, as shown in Fig. 5. Our initial production implementation was targeted for a small workstation cluster, so we only parallelized the real-space part, relegating the Fourier component to serial evaluation on the master processor. By Amdahl s principle, the 16% of the work attributable to the serially computed Fourier sum limits our potential speedup on 8 processors to 6.25, a number we are able to approach quite closely. 

The complexity analysis shows that the load is evenly balanced among processors and therefore we should expect speedup close to P and efficiency close to 100%. There are however few extra terms in the expression of the time complexity (first order terms in TV), that exist because of the need to compute the next available row in the force matrix. These row allocations can be computed ahead of time and this overhead can be minimized. This is done in the next algorithm. Note that, the communication complexity is the worst case for all interconnection topologies, since simple broadcast and gather on distributed memory parallel systems are assumed. 

Table 1 describes the timing results (in seconds) for a system of 4000 atoms on 4, 8 and 16 nodes. The average CPU seconds for 10 time steps per processor is calculated. In the case of the force-stripped row and force-row interleaving algorithms the CPU time is reduced by half each time the number of processors is doubled. This indicates a perfect speedup and efficiency as described in Table 2. Tables 3, refibm table3 and 5 describe the timing results, speedups and efficiencies for larger systems. In particular. Table 4 shows the scaling in the CPU time with increase in the system size. These results are very close to predicted theoretical results.

Table 2. Speedup and Efficiency results for a system of 4000 atoms on 4, 8 and 16 processors...

The family of short curves in Fig. 29-45 shows the power efficiency of conventional refrigeration systems. The curves for the latter are taken from the Engineering Data Book, Gas Processors Suppliers Association, Tulsa, Oklahoma. The data refer to the evaporator temperature as the point at which refrigeration is removed. If the refrigeration is used to cool a stream over a temperature interval, the efficiency is obviously somewhat less. The short curves in Fig. 29-45 are for several refrigeration-temperature intervals. A comparison of these curves with the expander curve shows that the refrigeration power requirement by expansion compares favorably with mechanical refrigeration below 360° R (—100° F). The expander efficiency is favored by lower temperature at which heat is to be removed. 

Heat rejection is only one aspect of thermal management. Thermal integration is vital for optimizing fuel cell system efficiency, cost, volume and weight. Other critical tasks, depending on the fuel cell, are water recovery (from fuel cell stack to fuel processor) and freeze-thaw management. 

These absorb moisture, which then has to be carefully removed before the plastics can be fabricated into acceptable products (2,3). Low concentrations, as specified by the plastic supplier, can be achieved through efficient drying systems and properly handling the dried plastic prior to and during molding, extrusion, etc. (Figs. 7-24 and 7-25). When desired processor can have these hygroscopic plastics properly dried and shipped in sealed containers. 

Assume that we have a program we will run on np processors and that this program has a serial portion and a parallel portion. For example, the serial portion of the code might read in input and calculate certain global parameters. It does not make any difference if this work is done on one processor and the results distributed, or if each processor performs identical tasks independently this is essentially serial work. Then the time t it takes the program to run in serial on one processor is the sum of the time spent in the serial portion of the code and the time spent in the parallel portion (i.e., the portion of the code that can be parallelized) is t = tg + tp. Amdahl s law defines a parallel efficiency, Pe, of the code as the ratio of total wall clock time to run on one processor to the total wall clock time to run on np processors. We give a formulation of Amdahl s law due to Meijer [42] ... 

Proper load balance is a major consideration for efficient parallel computation. Consider a job distributed over two processors (0 and 1) in such a way that wall clock time is reduced considerably. Nevertheless, it still may be that processor 0 has more work to perform so that processor 1 spends much time waiting for processor 0 to finish up a particular task. It is easy to see that, in this case, the scaling will, in general, not be linear because processor 1 is not performing an equal share of the work. 

---