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I am attempting to multithread some code with OMP. Currently my sequentially version using rand() to generate a set of random numbers with a consistent seed so that they return the same results when run each time. I want to parallelise my code but rand() is not thread safe. Can someone please show me how i would go about using a random number generator that works on threads so i can produce the same data set upon each test similar to that of using a seed with rand(). My code im parallelising is as follows:

First, the non-thread-safe nature of rand() function means that calling rand() at the same time from different threads might yield different values than if it were called sequentially. It's probably easiest to explain this with a simple example, so let's look at a 32-bit version of PCG since it's nice and simple. It has a 32-bit state, and generates 32-bit numbers like this:

Threads App Download Numbers

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Now think about what happens if two threads call generate() at roughly the same time. Maybe they both get the same value for state, and so generate the same random number twice. Maybe one updates state before the other reads it, so they get different values.

We can eliminate the problem by protecting the generate() function with a mutex or, in the case of 32-bit PGC (and this is why I use it for reproducible numbers), using atomics. If we do that then we'll always get the same numbers in the same order.

Part two of the problem is what happens when the order of the callers gets mixed up in your code. Let's say you have two threads (called A and B), and they each have to run two iterations of your loop. Even if you're getting your random numbers from a thread-safe source, the order of the calls could be AABB, ABAB, ABBA, BBAA, BABA, or BAAB, each of which would cause your code to generate a different result.

Here is a block based approach that divides the problem space in N/BLOCK_SIZE blocks and reseeds the RNG with your randInit + block number for each block. This gives a reproducible output no matter the number of threads you have. It also generates the same initial N numbers for sequence of N + x. This as long as you keep the same BLOCK_SIZE.

I intend to write a C++11 application for Linux which does some numerical simulation (not cryptography) based on approximately one million pseudorandom 32bit numbers. To speed things up, I'd like to perform the simulation in parallel threads using all cores of a desktop CPU. I'd like to use the Mersenne Twister mt19937 provided by boost as the PRNG, and I guess that for performance reasons I should have one such PRNG per thread. Now I'm unsure about how to seed them in order to avoid generating the same subsequence of random numbers in multiple threads.

As using the same algorithm to generate random numbers and to generate the initial seed feels somehow like it might be a bad idea, I thought about introducing some element which is not dependent on the Mersenne Twister algorithm. For example, I could XOR the thread id into each element of the initial seed vector. Does that make things any better?

This would have one thread generate all the required 1M random numbers up front, to be used by the different threads later on. The memory requirement of 4M would be small compared to that of the overall application. What worries me most in this approach is that the generation of random numbers itself is not concurrent. This whole approach also doesn't scale too well.

I would go with #1, seed every prng from urandom. This ensures that the states are totally independent (as far as the seed data is independent). Typically there will be plenty of entropy available unless you have many threads. Also, depending on the algorithm used for /dev/urandom you almost certainly don't need to worry about it.

I'd say #3 is the winner. Seed each thread with something like the processID or threadID; while it's technically possible you could have overlap, it's highly unlikely. Even consecutive numbers shouldn't be related in terms of seeds once you get out of the single digits (I don't know the Twister algorithm, but the worst PRNG I've seen was fine above 7). One million PRNGs isn't that many compared to the scope of most PRNG equations.

Finally, you could check fairly easily. Check the last seed generated by each thread against all numbers in each other thread. If the seed appears in the thread, then check the previous number generated in each thread; if they also match, then you have a collision and need to re-seed your streams and try again.

I am having trouble figuring out what my code is doing as this is my first time coding using multiple threads. To start off, in attempt to learn this type of programming I decided to write a miniature program that uses 8 threads to sum a number. However, no matter what I do it seems as if my program never stops when count = 10, it continues onward. I am using 8 threads as I planned on expanding my program to do large calculations. However, these threads are not correlating at all. They are going way past 10. I have used a synchronized method. I have tried a lock. I have tried implementing both at the same time. No matter what, it appears as if the threads still calculate past 10. See below for my current code.

After being the most downloaded app in the App Store and Google Play for several days and breaking download records, Threads, Meta's Twitter (now X) competitor, is now back to normal download numbers.

Threads achieved the 100 million signup mark within a week of launch. But since then, engagement numbers have dwindled. The company has shipped many sought-after features, such as post search, a web version, an edit button and easy account switching in the last few months. Meta is also working on upcoming features like trending posts and separate account deletion from Instagram for Threads.

The latest data reports from Sensor Tower show the daily active user count for the Twitter-like social media channel have dropped 82% since its breakthrough launch on July 5, CNN reports, citing data from Sensor Tower. The numbers are as of July 31.

Meta and Mark Zuckerberg have once again not reacted to the new numbers, but Zuckerberg addressed the initial drop-off two weeks ago, saying it was expected and he anticipates user numbers will stabilize in the coming months.

I am having the same problem. I am messaging my girlfriend and imessages are getting mixed up between her and her son's phones. They are sharing the same itunes account, but on imessage they only have their individual phone numbers accepting the imessages.I havent found a work around for this. Were yall able to resolve the issue?

This has been a problem for me too! My brother in law and sister have different phone numbers and both have the setting "Start New Conversations From" set to their unique phone numbers. I've opened a bug report for this weeks ago but haven't heard back from Apple yet. I'm checking with my sister to see if they share an Apple ID (for iCloud and/or iTunes). Right now I'm trying to verify if it was fixed in the latest beta build (16A5364a) released 8/23. I deleted the previous conversations and I have two separate conversations appearing in the list right now but I'm checking to see if they merge like they have in the past.

Several years ago, Groz-Beckert purchased Singer's industrial needle division. The yellow Singer needle cases of yore will soon disappear and all Groz-Beckert needles will be in the standard green plastic sleeves. The green packs have gone through several design and label iterations, most recently with the addition of a unique data matrix code printed on the bottom half. There are a few style characteristics that define important aspects of needles. Because Groz-Beckert produces thousands of types of needles (their product catalog is as thick as a phone book), it's helpful to know what the numbers printed on the label mean, so you can cross reference between the many versions of packaging in the marketplace.

The numbers and characters that will help you in selecting the right needle based on your desired sewing or quilting application is the needle system, needle point, and needle size. For longarm quilting, the most common points are the FFG and R point. Certain longarm companies swear by the FFG and others swear by the R point. We say, use what works best for you. Needles are not expensive and it's worth having a selection of needle sizes to accommodate the many threads in your thread stash. Thicker threads should be paired with a larger needle.

An AppDomain-wide Random instance is maintained in order to provide seeds for new Random instances created for any new threads that come along wanting random numbers. Each thread maintains its own Random instance in a ThreadStatic field, such that once initialized, calls to Next need only retrieve the ThreadStatic Random instance and use its Next method; no locks are necessary, and sharing is minimized. I tested this in a loop like the following:

Of course, if you really care about the quality of the random numbers, you should be using RNGCryptoServiceProvider, which generates cryptographically-strong random numbers (Addendum: davidacoder makes a good point in his comments on this post that while Random has certain statistical properties, using multiple Random instances as part of the same algorithm may change the statistical properties in unknown or undesirable ways). For a look at how to get a Random-based facade for RNGCryptoServiceProvider, see .NET Matters: Tales from the CryptoRandom in the September 2007 issue of MSDN Magazine. You could also settle on an intermediate solution, such as using an RNGCryptoServiceProvider to provide the seed values for the ThreadStatic Random instances in a solution like that in RandomGen2 (which would help to avoid another issue here, that of two threads starting with the same seed value due to accessing the global Random instance in the same time quantum):

Whenever the above scenario was encountered, I can recall that in older builds of iOS that two message threads would have been created - one for each number (in this case, Home and Work). Now, however, iOS appears to combine the conversations from both numbers into a single message thread because it recognises that despite being separate physical phone numbers, the contact is still the same person. 006ab0faaa