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the number of computations that can be performed per unit time, and the potential for improvement when additional computational resources are available However, these are often intertwined with other performance goals For example, parallelism may also improve response latencies for a service that hands off work to a parallel execution facility Among the main challenges of parallelism in the Java programming language is to construct portable programs that can exploit multiple CPUs when they are present, while at the same time working well on single processors, as well as on time-shared multiprocessors that are often processing unrelated programs Some classic approaches to parallelism don't mesh well with these goals Approaches that assume particular architectures, topologies, processor capabilities, or other fixed environmental constraints are ill suited to commonly available JVM implementations While it is not a crime to build run-time systems with extensions specifically geared to particular parallel computers, and to write parallel programs specifically targeted to them, the associated programming techniques necessarily fall outside the scope of this book Also, RMI and other distributed frameworks can be used to obtain parallelism across remote machines In fact, most of the designs discussed here can be adapted to use serialization and remote invocation to achieve parallelism over local networks This is becoming a common and efficient means of coarse-grained parallel processing However, these mechanics also lie outside the scope of this book We instead focus on three families of task-based designs, fork/join parallelism, computation trees, and barriers These techniques can yield very efficient programs that exploit multiple CPUs when present, yet still maintain portability and sequential efficiency Empirically, they are known to scale well, at least up through dozens of CPUs Moreover, even when these kinds of task-based parallel programs are tuned to maximally exploit a given hardware platform, they require only minor retunings to maximally exploit other platforms As of this writing, probably the most common targets for these techniques are applications servers and compute servers that are often, but by no means always, multiprocessors In either case, we assume that CPU cycles are usually available, so the main goal is to exploit them to speed up the solution of computational problems In other words, these techniques are unlikely to be very helpful when programs are run on computers that are already nearly saturated.

The most basic way to offer peripheral resources to other components within the kernel (not to user-mode applications) is to develop a simple kernel DLL (by specifying a targettype of KDLL in the extension s MMP le). Kernel DLLs can provide a static interface through which other kernel components gain access to the hardware:

Although the traces where collected over time periods of varying length (from less than one day to three weeks), the Weibull distribution produces good models for the interarrival times of the various applications This is a rst indication that the appropriateness of the tted Weibull distribution is not adversely affected by the considered time interval Indeed, the observations regarding the goodness of the tted distribution hold true over almost all time periods that are long enough to allow suf cient observations It even holds if we select just one compute server over a one hour time period or aggregate over a number of single user machines within one hour time periods We start by considering the HTTP connection interarrival times of the external AT&T dataset from 18 November Figure 1511 shows the discrepancy measures l2 for time periods of different length For periods of one day (Fig 15.

11(a)), one hour (Fig 1511(b)) and even 20 minutes (Fig 1511(c)), the Weibull distribution yields the best t If we isolate speci c traf c sources within our traces (eg, a compute server at AT&T), the Weibull distribution still gives the statistically best model Figure 1512(a) depicts the arrival time series of the individual sources as well as the total arrival time series for all HTTP connections of the external AT&T dataset from 16 to 31 March over some arbitrary one hour time period Figure 1512(b) and (c) show the empirical and tted cumulative interarrival time distribution of the interarrival time distribution of two speci c sources from the same data used for Fig 1512(a).

class MyKextIf { public: IMPORT_C static TUint32 GetStatus(); IMPORT_C static void SetStatus(TUint32 aVal); }; EXPORT_C TUint MyKextIf::GetStatus() { return *(volatile TUint32 *)(KHwBaseReg); } EXPORT_C void MyKextIf::SetStatus(TUint aVal) { *(volatile TUint32 *)(KHwBaseReg) = aVal; }

Fork/join decomposition relies on parallel versions of divide-and-conquer techniques familiar in sequential algorithm design Solutions take the form:

Again the t of the Weibull distribution is far superior to all the others an observation that is underlined by the discrepancy measure for other sources and time periods The time-dependent discrepancies for the interarrival times of another application, TELNET, are shown in Fig 1513 for the 29 30 June 1995 dataset Here the rst.

Of course, a shared library DLL may offer any functionality that may be of use from other components within the kernel (not only access to peripheral resources). However, a kernel DLL is not de ned as an extension so is not initialized by the kernel at boot time, so can t make use of writeable static data. Using a kernel extension opens up the opportunity to provide a much richer interface.

Fig. 15.11 Quality of t of application-speci c (HTTP) and time-dependent connection interarrival times for the external AT&T 18 November to 8 December 1995 dataset.