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Table 17.1 Results of Monte Carlo integration of cos2 Mean of 100 Points Trial No. 1 2 3 4 5 6 7 8 9 10 Average Hit or Miss 0.50 0.51 0.54 0.44 0.42 0.50 0.47 0.52 0.38 0.51 0.479 Sample Mean 0.459510 0.576580 0.546674 0.476346 0.482850 0.481759 0.492535 0.501243 0.488337 0.527823 0.503366

5

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the second parameter contain Essentially, it is the value of the third parameter passed to RtlRealInsertElementWorker plus 18 bytes (hex). When you looked earlier at the parameters that RtlRealInsertElementWorker takes, you had no idea what the third parameter was, but the number 0x18 sounds somehow familiar. Remember how RtlLocateNodeGenericTable added 0x18 (24 in decimal) to the pointer of the current element before it passed it to the TABLE_COMPARE_ELEMENTS callback I suspected that adding 24 bytes was a way of skipping the element s header and getting to the actual data. This corroborates that assumption it looks like elements in a generic table are each stored with 24-byte headers that are followed by the element s data. Let s dig further into this function to try and figure out how it works and what the callback does. Here s what happens after the callback returns.

Results are more accurate from the Sample-Mean approach than from the Hit-or-Miss method. Rubinstein (1981) proves this result in the general case. The technique is easy to use and does not require skill in evaluating de nite integrals. This example is intended only to illustrate the concepts of Monte Carlo techniques. Evaluating a de nite integral of one variable or, equivalently, nding the area of a plane gure is not a good application for the method. To integrate cos2 numerically, one could select a series of equally spaced values of , calculate cos2 at each point, average the results, and multiply by . This is simply repeated use of the trapezoidal rule. If each value of is in the center of one of n equal intervals, the exact result of 0.5 is recovered using any value of n greater than 1. If the values of are at the ends of n equal intervals (so there are n + 1 points), the results converge rapidly to the correct value so that the results using 100 points are as good as or better than those from 1000 points of Monte Carlo simulation. Monte Carlo methods are best suited for cases in which (a) there are many independent variables or (b) the function to be integrated is strongly non-linear so that it is dif cult to determine how to divide the region of integration ef ciently.

7C924E10 7C924E12 7C924E14 7C924E1A 7C924E1E 7C924E22 7C924E24 7C924E27 7C924E2A 7C924E2D 7C924E2F 7C924E32 7C924E34 7C924E37 7C924E3A 7C924E3E 7C924E40 7C924E44 7C924E47 7C924E4D 7C924E50 MOV EBX,EAX TEST EBX,EBX JE ntdll.7C94D4BE AND DWORD PTR [EBX+4],0 AND DWORD PTR [EBX+8],0 MOV DWORD PTR [EBX],EBX LEA ECX,DWORD PTR [ESI+4] MOV EDX,DWORD PTR [ECX+4] LEA EAX,DWORD PTR [EBX+C] MOV DWORD PTR [EAX],ECX MOV DWORD PTR [EAX+4],EDX MOV DWORD PTR [EDX],EAX MOV DWORD PTR [ECX+4],EAX INC DWORD PTR [ESI+14] CMP DWORD PTR [EBP+1C],0 JE SHORT ntdll.7C924E88 CMP DWORD PTR [EBP+1C],2 MOV EAX,DWORD PTR [EBP+18] JE ntdll.7C924F0C MOV DWORD PTR [EAX+8],EBX MOV DWORD PTR [EBX],EAX

This code tests the return value from the callback. If it s zero, the function jumps into a remote block. Let s take a quick look at that block.

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