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This class is described in 5, Kernel Services. At the end of this process, all of the extensions .data sections are initialized, and the variant and ASSP extensions are constructed and ready for the kernel to use.

15(b) and (c) show results that are based on the complete CMU trace for 29 30 August In Fig 1515(b) the average bandwidth requirement is onequarter of the edge bandwidth In this case the stationary Weibull model predicts the blocking probability more accurarately than any of the nonstationary Poisson models Once the average bandwidth requirements of the connection are reduced to one-tenth (Fig 1515(c)), the performance of the pure Weibull model is not that impressive Still, it does as well as the nonstationary process with a time segment of one hour This, in itself, is an accomplishment considering that we compare a model that is based on one set of two parameters over a 48 hour time period to a model that is based on 48 parameters.

KModuleEntryReasonExtensionInit0 The kernel passes this reason code to all extension entry points that it calls after it has started the scheduler. This reason code is an inquiry, asking whether the extension has already been initialized. It allows extensions that use the KModuleEntryReasonVariantInit0 reason code to perform initialization, as I described earlier. If the extension is already loaded, returning any error code (other than KErrNone) will prevent the next stage from being performed. The ASSP DLL returns KErrGeneral in response to this reason code to report that it has already been initialized by the variant, as does the crash monitor, which hooks into the variant initialization phase to provide diagnostics of the kernel boot process. If the extension is not already loaded, the kernel will invoke its DLL entry point with the reason code KModuleEntryReasonExtensionInit1. KModuleEntryReasonExtensionInit1 The kernel passes this reason code to all extension entry points after it has veri ed that the extension has not already been initialized. This causes the DLL entry point to initialize global constructors before calling KernelModuleEntry to initialize the extension itself. Note that the ASSP kernel extension has already been initialized by this point so will not receive this reason code at this point in the boot process. KModuleEntryReasonProcessAttach and KModuleEntryReasonProcessDetach These reason codes are only handled by EDEV.LIB, which is speci cally intended to support dynamically loadable DLLs (device drivers). KModuleEntryReasonProcessAttach is directly equivalent to KModuleEntryReasonExtensionInit1 and is used to initialize the driver s constructors. Conversely, KModuleEntryReasonProcessDetach calls the driver s destructors. These are called when the relevant code segment is created or destroyed, as described in 10, The Loader.

Even more impressive is that a nonstationary Weibull model with segments of one hour clearly outperforms the nonstationary Poisson model with segments as small as 6 minutes..

This example is unrealistic because there is a much faster non-recursive solution for this particular problem, but it is a favorite for demonstrating both recursion and parallelism Because it does so little other computation, it makes the basic structure of fork/join designs easier to see, yet it generates many recursive calls at least fib(n) calls to compute fib(n) The first few values of the sequence are 0, 1, 1, 2, 3, 5, 8; fib(10) is 55; fib(20) is 6,765; fib(30) is 832,040; fib(40) is 102,334,155 Function seqFib can be transformed into a task class such as the following:

^ 2.3.2.2 Properties of the Estimator By construction, a is unbiased under ID1a and ID2, and if, in addition, we assume ID1b its variance is simply Var ^ a s2 w2 : j j 2:24

Kernel drivers and extensions need to ensure that read and write operations involving user process memory are performed safely. There are two scenarios to consider when servicing requests from a user process,

Note that the variance is a function of the amount of data, the nj , but is independent both of the data itself, Eq. (2.22), and of the actual (unknown) value of a. It is also independent of the precise choice of mother wavelet, except indirectly through the choice of N , the number of vanishing moments. A quantitative study of this dependence is given by Delbeke and Abry [27].

class Fib extends FJTask { static final int sequentialThreshold = 13; // for tuning volatile int number; // argument/result Fib(int n) { number = n; } int getAnswer() { if (!isDone()) throw new IllegalStateException("Not yet computed"); return number; } public void run() { int n = number;

depending on whether the request is serviced in the context of the calling thread or a kernel thread.