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With all these well-known techniques, respectable scaling is claimed (88% and 63%) for the problem size and resources used. The outstanding issue here is how well these techniques can be incorporated into programming models and tools such that they can be transparently applied to Grid applications. 21.4.2 Data-driven techniques Besides improving communication performance, the techniques in the previous section are also oriented towards providing a more loosely coupled execution of the MPI code. How can a more asynchronous, loosely coupled execution model be realized to support programming models Clearly data-driven programming techniques can facilitate this. While such models can suffer from excessive operand matching and scheduling overheads, restricted, coarse-grain forms can realize signi cant net bene ts. Work ow is an instance of this model. Another instance is stream programming. As exempli ed by the DataCutter framework [57, 58], stream programming can be used to manage the access to large data stores and associate processing with communication in a distributed manner. Data sets that are too large to easily copy or move can be accessed through upstream lters that can do spatial ltering, decimation, corner-turns, or caching copies closer to home . The coallocation of lters and streams in a Grid environment is an important issue. Stream programming can also have a wide variety of semantics and representations, as catalogued in Reference [59]. This is also closely related to the notion of advanced communication services discussed below. 21.4.3 Speculative or optimistic techniques Another method for producing a more asynchronous, loosely coupled execution is that of speculative or optimistic computing. By direct analogy with optimistic discrete-event simulation, speculative or optimistic computing is the relaxation of synchronization and communication requirements by allowing speculative execution among multiple hosts with the probability that some work optimistically computed would have to be discarded

low cost, even for very complex array antennas An important factor was also the development of digital processors that can handle the enormously increased rate of information provided by phased array systems Digital processing techniques made phased array antenna systems cost effective, that is, they provided the customers value for the money spent This being true for phased arrays in general, it also holds for conformal array antennas However, in the area of conformal arrays, electromagnetic models and design know-how needed extra development During the last 10 to 20 years, numerical techniques, electromagnetic analysis methods, and the understanding of antennas on curved surfaces have improved Important progress has been made in high-frequency techniques, including analysis of surface wave diffraction and modeling of radiating sources on curved surfaces.

A digital system frequently requires memory for storage. To facilitate this need, most FPGA devices contain dedicated embedded memory modules. While these modules cannot replace the massive external memory devices, they are useful for applications that require small or intermediate-sized memory. Although the basic internal structure of memory modules is similar, there are many subtle differences in their I/O interfaces. It is usually difficult for synthesis software to extract the desired features from the code and to infer a matching memory module from the underlying device library. In Xilinx ISE, we can use HDL instantiation, the Core Generator program, or the behavioral HDL inference template to incorporate an embedded memory module into a design. The third one is semi-device independent and we use this method in this book. In this chapter, we briefly examine Spartan-3 memory modules and the first two methods and provide detailed descriptions of several key behavioral HDL templates.

Then the average drop size from eq. (7-25) is 100 m. This is too small; we must go to larger pipe use 4 in. pipe with a diameter of 0.100 m and a velocity of 0.95 m/s: We = d32 0.100 0.952 1154 = 8596 12 10 3 0.100 = 0.49 = 213 m (8596)0.6

message contains and the actual way of coding it for the transmission Consider the following example John wants to wish his friend Mary on her birthday, so he calls her up and says Happy Birthday Mary! when she answers Assuming that it takes John two seconds to say it, and that the telephone system uses PCM coding with 64 kbps, results in John needing 128 kB for sending his message An alternative way could be for John to send an email In this case his message is coded to ASCII text and he basically needs 1 byte per letter or 160 bits to send his message (for the sake of this example we neglect to include header information and addresses etc).