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v1 = 0 g + 09v1 + 01v2 v2 = 0 g + 08v2 + 01v3 + 005v4 + 005v5 v3 = 0 g + 07v3 + 01v4 + 02v5 v4 = 5 g + v1 v5 = 10 g + v6 v6 = 0 g + v1 v6 = 0 The solution of these linear equations is given by g(R (3) ) = 04338, v1 (R (3) ) = 04338, v2 (R (3) ) = 47717, v3 (R (3) ) = 65982, v4 (R (3) ) = 50000, v5 (R (3) ) = 95662, v6 (R (3) ) = 0 Step 2 (policy improvement) The test quantity Ti (a, R (3) ) has the values T2 (0, R (3) ) = 47717, T2 (1, R (3) ) = 7, T3 (0, R (3) ) = 65987, T3 (1, R (3) ) = 70000, T4 (0, R (3) ) = 68493, T4(1) (1, R (3) ) = 50000 This yields the new policy R (4) = (0, 0, 0, 1, 2, 2) Step 3 (convergence test) The new policy R (4 is identical to the previous policy R (3) and is thus average cost optimal The minimal average cost is 04338 per day Remark 642 Deterministic state transitions For the case of deterministic state transitions the computational burden of policy iteration can be reduced considerably Instead of solving a system of linear equations at each step, the average cost and relative values can be obtained from recursive calculations The reason for this is that under each stationary policy the process moves cyclically among the recurrent states The simpli ed policy-iteration calculations for deterministic state transitions are as follows: (a) Determine for the current policy R the cycle of recurrent states among which the process cyclically moves (b) The cost rate g(R) equals the sum of one-step costs in the cycle divided by the number of states in the cycle (c) The relative values for the recurrent states are calculated recursively, in reverse direction to the natural ow around the cycle, after assigning a value 0 to one recurrent state.

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Using Barcode maker for NET Control to generate, create Denso QR ar Code image in Visual Studio NET applications Scanner In Visual Studio NET Using Barcode decoder for If your device is short of memory, you might decide to forego time performance in order to se Thumb code, which is typically about 70% of the size of ARM code Conversely, if your device is short of processing power, you might decide that the 20% faster execution of ARM code is a better compromise for your application Depending on what your code does, ARM might be much faster than Thumb, particularly if you need multiword arithmetic or other things that are improved by instructions that are only available in ARM state Your choice of optimizations will also depend on which processor you expect your code to run on.

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For example, there s little need to unroll loops if your code will only run on processors with branch prediction such as ARM1136JF-S and XScale In general, you have to target your code at the slowest processor it will run on If you are aiming for the installed base of EKA2-based Symbian OS devices, these will all be ARMv5 or higher, which means that they have Harvard architecture caches,1 at least 5-stage pipelines and they support the Thumb instruction set If your performance is limited by the amount of code that can t in the instruction cache, you may nd that your performance is actually improved by compiling to a Thumb target However, this is rare, especially on ARMv5 platforms where every process switch potentially eliminates useful content from the data cache.

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Performance on these devices is generally limited by the high rate of data cache misses due to these ushes The memory systems on mobile phone platforms are usually quite slow, and so, to alleviate the performance problems this creates, ARM processors have caches The cache is a special area of memory that is used to keep a local copy of words that are also used in main memoryRelated: .

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GICS follows the outline of Algorithm 20, Section 65 Naturally, the chromosome encodes the processor allocation, which is an indirect representation (Section 652) Hence, the construction of the schedule corresponding to each chromosome, which is necessary for the evaluation of its tness, requires the application of a heuristic As already mentioned, this is performed by a list scheduling heuristic under the involvement contention model (see later discussion) Strictly speaking, this means that a two-phase heuristic is applied multiple times in GICS once for each new chromosome To educe the running time of the evaluation, the node order is determined only once at the beginning of the algorithm, namely, according to their bottom levels Like most GAs, GICS starts with a random initial population, enhanced with a chromosome representing a sequential schedule (all nodes are allocated to a single processor) The pool is completed by one allocation extracted from a schedule produced with a list scheduling heuristic (Section 841) Most other components are fairly standard In fact, the algorithm can even be employed for scheduling under the classic or the contention model, using a modi ed evaluation (ie, scheduling heuristic) Phase 2 List Scheduling From Section 52 it is known that the second phase can be performed with a simple list scheduling algorithm The processor choice of list scheduling is simply a lookup from the given mapping A established in the rst phase Even though list scheduling under the involvement contention model was already studied in Section 841, it must be revisited as there are three alternatives for the scheduling of the edges when the processor allocation is already given (Section 832) Scheduling of the Edges The rst alternative corresponds to direct scheduling, and the procedures for the scheduling of a node and the calculation of the DRT are presented in Algorithms 23 and 24 The scheduling of an edge on the source processor must be modi ed (line 16 in Algorithm 23), since it is no longer necessary to assume the worst case Lines 1 5 (removing local edges) and line 9 (correcting the nish time) can be dropped completely, since the provisional scheduling of the edges is not necessary with the given processor allocation The other two alternatives are only distinguished from this procedure through the place where the edges are scheduled on the links and the destination processor So, for the second alternative, where the edge is scheduled on the links as a leaving edge, lines 8 and 10 of Algorithm 23 move to the for loop on lines 15 17 In the third alternative, the edge is also scheduled on the destination processor within this loop, and the for loop for the entering edges (lines 6 13) is completely dropped Of course, in the determination of the DRT the entering edges do not need to be tentatively scheduled, as the processor allocation is already given Complexity The complexity of list scheduling with a given processor allocation decreases slightly in comparison to list scheduling with start/ nish time minimization It is not necessary to tentatively schedule a node and its edges on every processor Thus,.

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