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in men and 38 cm in women is strongly predictive of clinically signi cant OSA. Sleep studies (polysomnography) are diagnostic, demonstrating both the apnoeic episodes (with increased inspiratory effort against a closed pharynx), and the resulting fall in transcutaneous oxygen tension (Figure 13.5). Once the diagnosis is established, the Apnoea/Hypopnoea Index (AHI), which quanti es sleep disruption and oxygen desaturation, is useful to determine the severity of the OSA (American Academy of Sleep Medicine Task Force, 1991). Treatment of OSA requires specialist intervention. Continuous positive airway pressure (CPAP) ventilation during the night, delivered using a tightly- tting mask over the mouth and nose, keeps the upper airway open; this dramatically reduces the frequency of apnoea and oxygen desaturation, with a striking improvement in symptoms and quality of life (Stradling, 2007). Blood pressure and cardiac function also

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Operation Cost: This experiment evaluated the cost of Seine coupling operations In this experiment, 7200 particles were used in each poloidal plane resulting in an abstract domain of size 7200 8 9 between XGC and MI and 7200 6 9 between MI and MHD The number of processors at site M, which ran the XGC code, was varied and the costs of the register and put operations were measured and plotted in Figure 148 The plot shows the operation cost and the normalized operation cost, that is, the cost per-unit region in the abstract domain The gure shows that as the number of processors at site M increases, the absolute time costs of the register and put operations decrease while the normalized time costs for them increase.

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The decrease in absolute time cost is because the size of the abstract domain is xed, and as the number of processor increases, each processor registers/puts a smaller portion of this domain Since the operation costs are directly affected by the size of the region, these costs decrease accordingly The increase in the normalized time cost is due to the increasing communication overhead imposed on each unit region being sent Detailed analysis of this behavior can be found in Ref [20] Throughput Achieved: The goal of this experiment is to measure the effective system throughput that can be achieved during wide-area data coupling for different system and abstract domain sizes Herein, the effective system throughput refers to the amount of data pushed out by the entire parallel application running on site M during a unit time.

The major differences that you will notice from GNOME are that there is only one panel and that there are no named menus. That is, there are not the textual clues to alert you that menus are there. In fact, the main menu, shown in Figure 9-15, is accessed by clicking the red hat icon on the left end of the panel. You will notice as you look through the menu items that many of the applications listed are different from the applications listed in the GNOME Applications menu. These applications are those that are bundled with the release of KDE. Certain important applications, such as Firefox and Evolution, are available via these submenus, but on the whole, the Fedora developers reason that if you re using KDE, you probably want to use KDE applications as well.

Since data transfers issued from different computer nodes can overlap with each other, this effective system throughput is expected to be higher than throughput of a single node In the experiment, the number of particles per poloidal plane was varied to be 7200, 14400, and 28800, and the number of processors running XGC at site M were varied to be 8, 16, 32, 64, and 128 We measured the per-processor throughput using netperf [21] benchmark The measurement was conducted by adjusting parameters that determine the different data transfer size at the measurement The data size used in netperf measurement is set up to be the same as the data size sent out by each processor in the data redistribution experiment The per-processor throughput on site M is plotted in Figure 14.

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