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  • Parallelizing a serial algorithm

    - by user643813
    Hej folks, I am working on porting a Text mining/Natural language application from single-core to a Map-Reduce style system. One of the steps involves a while loop similar to this: Queue<Element>; while (!queue.empty()) { Element e = queue.next(); Set<Element> result = calculateResultSet(e); if (!result.empty()) { queue.addAll(result); } } Each iteration depends on the result of the one before (kind of). There is no way of determining the number of iterations this loop will have to perform. Is there a way of parallelizing a serial algorithm such as this one? I am trying to think of a feedback mechanism, that is able to provide its own input, but how would one go about parallelizing it? Thanks for any help/remarks

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  • Can a Perl subroutine return data but keep processing?

    - by Perl QuestionAsker
    Is there any way to have a subroutine send data back while still processing? For instance (this example used simply to illustrate) - a subroutine reads a file. While it is reading through the file, if some condition is met, then "return" that line and keep processing. I know there are those that will answer - why would you want to do that? and why don't you just ...?, but I really would like to know if this is possible. Thank you so much in advance.

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  • Simple C++ container class that is thread-safe for writing

    - by conradlee
    I am writing a multi-threaded program using OpenMP in C++. At one point my program forks into many threads, each of which need to add "jobs" to some container that keeps track of all added jobs. Each job can just be a pointer to some object. Basically, I just need the add pointers to some container from several threads at the same time. Is there a simple solution that performs well? After some googling, I found that STL containers are not thread-safe. Some stackoverflow threads address this question, but none that forms a consensus on a simple solution.

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  • What did they program this toy with?

    - by Trix
    A rather strange question: I'm often asking myself with what programming languages things were created. I recently found this toy mini computer I played with when I was 13 or so at home. (Note: It is not one of those toy "notebooks", it's really small and came as an extra with a magazine) "Features": Hadware: LCD with a small field of pixels where the games were going on, besides that some stats such as score, highscore etc. Sounds and horrible music when started A really small "keyboard" with a wire Software: At least 14 or so games, from Snake over Tetris and Breakdown to some abomination of a car racing game A calculator Game selecting menu An alarm clock Inside there is a really small circuit board, I don't want to open the thing up now, though. Can you imagine if the games and "Operating System" of this thing where actually programmed using a language? If yes, what language could it be? If not with a programming language, how else was it created?

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  • How Do Sockets Work in C?

    - by kaybenleroll
    I am a bit confused about socket programming in C. You create a socket, bind it to an interface and an IP address and get it to listen. I found a couple of web resources on that, and understood it fine. In particular, I found an article Network programming under Unix systems to be very informative. What confuses me is the timing of data arriving on the socket. How can you tell when packets arrive, and how big the packet is, do you have to do all the heavy lifting yourself? My basic assumption here is that packets can be of variable length, so once binary data starts appearing down the socket, how do you begin to construct packets from that?

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  • What is it like working as a computer programmer

    - by Luke101
    I have a day job as an IT system administrator, but I do a lot of c# asp.net programming on my spare time. I have always wondered what its like to be a real software developer. I have taken a look at big CMS systems like umbraco and Dotnetnuke and said to myself that these developers must have decades of programming experience. Just the design of these products are overwhelming let alone the actual code. I just would like your comments on what it is like being a programmer.

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  • Why are Asynchronous processes not called Synchronous?

    - by Balk
    So I'm a little confused by this terminology. Everyone refers to "Asynchronous" computing as running different processes on seperate threads, which gives the illusion that these processes are running at the same time. This is not the definition of the word asynchronous. a·syn·chro·nous –adjective 1. not occurring at the same time. 2. (of a computer or other electrical machine) having each operation started only after the preceding operation is completed. What am I not understanding here?

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  • Can I use MPI_Probe to probe messsages sent by any collective operation?

    - by takwing
    In my code I have a server process repeatedly probing for incoming messages, which come in two types. One type of the two will be sent once by each process to give hint to the server process about its termination. I was wondering if it is valid to use MPI_Broadcast to broadcast these termination messages and use MPI_Probe to probe their arrivals. I tried using this combination but it failed. This failure might have been caused by some other things. So I would like anyone who knows about this to confirm. Cheers.

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  • [C++] Needed: A simple C++ container (stack, linked list) that is thread-safe for writing

    - by conradlee
    I am writing a multi-threaded program using OpenMP in C++. At one point my program forks into many threads, each of which need to add "jobs" to some container that keeps track of all added jobs. Each job can just be a pointer to some object. Basically, I just need the add pointers to some container from several threads at the same time. Is there a simple solution that performs well? After some googling, I found that STL containers are not thread-safe. Some stackoverflow threads address this question, but none form a consensus on a simple solution.

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  • Which language should I learn to create a sudoku game?

    - by Brandan
    I'd like to learn a new programming language, something besides all the scripting languages I've used for the past many years (Ruby, JavaScript, Perl, PHP, bash). I figured it might be interesting to make a sudoku game since there are plenty of documented algorithms and it only requires fairly simple data structures. It might start out as either a generator or a solver of puzzles, not necessarily both and not necessarily with a GUI. My goal is primarily to learn some new programming concepts beyond MVC and UI design, secondarily for this thing to be pretty fast. Is there a language that particularly shines for these sorts of constraint satisfaction problems? Is it suited to a functional language like Haskell or a highly concurrent language like Erlang (say for solving puzzles much larger than 9 x 9)? Or is this question mostly meaningless?

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  • Take advantage of multiple cores executing SQL statements

    - by willvv
    I have a small application that reads XML files and inserts the information on a SQL DB. There are ~ 300 000 files to import, each one with ~ 1000 records. I started the application on 20% of the files and it has been running for 18 hours now, I hope I can improve this time for the rest of the files. I'm not using a multi-thread approach, but since the computer I'm running the process on has 4 cores I was thinking on doing it to get some improvement on the performance (although I guess the main problem is the I/O and not only the processing). I was thinking on using the BeginExecutingNonQuery() method on the SqlCommand object I create for each insertion, but I don't know if I should limit the max amount of simultaneous threads (nor I know how to do it). What's your advice to get the best CPU utilization? Thanks

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  • how to cancel an awaiting task c#

    - by user1748906
    I am trying to cancel a task awaiting for network IO using CancellationTokenSource, but I have to wait until TcpClient connects: try { while (true) { token.Token.ThrowIfCancellationRequested(); Thread.Sleep(int.MaxValue); //simulating a TcpListener waiting for request } } any ideas ? Secondly, is it OK to start each client in a separate task ?

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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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  • Parallelism in .NET – Part 5, Partitioning of Work

    - by Reed
    When parallelizing any routine, we start by decomposing the problem.  Once the problem is understood, we need to break our work into separate tasks, so each task can be run on a different processing element.  This process is called partitioning. Partitioning our tasks is a challenging feat.  There are opposing forces at work here: too many partitions adds overhead, too few partitions leaves processors idle.  Trying to work the perfect balance between the two extremes is the goal for which we should aim.  Luckily, the Task Parallel Library automatically handles much of this process.  However, there are situations where the default partitioning may not be appropriate, and knowledge of our routines may allow us to guide the framework to making better decisions. First off, I’d like to say that this is a more advanced topic.  It is perfectly acceptable to use the parallel constructs in the framework without considering the partitioning taking place.  The default behavior in the Task Parallel Library is very well-behaved, even for unusual work loads, and should rarely be adjusted.  I have found few situations where the default partitioning behavior in the TPL is not as good or better than my own hand-written partitioning routines, and recommend using the defaults unless there is a strong, measured, and profiled reason to avoid using them.  However, understanding partitioning, and how the TPL partitions your data, helps in understanding the proper usage of the TPL. I indirectly mentioned partitioning while discussing aggregation.  Typically, our systems will have a limited number of Processing Elements (PE), which is the terminology used for hardware capable of processing a stream of instructions.  For example, in a standard Intel i7 system, there are four processor cores, each of which has two potential hardware threads due to Hyperthreading.  This gives us a total of 8 PEs – theoretically, we can have up to eight operations occurring concurrently within our system. In order to fully exploit this power, we need to partition our work into Tasks.  A task is a simple set of instructions that can be run on a PE.  Ideally, we want to have at least one task per PE in the system, since fewer tasks means that some of our processing power will be sitting idle.  A naive implementation would be to just take our data, and partition it with one element in our collection being treated as one task.  When we loop through our collection in parallel, using this approach, we’d just process one item at a time, then reuse that thread to process the next, etc.  There’s a flaw in this approach, however.  It will tend to be slower than necessary, often slower than processing the data serially. The problem is that there is overhead associated with each task.  When we take a simple foreach loop body and implement it using the TPL, we add overhead.  First, we change the body from a simple statement to a delegate, which must be invoked.  In order to invoke the delegate on a separate thread, the delegate gets added to the ThreadPool’s current work queue, and the ThreadPool must pull this off the queue, assign it to a free thread, then execute it.  If our collection had one million elements, the overhead of trying to spawn one million tasks would destroy our performance. The answer, here, is to partition our collection into groups, and have each group of elements treated as a single task.  By adding a partitioning step, we can break our total work into small enough tasks to keep our processors busy, but large enough tasks to avoid overburdening the ThreadPool.  There are two clear, opposing goals here: Always try to keep each processor working, but also try to keep the individual partitions as large as possible. When using Parallel.For, the partitioning is always handled automatically.  At first, partitioning here seems simple.  A naive implementation would merely split the total element count up by the number of PEs in the system, and assign a chunk of data to each processor.  Many hand-written partitioning schemes work in this exactly manner.  This perfectly balanced, static partitioning scheme works very well if the amount of work is constant for each element.  However, this is rarely the case.  Often, the length of time required to process an element grows as we progress through the collection, especially if we’re doing numerical computations.  In this case, the first PEs will finish early, and sit idle waiting on the last chunks to finish.  Sometimes, work can decrease as we progress, since previous computations may be used to speed up later computations.  In this situation, the first chunks will be working far longer than the last chunks.  In order to balance the workload, many implementations create many small chunks, and reuse threads.  This adds overhead, but does provide better load balancing, which in turn improves performance. The Task Parallel Library handles this more elaborately.  Chunks are determined at runtime, and start small.  They grow slowly over time, getting larger and larger.  This tends to lead to a near optimum load balancing, even in odd cases such as increasing or decreasing workloads.  Parallel.ForEach is a bit more complicated, however. When working with a generic IEnumerable<T>, the number of items required for processing is not known in advance, and must be discovered at runtime.  In addition, since we don’t have direct access to each element, the scheduler must enumerate the collection to process it.  Since IEnumerable<T> is not thread safe, it must lock on elements as it enumerates, create temporary collections for each chunk to process, and schedule this out.  By default, it uses a partitioning method similar to the one described above.  We can see this directly by looking at the Visual Partitioning sample shipped by the Task Parallel Library team, and available as part of the Samples for Parallel Programming.  When we run the sample, with four cores and the default, Load Balancing partitioning scheme, we see this: The colored bands represent each processing core.  You can see that, when we started (at the top), we begin with very small bands of color.  As the routine progresses through the Parallel.ForEach, the chunks get larger and larger (seen by larger and larger stripes). Most of the time, this is fantastic behavior, and most likely will out perform any custom written partitioning.  However, if your routine is not scaling well, it may be due to a failure in the default partitioning to handle your specific case.  With prior knowledge about your work, it may be possible to partition data more meaningfully than the default Partitioner. There is the option to use an overload of Parallel.ForEach which takes a Partitioner<T> instance.  The Partitioner<T> class is an abstract class which allows for both static and dynamic partitioning.  By overriding Partitioner<T>.SupportsDynamicPartitions, you can specify whether a dynamic approach is available.  If not, your custom Partitioner<T> subclass would override GetPartitions(int), which returns a list of IEnumerator<T> instances.  These are then used by the Parallel class to split work up amongst processors.  When dynamic partitioning is available, GetDynamicPartitions() is used, which returns an IEnumerable<T> for each partition.  If you do decide to implement your own Partitioner<T>, keep in mind the goals and tradeoffs of different partitioning strategies, and design appropriately. The Samples for Parallel Programming project includes a ChunkPartitioner class in the ParallelExtensionsExtras project.  This provides example code for implementing your own, custom allocation strategies, including a static allocator of a given chunk size.  Although implementing your own Partitioner<T> is possible, as I mentioned above, this is rarely required or useful in practice.  The default behavior of the TPL is very good, often better than any hand written partitioning strategy.

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  • Which programming idiom to choose for this open source library?

    - by Walkman
    I have an interesting question about which programming idiom is easier to use for beginner developers writing concrete file parsing classes. I'm developing an open source library, which one of the main functionality is to parse plain text files and get structured information from them. All of the files contains the same kind of information, but can be in different formats like XML, plain text (each of them is structured differently), etc. There are a common set of information pieces which is the same in all (e.g. player names, table names, some id numbers) There are formats which are very similar to each other, so it's possible to define a common Base class for them to facilitate concrete format parser implementations. So I can clearly define base classes like SplittablePlainTextFormat, XMLFormat, SeparateSummaryFormat, etc. Each of them hints the kind of structure they aim to parse. All of the concrete classes should have the same information pieces, no matter what. To be useful at all, this library needs to define at least 30-40 of these parsers. A couple of them are more important than others (obviously the more popular formats). Now my question is, which is the best programming idiom to choose to facilitate the development of these concrete classes? Let me explain: I think imperative programming is easy to follow even for beginners, because the flow is fixed, the statements just come one after another. Right now, I have this: class SplittableBaseFormat: def parse(self): "Parses the body of the hand history, but first parse header if not yet parsed." if not self.header_parsed: self.parse_header() self._parse_table() self._parse_players() self._parse_button() self._parse_hero() self._parse_preflop() self._parse_street('flop') self._parse_street('turn') self._parse_street('river') self._parse_showdown() self._parse_pot() self._parse_board() self._parse_winners() self._parse_extra() self.parsed = True So the concrete parser need to define these methods in order in any way they want. Easy to follow, but takes longer to implement each individual concrete parser. So what about declarative? In this case Base classes (like SplittableFormat and XMLFormat) would do the heavy lifting based on regex and line/node number declarations in the concrete class, and concrete classes have no code at all, just line numbers and regexes, maybe other kind of rules. Like this: class SplittableFormat: def parse_table(): "Parses TABLE_REGEX and get information" # set attributes here def parse_players(): "parses PLAYER_REGEX and get information" # set attributes here class SpecificFormat1(SplittableFormat): TABLE_REGEX = re.compile('^(?P<table_name>.*) other info \d* etc') TABLE_LINE = 1 PLAYER_REGEX = re.compile('^Player \d: (?P<player_name>.*) has (.*) in chips.') PLAYER_LINE = 16 class SpecificFormat2(SplittableFormat): TABLE_REGEX = re.compile(r'^Tournament #(\d*) (?P<table_name>.*) other info2 \d* etc') TABLE_LINE = 2 PLAYER_REGEX = re.compile(r'^Seat \d: (?P<player_name>.*) has a stack of (\d*)') PLAYER_LINE = 14 So if I want to make it possible for non-developers to write these classes the way to go seems to be the declarative way, however, I'm almost certain I can't eliminate the declarations of regexes, which clearly needs (senior :D) programmers, so should I care about this at all? Do you think it matters to choose one over another or doesn't matter at all? Maybe if somebody wants to work on this project, they will, if not, no matter which idiom I choose. Can I "convert" non-programmers to help developing these? What are your observations? Other considerations: Imperative will allow any kind of work; there is a simple flow, which they can follow but inside that, they can do whatever they want. It would be harder to force a common interface with imperative because of this arbitrary implementations. Declarative will be much more rigid, which is a bad thing, because formats might change over time without any notice. Declarative will be harder for me to develop and takes longer time. Imperative is already ready to release. I hope a nice discussion will happen in this thread about programming idioms regarding which to use when, which is better for open source projects with different scenarios, which is better for wide range of developer skills. TL; DR: Parsing different file formats (plain text, XML) They contains same kind of information Target audience: non-developers, beginners Regex probably cannot be avoided 30-40 concrete parser classes needed Facilitate coding these concrete classes Which idiom is better?

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  • Where do all the old programmers go?

    - by Tony Lambert
    I know some people move over to management and some die... but where do the rest go and why? One reason people change to management is that in some companies the "Programmer" career path is very short - you can get to be a senior programmer within a few years. Leaving no way to get more money but to become a manager. In other companies project managers and programmers are parallel career paths so your project manager can be your junior. Tony

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  • Persistent (purely functional) Red-Black trees on disk performance

    - by Waneck
    I'm studying the best data structures to implement a simple open-source object temporal database, and currently I'm very fond of using Persistent Red-Black trees to do it. My main reasons for using persistent data structures is first of all to minimize the use of locks, so the database can be as parallel as possible. Also it will be easier to implement ACID transactions and even being able to abstract the database to work in parallel on a cluster of some kind. The great thing of this approach is that it makes possible implementing temporal databases almost for free. And this is something quite nice to have, specially for web and for data analysis (e.g. trends). All of this is very cool, but I'm a little suspicious about the overall performance of using a persistent data structure on disk. Even though there are some very fast disks available today, and all writes can be done asynchronously, so a response is always immediate, I don't want to build all application under a false premise, only to realize it isn't really a good way to do it. Here's my line of thought: - Since all writes are done asynchronously, and using a persistent data structure will enable not to invalidate the previous - and currently valid - structure, the write time isn't really a bottleneck. - There are some literature on structures like this that are exactly for disk usage. But it seems to me that these techniques will add more read overhead to achieve faster writes. But I think that exactly the opposite is preferable. Also many of these techniques really do end up with a multi-versioned trees, but they aren't strictly immutable, which is something very crucial to justify the persistent overhead. - I know there still will have to be some kind of locking when appending values to the database, and I also know there should be a good garbage collecting logic if not all versions are to be maintained (otherwise the file size will surely rise dramatically). Also a delta compression system could be thought about. - Of all search trees structures, I really think Red-Blacks are the most close to what I need, since they offer the least number of rotations. But there are some possible pitfalls along the way: - Asynchronous writes -could- affect applications that need the data in real time. But I don't think that is the case with web applications, most of the time. Also when real-time data is needed, another solutions could be devised, like a check-in/check-out system of specific data that will need to be worked on a more real-time manner. - Also they could lead to some commit conflicts, though I fail to think of a good example of when it could happen. Also commit conflicts can occur in normal RDBMS, if two threads are working with the same data, right? - The overhead of having an immutable interface like this will grow exponentially and everything is doomed to fail soon, so this all is a bad idea. Any thoughts? Thanks! edit: There seems to be a misunderstanding of what a persistent data structure is: http://en.wikipedia.org/wiki/Persistent_data_structure

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  • For distributed applications, which to use, ASIO vs. MPI?

    - by Rhubarb
    I am a bit confused about this. If you're building a distributed application, which in some cases may perform parallel operations (although not necessarily mathematical), should you use ASIO or something like MPI? I take it MPI is a higher level than ASIO, but it's not clear where in the stack one would begin.

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  • Interactive Data Language, IDL: Does anybody care?

    - by Alex
    Anyone use a language called Interactive Data Language, IDL? It is popular with scientists. I think it is a poor language because it is proprietary (every terminal running it has to have an expensive license purchased) and it has minimal support (try searching for IDL, the language, right now on stack) . I am trying to convince my colleagues to stop using it and learn C/C++/Python/Fortran/Java/Ruby. Does anybody know about or even care about IDL enough to have opinions on it? What do you think of it? Should I tell my colleagues to stop wasting their time on it now? How can I convince them? Edit: People are getting the impression that I don't know or use IDL. Also, I said IDL has minimal support which is true in one sense, so I must clarify that the scientific libraries are indeed large. I use IDL all the time, but this is exactly the problem: I am only using IDL because colleagues use it. There is a file format IDL uses, the .sav, which can only be opened in IDL. So I must use IDL to work with this data and transfer the data back to colleagues, but I know I would be more efficient in another language. This is like someone sending you a microsoft word file in an email attachment and if you don't understand how wrong that is then you probably write too many words not enough code and you bought microsoft word. Edit: As an alternative to IDL Python is popular. Here is a list of The Pros of IDL (and the cons) from AstroBetter: Pros of IDL Mature many numerical and astronomical libraries available Wide astronomical user base Numerical aspect well integrated with language itself Many local users with deep experience Faster for small arrays Easier installation Good, unified documentation Standard GUI run/debug tool (IDLDE) Single widget system (no angst about which to choose or learn) SAVE/RESTORE capability Use of keyword arguments as flags more convenient Cons of IDL Narrow applicability, not well suited to general programming Slower for large arrays Array functionality less powerful Table support poor Limited ability to extend using C or Fortran, such extensions hard to distribute and support Expensive, sometimes problem collaborating with others that don’t have or can’t afford licenses. Closed source (only RSI can fix bugs) Very awkward to integrate with IRAF tasks Memory management more awkward Single widget system (useless if working within another framework) Plotting: Awkward support for symbols and math text Many font systems, portability issues (v5.1 alleviates somewhat) not as flexible or as extensible plot windows not intrinsically interactive (e.g., pan & zoom) Pros of Python Very general and powerful programming language, yet easy to learn. Strong, but optional, Object Oriented programming support Very large user and developer community, very extensive and broad library base Very extensible with C, C++, or Fortran, portable distribution mechanisms available Free; non-restrictive license; Open Source Becoming the standard scripting language for astronomy Easy to use with IRAF tasks Basis of STScI application efforts More general array capabilities Faster for large arrays, better support for memory mapping Many books and on-line documentation resources available (for the language and its libraries) Better support for table structures Plotting framework (matplotlib) more extensible and general Better font support and portability (only one way to do it too) Usable within many windowing frameworks (GTK, Tk, WX, Qt…) Standard plotting functionality independent of framework used plots are embeddable within other GUIs more powerful image handling (multiple simultaneous LUTS, optional resampling/rescaling, alpha blending, etc) Support for many widget systems Strong local influence over capabilities being developed for Python Cons of Python More items to install separately Not as well accepted in astronomical community (but support clearly growing) Scientific libraries not as mature: Documentation not as complete, not as unified Not as deep in astronomical libraries and utilities Not all IDL numerical library functions have corresponding functionality in Python Some numeric constructs not quite as consistent with language (or slightly less convenient than IDL) Array indexing convention “backwards” Small array performance slower No standard GUI run/debug tool Support for many widget systems (angst regarding which to choose) Current lack of function equivalent to SAVE/RESTORE in IDL matplotlib does not yet have equivalents for all IDL 2-D plotting capability (e.g., surface plots) Use of keyword arguments used as flags less convenient Plotting: comparatively immature, still much development going on missing some plot type (e.g., surface) 3-d capability requires VTK (though matplotlib has some basic 3-d capability)

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  • Defend PHP; convince me it isn't horrible

    - by Jason L
    I made a tongue-in-cheek comment in another question thread calling PHP a terrible language and it got down-voted like crazy. Apparently there are lots of people here who love PHP. So I'm genuinely curious. What am I missing? What makes PHP a good language? Here are my reasons for disliking it: PHP has inconsistent naming of built-in and library functions. Predictable naming patterns are important in any design. PHP has inconsistent parameter ordering of built-in functions, eg array_map vs. array_filter which is annoying in the simple cases and raises all sorts of unexpected behaviour or worse. The PHP developers constantly deprecate built-in functions and lower-level functionality. A good example is when they deprecated pass-by-reference for functions. This created a nightmare for anyone doing, say, function callbacks. A lack of consideration in redesign. The above deprecation eliminated the ability to, in many cases, provide default keyword values for functions. They fixed this in PHP 5, but they deprecated the pass-by-reference in PHP 4! Poor execution of name spaces (formerly no name spaces at all). Now that name spaces exist, what do we use as the dereference character? Backslash! The character used universally for escaping, even in PHP! Overly-broad implicit type conversion leads to bugs. I have no problem with implicit conversions of, say, float to integer or back again. But PHP (last I checked) will happily attempt to magically convert an array to an integer. Poor recursion performance. Recursion is a fundamentally important tool for writing in any language; it can make complex algorithms far simpler. Poor support is inexcusable. Functions are case insensitive. I have no idea what they were thinking on this one. A programming language is a way to specify behavior to both a computer and a reader of the code without ambiguity. Case insensitivity introduces much ambiguity. PHP encourages (practically requires) a coupling of processing with presentation. Yes, you can write PHP that doesn't do so, but it's actually easier to write code in the incorrect (from a sound design perspective) manner. PHP performance is abysmal without caching. Does anyone sell a commercial caching product for PHP? Oh, look, the designers of PHP do. Worst of all, PHP convinces people that designing web applications is easy. And it does indeed make much of the effort involved much easier. But the fact is, designing a web application that is both secure and efficient is a very difficult task. By convincing so many to take up programming, PHP has taught an entire subgroup of programmers bad habits and bad design. It's given them access to capabilities that they lack the understanding to use safely. This has led to PHP's reputation as being insecure. (However, I will readily admit that PHP is no more or less secure than any other web programming language.) What is it that I'm missing about PHP? I'm seeing an organically-grown, poorly-managed mess of a language that's spawning poor programmers. So convince me otherwise!

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  • Programming Java 7 in Eclipse

    - by reprogrammer
    I installed JDK 7 and Eclipse 3.6M6. Then, I added JRE 7 as a new JRE execution environment in Eclipse, and set the compiler compliance level to Java 7. I can compile the following piece of code through command line using the javac that comes with JDK 7. import java.util.HashMap; import java.util.Map; public class Try { public static void main(String[] args) { Map<Integer, String> map = new HashMap<>(); } } But, Eclipse gives the following error messages. Incorrect number of arguments for type HashMap; it cannot be parameterized with arguments Try.java /TryJava7/src line 7 Java Problem Syntax error on token "<", ? expected after this token Try.java /TryJava7/src line 7 Java Problem Even though I've set the compliance level of the compiler to Java 7, it looks like Eclipse doesn't understand Java7 syntax yet. Is it possible to play with Java 7 in Eclipse? The following is the content of .classpath. <?xml version="1.0" encoding="UTF-8"?> <classpath> <classpathentry kind="src" path="src"/> <classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER/org.eclipse.jdt.internal.debug.ui.launcher.StandardVMType/JavaSE-1.7"/> <classpathentry kind="output" path="bin"/> </classpath> And, the following is the content of .settings/org.eclipse.jdt.core.prefs. eclipse.preferences.version=1 org.eclipse.jdt.core.compiler.codegen.inlineJsrBytecode=enabled org.eclipse.jdt.core.compiler.codegen.targetPlatform=1.7 org.eclipse.jdt.core.compiler.codegen.unusedLocal=preserve org.eclipse.jdt.core.compiler.compliance=1.7 org.eclipse.jdt.core.compiler.debug.lineNumber=generate org.eclipse.jdt.core.compiler.debug.localVariable=generate org.eclipse.jdt.core.compiler.debug.sourceFile=generate org.eclipse.jdt.core.compiler.problem.assertIdentifier=error org.eclipse.jdt.core.compiler.problem.enumIdentifier=error org.eclipse.jdt.core.compiler.source=1.7

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