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• Is it possible to shrink rt.jar with ProGuard?

  - by PatlaDJ

  Is there a procedure by which you can optimize/shrink/select/obfuscate only 'used by your app' classes/methods/fields from rt.jar provided by Sun by using some optimization software like ProGuard (or maybe other?). Then you would actually be able to minimize the download size of your application considerably and make it much more secure ? Right? Related questions: Do you know if Sun's "jigsaw project" which is waited to come out, is intended to automatically handle this particular issue? Did somebody manage yet to form an opinion about Avian java alternative? Please share it here. Thank you.

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• Compile-time trigonometry in C

  - by lhahne

  I currently have code that looks like while (very_long_loop) { ... y1 = getSomeValue(); ... x1 = y1*cos(PI/2); x2 = y2*cos(SOME_CONSTANT); ... outputValues(x1, x2, ...); } the obvious optimization would be to compute the cosines ahead-of-time. I could do this by filling an array with the values but I was wondering would it be possible to make the compiler compute these at compile-time?

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• In C, would !~b ever be faster than b == 0xff ?

  - by James Morris

  From a long time ago I have a memory which has stuck with me that says comparisons against zero are faster than any other value (ahem Z80). In some C code I'm writing I want to skip values which have all their bits set. Currently the type of these values is char but may change. I have two different alternatives to perform the test: if (!~b) /* skip */ and if (b == 0xff) /* skip */ Apart from the latter making the assumption that b is an 8bit char whereas the former does not, would the former ever be faster due to the old compare to zero optimization trick, or are the CPUs of today way beyond this kind of thing?

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• How do you design a database to allow fast multicolumn searching?

  - by Fletcher Moore

  I am creating a real estate search from RETS data, but this is a general question. When you have a variety of columns that you would like the user to be able to filter their search result by, how do you optimize this? For example, http://www.charlestonrealestateguide.com/listings.php has 16 or so optional filters. Granted, he only has up to 11,000 entries (I have the same data), but I don't imagine the search is performed with just a giant WHERE AND AND AND ... clause. Or is this typically accomplished with one giant multicolumn index? Newegg, Amazon, and countless others also have cool & fast filtering systems for large amounts of data. How do they do it? And is there a database optimization reason for the tendency to provide ranges instead of empty inputs, or is that merely for user convenience?

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• Optimizing MySQL statement with lot of count(row) an sum(row+row2)...

  - by Zombies

  I need to use InnoDB storage engine on a table with about 1mil or so records in it at any given time. It has records being inserted to it at a very fast rate, which are then dropped within a few days, maybe a week. The ping table has about a million rows, whereas the website table only about 10,000. My statement is this: select url from website ws, ping pi where ws.idproxy = pi.idproxy and pi.entrytime > curdate() - 3 and contentping+tcpping is not null group by url having sum(contentping+tcpping)/(count(*)-count(errortype)) < 500 and count(*) > 3 and count(errortype)/count(*) < .15 order by sum(contentping+tcpping)/(count(*)-count(errortype)) asc; I added an index on entrytime, yet no dice. Can anyone throw me a bone as to what I should consider to look into for basic optimization of this query. The result set is only like 200 rows, so I'm not getting killed there.

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• strange results with /fp:fast

  - by martinus

  We have some code that looks like this: inline int calc_something(double x) { if (x > 0.0) { // do something return 1; } else { // do something else return 0; } } Unfortunately, when using the flag /fp:fast, we get calc_something(0)==1 so we are clearly taking the wrong code path. This only happens when we use the method at multiple points in our code with different parameters, so I think there is some fishy optimization going on here from the compiler (Microsoft Visual Studio 2008, SP1). Also, the above problem goes away when we change the interface to inline int calc_something(const double& x) { But I have no idea why this fixes the strange behaviour. Can anyone explane this behaviour? If I cannot understand what's going on we will have to remove the /fp:fastswitch, but this would make our application quite a bit slower.

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• Django: Set foreign key using integer?

  - by User

  Is there a way to set foreign key relationship using the integer id of a model? This would be for optimization purposes. For example, suppose I have an Employee model: class Employee(models.Model): first_name = models.CharField(max_length=100) last_name = models.CharField(max_length=100) type = models.ForeignKey('EmployeeType') and EmployeeType(models.Model): type = models.CharField(max_length=100) I want the flexibility of having unlimited employee types, but in the deployed application there will likely be only a single type so I'm wondering if there is a way to hardcode the id and set the relationship this way. This way I can avoid a db call to get the EmployeeType object first.

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• Should I make my MutexLock volatile?

  - by sje397

  I have some code in a function that goes something like this: void foo() { { // scope the locker MutexLocker locker(&mutex); // do some stuff.. } bar(); } The function call bar() also locks the mutex. I am having an issue whereby the program crashes (for someone else, who has not as yet provided a stack trace or more details) unless the mutex lock inside bar is disabled. Is it possible that some optimization is messing around with the way I have scoped the locker instance, and if so, would making it volatile fix it? Is that a bad idea? Thanks.

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• How to optimize dynamic programming?

  - by Chan

  Problem A number is called lucky if the sum of its digits, as well as the sum of the squares of its digits is a prime number. How many numbers between A and B are lucky? Input: The first line contains the number of test cases T. Each of the next T lines contains two integers, A and B. Output: Output T lines, one for each case containing the required answer for the corresponding case. Constraints: 1 <= T <= 10000 1 <= A <= B <= 10^18 Sample Input: 2 1 20 120 130 Sample Output: 4 1 Explanation: For the first case, the lucky numbers are 11, 12, 14, 16. For the second case, the only lucky number is 120. The problem is quite simple if we use brute force, however the running time is so critical that my program failed most test cases. My current idea is to use dynamic programming by storing the previous sum in a temporary array, so for example: sum_digits(10) = 1 -> sum_digits(11) = sum_digits(10) + 1 The same idea is applied for sum square but with counter equals to odd numbers. Unfortunately, it still failed 9 of 10 test cases which makes me think there must be a better way to solve it. Any idea would be greatly appreciated. #include <iostream> #include <vector> #include <string> #include <algorithm> #include <unordered_map> #include <unordered_set> #include <cmath> #include <cassert> #include <bitset> using namespace std; bool prime_table[1540] = { 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0 }; unsigned num_digits(long long i) { return i > 0 ? (long) log10 ((double) i) + 1 : 1; } void get_sum_and_sum_square_digits(long long n, int& sum, int& sum_square) { sum = 0; sum_square = 0; int digit; while (n) { digit = n % 10; sum += digit; sum_square += digit * digit; n /= 10; } } void init_digits(long long n, long long previous_sum[], const int size = 18) { int current_no_digits = num_digits(n); int digit; for (int i = 0; i < current_no_digits; ++i) { digit = n % 10; previous_sum[i] = digit; n /= 10; } for (int i = current_no_digits; i <= size; ++i) { previous_sum[i] = 0; } } void display_previous(long long previous[]) { for (int i = 0; i < 18; ++i) { cout << previous[i] << ","; } } int count_lucky_number(long long A, long long B) { long long n = A; long long end = B; int sum = 0; int sum_square = 0; int lucky_counter = 0; get_sum_and_sum_square_digits(n, sum, sum_square); long long sum_counter = sum; long long sum_square_counter = sum_square; if (prime_table[sum_counter] && prime_table[sum_square_counter]) { lucky_counter++; } long long previous_sum[19] = {1}; init_digits(n, previous_sum); while (n < end) { n++; if (n % 100000000000000000 == 0) { previous_sum[17]++; sum_counter = previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[16] = 0; previous_sum[15] = 0; previous_sum[14] = 0; previous_sum[13] = 0; previous_sum[12] = 0; previous_sum[11] = 0; previous_sum[10] = 0; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 10000000000000000 == 0) { previous_sum[16]++; sum_counter = previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[15] = 0; previous_sum[14] = 0; previous_sum[13] = 0; previous_sum[12] = 0; previous_sum[11] = 0; previous_sum[10] = 0; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 1000000000000000 == 0) { previous_sum[15]++; sum_counter = previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[14] = 0; previous_sum[13] = 0; previous_sum[12] = 0; previous_sum[11] = 0; previous_sum[10] = 0; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 100000000000000 == 0) { previous_sum[14]++; sum_counter = previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[13] = 0; previous_sum[12] = 0; previous_sum[11] = 0; previous_sum[10] = 0; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 10000000000000 == 0) { previous_sum[13]++; sum_counter = previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[12] = 0; previous_sum[11] = 0; previous_sum[10] = 0; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 1000000000000 == 0) { previous_sum[12]++; sum_counter = previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[11] = 0; previous_sum[10] = 0; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 100000000000 == 0) { previous_sum[11]++; sum_counter = previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[10] = 0; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 10000000000 == 0) { previous_sum[10]++; sum_counter = previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 1000000000 == 0) { previous_sum[9]++; sum_counter = previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 100000000 == 0) { previous_sum[8]++; sum_counter = previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 10000000 == 0) { previous_sum[7]++; sum_counter = previous_sum[7] + previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[7] * previous_sum[7] + previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 1000000 == 0) { previous_sum[6]++; sum_counter = previous_sum[6] + previous_sum[7] + previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[6] * previous_sum[6] + previous_sum[7] * previous_sum[7] + previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 100000 == 0) { previous_sum[5]++; sum_counter = previous_sum[5] + previous_sum[6] + previous_sum[7] + previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[5] * previous_sum[5] + previous_sum[6] * previous_sum[6] + previous_sum[7] * previous_sum[7] + previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 10000 == 0) { previous_sum[4]++; sum_counter = previous_sum[4] + previous_sum[5] + previous_sum[6] + previous_sum[7] + previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[4] * previous_sum[4] + previous_sum[5] * previous_sum[5] + previous_sum[6] * previous_sum[6] + previous_sum[7] * previous_sum[7] + previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 1000 == 0) { previous_sum[3]++; sum_counter = previous_sum[3] + previous_sum[4] + previous_sum[5] + previous_sum[6] + previous_sum[7] + previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[3] * previous_sum[3] + previous_sum[4] * previous_sum[4] + previous_sum[5] * previous_sum[5] + previous_sum[6] * previous_sum[6] + previous_sum[7] * previous_sum[7] + previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 100 == 0) { previous_sum[2]++; sum_counter = previous_sum[2] + previous_sum[3] + previous_sum[4] + previous_sum[5] + previous_sum[6] + previous_sum[7] + previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[2] * previous_sum[2] + previous_sum[3] * previous_sum[3] + previous_sum[4] * previous_sum[4] + previous_sum[5] * previous_sum[5] + previous_sum[6] * previous_sum[6] + previous_sum[7] * previous_sum[7] + previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 10 == 0) { previous_sum[1]++; sum_counter = previous_sum[1] + previous_sum[2] + previous_sum[3] + previous_sum[4] + previous_sum[5] + previous_sum[6] + previous_sum[7] + previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[1] * previous_sum[1] + previous_sum[2] * previous_sum[2] + previous_sum[3] * previous_sum[3] + previous_sum[4] * previous_sum[4] + previous_sum[5] * previous_sum[5] + previous_sum[6] * previous_sum[6] + previous_sum[7] * previous_sum[7] + previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[0] = 0; } else { sum_counter++; sum_square_counter += ((n - 1) % 10) * 2 + 1; } // get_sum_and_sum_square_digits(n, sum, sum_square); // assert(sum == sum_counter && sum_square == sum_square_counter); if (prime_table[sum_counter] && prime_table[sum_square_counter]) { lucky_counter++; } } return lucky_counter; } void inout_lucky_numbers() { int n; cin >> n; long long a; long long b; while (n--) { cin >> a >> b; cout << count_lucky_number(a, b) << endl; } } int main() { inout_lucky_numbers(); return 0; }

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• PHP speed optimisation.

  - by Petah

  Hi, Im wondering about speed optimization in PHP. I have a series of files that are requested every page load. On average there are 20 files. Each file must be read an parsed if they have changed. And this is excluding that standard files required for a web page (HTML, CSS, images, etc). EG - client requests page - server outputs html, css, images - server outputs dynamic content (20+/- files combined and minified). What would be the best way to serve these files as fast as possible?

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• How to check for C++ copy ellision

  - by Steve

  I ran across this article on copy ellision in C++ and I've seen comments about it in the boost library. This is appealing, as I prefer my functions to look like verylargereturntype DoSomething(...) rather than void DoSomething(..., verylargereturntype& retval) So, I have two questions about this Google has virtually no documentation on this at all, how real is this? How can I check that this optimization is actually occuring? I assume it involves looking at the assembly, but lets just say that isn't my strong suit. If anyone can give a very basic example as to what successful ellision looks like, that would be very useful I won't be using copy ellision just to prettify things, but if I can be guaranteed that it works, it sounds pretty useful.

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• In Python, is there a way to call a method on every item of an iterable? [closed]

  - by Thane Brimhall

  Possible Duplicate: Is there a map without result in python? I often come to a situation in my programs when I want to quickly/efficiently call an in-place method on each of the items contained by an iterable. (Quickly meaning the overhead of a for loop is unacceptable). A good example would be a list of sprites when I want to call draw() on each of the Sprite objects. I know I can do something like this: [sprite.draw() for sprite in sprite_list] But I feel like the list comprehension is misused since I'm not using the returned list. The same goes for the map function. Stone me for premature optimization, but I also don't want the overhead of the return value. What I want to know is if there's a method in Python that lets me do what I just explained, perhaps like the hypothetical function I suggest below: do_all(sprite_list, draw)

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• SQL: Optimize insensive SELECTs on DateTime fields

  - by Fedyashev Nikita

  I have an application for scheduling certain events. And all these events must be reviewed after each scheduled time. So basically we have 3 tables: items(id, name) scheduled_items(id, item_id, execute_at - datetime) - item_id column has an index option. reviewed_items(id, item_id, created_at - datetime) - item_id column has an index option. So core function of the application is "give me any items(which are not yet reviewed) for the actual moment". How can I optimize this solution for speed(because it is very core business feature and not micro optimization)? I suppose that adding index to the datetime fields doesn't make any sense because the cardinality or uniqueness on that fields are very high and index won't give any(?) speed-up. Is it correct? What would you recommend? Should I try no-SQL? -- mysql -V 5.075 I use caching where it makes sence.

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• Genetic algorithms

  - by daniels

  I'm trying to implement a genetic algorithm that will calculate the minimum of the Rastrigin functon and I'm having some issues. I need to represent the chromosome as a binary string and as the Rastrigin's function takes a list of numbers as a parameter, how can decode the chromosome to a list of numbers? Also the Rastrigin's wants the elements in the list to be -5.12<=x(i)<=5.12 what happens if when i generate the chromosome it will produce number not in that interval? I'm new to this so help and explanation that will aid me in understanding will be highly appreciated. Thanks.

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• Java: ArrayList bottleneck

  - by Jack

  Hello, while profiling a java application that calculates hierarchical clustering of thousands of elements I realized that ArrayList.get occupies like half of the CPU needed in the clusterization part of the execution. The algorithm searches the two more similar elements (so it is O(n*(n+1)/2) ), here's the pseudo code: int currentMax = 0.0f for (int i = 0 to n) for (int j = i to n) get content i-th and j-th if their similarity > currentMax update currentMax merge the two clusters So effectively there are a lot of ArrayList.get involved. Is there a faster way? I though that since ArrayList should be a linear array of references it should be the quickest way and maybe I can't do anything since there are simple too many gets.. but maybe I'm wrong. I don't think using a HashMap could work since I need to get them all on every iteration and map.values() should be backed by an ArrayList anyway.. Otherwise should I try other collection libraries that are more optimized? Like google's one, or apache one.. Thanks

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• Optimizing a large iteration of PHP objects (EAV-based)

  - by Aron Rotteveel

  I am currently working on a project that utilizes the EAV model. This turns out to work quite well, but like many others I am now stumbling upon some performance issues. The data set in this particular case consists of aproximately 2500 entities, each with aprox. 150 attributes. Each entity and each attribute is represented by a PHP-object. Since most parts of the application only iterate through a filtered set of entities, we have not had very large issues yet. Now, however, I am working on an algorithm that requires iteration over the entire dataset, which causes a major impact on performance. This information is perhaps not very much to work with, but since this is an architectural problem, I am hoping for a architectural pattern to help me on the way as well. Each entity, including it's attributes takes up aprox. 500KB of memory.

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• Fastest way to list all primes below N in python

  - by jbochi

  This is the best algorithm I could come up with after struggling with a couple of Project Euler's questions. def get_primes(n): numbers = set(range(n, 1, -1)) primes = [] while numbers: p = numbers.pop() primes.append(p) numbers.difference_update(set(range(p*2, n+1, p))) return primes >>> timeit.Timer(stmt='get_primes.get_primes(1000000)', setup='import get_primes').timeit(1) 1.1499958793645562 Can it be made even faster? EDIT: This code has a flaw: Since numbers is an unordered set, there is no guarantee that numbers.pop() will remove the lowest number from the set. Nevertheless, it works (at least for me) for some input numbers: >>> sum(get_primes(2000000)) 142913828922L #That's the correct sum of all numbers below 2 million >>> 529 in get_primes(1000) False >>> 529 in get_primes(530) True EDIT: The rank so far (pure python, no external sources, all primes below 1 million): Sundaram's Sieve implementation by myself: 327ms Daniel's Sieve: 435ms Alex's recipe from Cookbok: 710ms EDIT: ~unutbu is leading the race.

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• Will these optimizations to my Ruby implementation of diff improve performance in a Rails app?

  - by grg-n-sox

  <tl;dr> In source version control diff patch generation, would it be worth it to use the optimizations listed at the very bottom of this writing (see <optimizations>) in my Ruby implementation of diff for making diff patches? </tl;dr> <introduction> I am programming something I have never done before and there might already be tools out there to do the exact thing I am programming but at this point I am having too much fun to care so I am still going to do it from scratch, even if there is a tool for this. So anyways, I am working on a Ruby on Rails app and need a certain feature. Basically I want each entry in a table of mine, let's say for example a table of video games, to have a stored chunk of text that represents a review or something of the sort for that table entry. However, I want this text to be both editable by any registered user and also keep track of different submissions in a version control system. The simplest solution I could think of is just implement a solution that keeps track of the text body and the diff patch history of different versions of the text body as objects in Ruby and then serialize it, preferably in human readable form (so I'll most likely use YAML for this) for editing if needed due to corruption by a software bug or a mistake is made by an admin doing some version editing. So at first I just tried to dive in head first into this feature to find that the problem of generating a diff patch is more difficult that I thought to do efficiently. So I did some research and came across some ideas. Some I have implemented already and some I have not. However, it all pretty much revolves around the longest common subsequence problem, as you would already know if you have already done anything with diff or diff-like features, and optimization the function that solves it. Currently I have it so it truncates the compared versions of the text body from the beginning and end until non-matching lines are found. Then it solves the problem using a comparison matrix, but instead of incrementing the value stored in a cell when it finds a matching line like in most longest common subsequence algorithms I have seen examples of, I increment when I have a non-matching line so as to calculate edit distance instead of longest common subsequence. Although as far as I can tell between the two approaches, they are essentially two sides of the same coin so either could be used to derive an answer. It then back-traces through the comparison matrix and notes when there was an incrementation and in which adjacent cell (West, Northwest, or North) to determine that line's diff entry and assumes all other lines to be unchanged. Normally I would leave it at that, but since this is going into a Rails environment and not just some stand-alone Ruby script, I started getting worried about needing to optimize at least enough so if a spammer that somehow knew how I implemented the version control system and knew my worst case scenario entry still wouldn't be able to hit the server that bad. After some searching and reading of research papers and articles through the internet, I've come across several that seem decent but all seem to have pros and cons and I am having a hard time deciding how well in this situation that the pros and cons balance out. So are the ones listed here worth it? I have listed them with known pros and cons. </introduction> <optimizations> Chop the compared sequences into multiple chucks of subsequences by splitting where lines are unchanged, and then truncating each section of unchanged lines at the beginning and end of each section. Then solve the edit distance of each subsequence. Pro: Changes the time increase as the changed area gets bigger from a quadratic increase to something more similar to a linear increase. Con: Figuring out where to split already seems like you have to solve edit distance except now you don't care how it is changed. Would be fine if this was solvable by a process closer to solving hamming distance but a single insertion would throw this off. Use a cryptographic hash function to both convert all sequence elements into integers and ensure uniqueness. Then solve the edit distance comparing the hash integers instead of the sequence elements themselves. Pro: The operation of comparing two integers is faster than the operation of comparing two strings, so a slight performance gain is received after every comparison, which can be a lot overall. Con: Using a cryptographic hash function takes time to convert all the sequence elements and may end up costing more time to do the conversion that you gain back from the integer comparisons. You could use the built in hash function for a string but that will not guarantee uniqueness. Use lazy evaluation to only calculate the three center-most diagonals of the comparison matrix and then only calculate additional diagonals as needed. And then also use this approach to possibly remove the need on some comparisons to compare all three adjacent cells as desribed here. Pro: Can turn an algorithm that always takes O(n * m) time and make it so only worst case scenario is that time, best case becomes practically linear, and average case is somewhere between the two. Con: It is an algorithm I've only seen implemented in functional programming languages and I am having a difficult time comprehending how to convert this into Ruby based on how it is described at the site linked to above. Make a C module and do the hard work at the native level in C and just make a Ruby wrapper for it so Ruby can make all the calls to it that it needs. Pro: I have to imagine that evaluating something like this in could be a LOT faster. Con: I have no idea how Rails handles apps with ruby code that has C extensions and it hurts the portability of the app. This is an optimization for after the solving of edit distance, but idea is to store additional combined diffs with the ones produced by each version to make a delta-tree data structure with the most recently made diff as the root node of the tree so getting to any version takes worst case time of O(log n) instead of O(n). Pro: Would make going back to an old version a lot faster. Con: It would mean every new commit, the delta-tree would get a new root node that will cost time to reorganize the delta-tree for an operation that will be carried out a lot more often than going back a version, not to mention the unlikelihood it will be an old version. </optimizations> So are these things worth the effort?

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• Google Web Optimizer -- How long until winning combination?

  - by Django Reinhardt

  I've had an A/B Test running in Google Web Optimizer for six weeks now, and there's still no end in sight. Google is still saying: "We have not gathered enough data yet to show any significant results. When we collect more data we should be able to show you a winning combination." Is there any way of telling how close Google is to making up its mind? (Does anyone know what algorithm does it use to decide if there's been any "high confidence winners"?) According to the Google help documentation: Sometimes we simply need more data to be able to reach a level of high confidence. A tested combination typically needs around 200 conversions for us to judge its performance with certainty. But all of our conversions have over 200 conversations at the moment: 230 / 4061 (Original) 223 / 3937 (Variation 1) 205 / 3984 (Variation 2) 205 / 4007 (Variation 3) How much longer is it going to have to run?? Thanks for any help.

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• Merging and splitting overlapping rectangles to produce non-overlapping ones

  - by uj

  I am looking for an algorithm as follows: Given a set of possibly overlapping rectangles (All of which are "not rotated", can be uniformly represented as (left,top,right,bottom) tuplets, etc...), it returns a minimal set of (non-rotated) non-overlapping rectangles, that occupy the same area. It seems simple enough at first glance, but prooves to be tricky (at least to be done efficiently). Are there some known methods for this/ideas/pointers? Methods for not necessarily minimal, but heuristicly small, sets, are interesting as well, so are methods that produce any valid output set at all.

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• Overhead of serving pages - JSPs vs. PHP vs. ASPXs vs. C

  - by John Shedletsky

  I am interested in writing my own internet ad server. I want to serve billions of impressions with as little hardware possible. Which server-side technologies are best suited for this task? I am asking about the relative overhead of serving my ad pages as either pages rendered by PHP, or Java, or .net, or coding Http responses directly in C and writing some multi-socket IO monster to serve requests (I assume this one wins, but if my assumption is wrong, that would actually be most interesting). Obviously all the most efficient optimizations are done at the algorithm level, but I figure there has got to be some speed differences at the end of the day that makes one method of serving ads better than another. How much overhead does something like apache or IIS introduce? There's got to be a ton of extra junk in there I don't need. At some point I guess this is more a question of which platform/language combo is best suited - please excuse the in-adroitly posed question, hopefully you understand what I am trying to get at.

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• How can i optimize this c# code?

  - by Pandiya Chendur

  I have converted my Datatable to json string use the following method... public string GetJSONString(DataTable Dt) { string[] StrDc = new string[Dt.Columns.Count]; string HeadStr = string.Empty; for (int i = 0; i < Dt.Columns.Count; i++) { StrDc[i] = Dt.Columns[i].Caption; HeadStr += "\"" + StrDc[i] + "\" : \"" + StrDc[i] + i.ToString() + "¾" + "\","; } HeadStr = HeadStr.Substring(0, HeadStr.Length - 1); StringBuilder Sb = new StringBuilder(); Sb.Append("{\"" + Dt.TableName + "\" : ["); for (int i = 0; i < Dt.Rows.Count; i++) { string TempStr = HeadStr; Sb.Append("{"); for (int j = 0; j < Dt.Columns.Count; j++) { if (Dt.Rows[i][j].ToString().Contains("'") == true) { Dt.Rows[i][j] = Dt.Rows[i][j].ToString().Replace("'", ""); } TempStr = TempStr.Replace(Dt.Columns[j] + j.ToString() + "¾", Dt.Rows[i][j].ToString()); } Sb.Append(TempStr + "},"); } Sb = new StringBuilder(Sb.ToString().Substring(0, Sb.ToString().Length - 1)); Sb.Append("]}"); return Sb.ToString(); } Is this fair enough or still there is margin for optimization to make it execute faster.... Any suggestion...

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• Draw Rectangle with XNA

  - by mazzzzz

  Hey guys, I was working on game, and wanted to highlight a spot on the screen when something happens, I created a class to do this for me, and found a bit of code to draw the rectangle static private Texture2D CreateRectangle(int width, int height, Color colori) { Texture2D rectangleTexture = new Texture2D(game.GraphicsDevice, width, height, 1, TextureUsage.None, SurfaceFormat.Color);// create the rectangle texture, ,but it will have no color! lets fix that Color[] color = new Color[width * height];//set the color to the amount of pixels in the textures for (int i = 0; i < color.Length; i++)//loop through all the colors setting them to whatever values we want { color[i] = colori; } rectangleTexture.SetData(color);//set the color data on the texture return rectangleTexture;//return the texture } Problem is that the code above is called every update, (60 times a second), and it was not written with optimization in mind, I have no clue how else to write a code to do this though. It needs to be extremely fast (the code above freezes the game, which has only skeleton code right now).. Any suggestions. Note: Any new code would be great (WireFrame/Fill are both fine). I would like to be able to specify color. Something to point in the right direction would be great, Thanks, Max

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• How to implement Excel Solver functionality in C#?

  - by Vic

  Hi, I have an application in C#, I need to do some optimization calculations, like Excel Solver Add-in does, one option is certainly to write my own solver implementation, but I'm kind of short of time, so I'm looking into libraries that already exist that can help me with this. I've been trying the Microsoft Solver Foundation, which seems pretty neat and cool, the problem is that it doesn't seem to work with the kind of calculations that I need to do. At the end of this question I'm adding the information about the calculations I need to perform and optimize. So basically my question is if any of you know of any other library that I can use for this purpose, or any tutorial that can help to do my own solver, or any idea that gives me a lead to solve this issue. Thanks. Additional Info: This is the data I need to calculate: I have 7 variables, lets call them var1, var2,...,var7 The constraints for these variables are: All of them need to be 0 <= varn <= 0.5 (where n is the number of the variable) The sum of all the variables should be equal to 1 The objective is to maximize the target formula, which in Excel looks like this: (MMULT(TRANSPOSE(L26:L32),M14:M20)) / (SQRT(MMULT(MMULT(TRANSPOSE(L26:L32),M4:S10),L26:L32))) The range that you see in this formula, L26:L32, is actually the range with the variables from above, var1, var2,..., varn. M14:M20 and M4:S10 are ranges with data that I get from different sources, there are more likely decimal values. As I said before, I was using Microsoft Solver Foundation, I modeled pretty much everything with it, I created functions that handle the operations of the target formula, but when I tried to solve the model it always fail, I think it is because of the complexity of the operations. In any case, I just wanted to show these data so you can have an idea about the kind of calculations that I need to implement.

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• PHP Performance Metrics

  - by bigstylee

  I am currently developing a PHP MVC Framework for a personal project. While I am developing the framework I am interested to see any notable performance by implementing different techniques for optimization. I have implemented a crude BenchMark class that logs mircotime. The problem is I have no frame of reference for execution times. I am very near the beginnig of this project with a database connection and a few queries but no output (bar some debugging text and BenchMark log). I have a current execution time of 0.01917 seconds. I was expecting this to be lower but as I said before I have no frame of reference. I appreciate there are many variables to take into account when juding performance but I am hoping to find some sort of metric to a) techniques to measure performance for example requests per second and b) compare results for example; how a "moderately" sized PHP application on a "standard" webserver will perform. I appreciate "moderately" and "standard" are very subjective words so perhaps a table of known execution times for a particular application (eg StackOverFlow's executing time). What are other techniques of measuring performance are there other than execution time? When looking at MVC Framework Performance Comparisom it talks about Requests Per Second (RPS). How is this calculated? I am guessing with my current execution time of 0.01917 seconds can handle 52 RPS (= 1 / 0.01917 ). This seems to be significantly lower than that quoted on the graph especially when you consider my current limited funcitonality.

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