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I have been preparing for the java exam in a weeks time and ran into a recursion I can't understand that is being used as an example. Below can of course be done better by not using recursion but it doesn't matter here.
public static int findMax(int[] v, int n) {
if (n==1) {
return v[0];
} else {
int temp = findMax(v, n-1);
if (v[n-1] > temp) {
return v[n-1];
} else {
return temp;
}
}
}
Where I get stuck in my understanding is that the int temp = findMax line should change temp to v[0]. After that it shouldn't return a max unless you get lucky. So for some reason the int temp = findMax line is bypassed to use the actual comparison before running again? I just don't get the logic behind the code, perhaps a printing error but I don't understand recursion well enough to know.
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On January 01 2017 19:43 Yurie wrote:I have been preparing for the java exam in a weeks time and ran into a recursion I can't understand that is being used as an example. Below can of course be done better by not using recursion but it doesn't matter here. public static int findMax(int[] v, int n) {
if (n==1) {
return v[0];
} else {
int temp = findMax(v, n-1);
if (v[n-1] > temp) {
return v[n-1];
} else {
return temp;
}
}
}
Where I get stuck in my understanding is that the int temp = findMax line should change temp to v[0]. After that it shouldn't return a max unless you get lucky. So for some reason the int temp = findMax line is bypassed to use the actual comparison before running again? I just don't get the logic behind the code, perhaps a printing error but I don't understand recursion well enough to know.
This method finds the max in the first n elements of the array v. Assume that findMax is correct for n-1 (same concept as proof by induction). The first if says: if we only have one element, obviously that is the max. Then in the else we first find the max of the first n-1 elements. As per our assumption, this returns the correct answer. So now that we know the max of the first n-1 elements, we only need to check it against the very last element, v[n-1], which wasn't looked at in findMax(v, n-1). If the very last element is bigger, we return that, else the max of the first n-1 elements.
That's the most important thing about recursion: Don't try to figure out what the recursion does in detail. Assume your method does the right thing for some different case (usually a smaller n of some sorts), then build the solution for your one current n using that assumption. All you have to do is cover the corner cases (like 0 or 1) and solve the n case using the n-1 case.
By the way, this method doesn't work too well if you call it with n < 1.
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On January 01 2017 20:01 spinesheath wrote:Show nested quote +On January 01 2017 19:43 Yurie wrote:I have been preparing for the java exam in a weeks time and ran into a recursion I can't understand that is being used as an example. Below can of course be done better by not using recursion but it doesn't matter here. public static int findMax(int[] v, int n) {
if (n==1) {
return v[0];
} else {
int temp = findMax(v, n-1);
if (v[n-1] > temp) {
return v[n-1];
} else {
return temp;
}
}
}
Where I get stuck in my understanding is that the int temp = findMax line should change temp to v[0]. After that it shouldn't return a max unless you get lucky. So for some reason the int temp = findMax line is bypassed to use the actual comparison before running again? I just don't get the logic behind the code, perhaps a printing error but I don't understand recursion well enough to know. This method finds the max in the first n elements of the array v. Assume that findMax is correct for n-1 (same concept as proof by induction). The first if says: if we only have one element, obviously that is the max. Then in the else we first find the max of the first n-1 elements. As per our assumption, this returns the correct answer. So now that we know the max of the first n-1 elements, we only need to check it against the very last element, v[n-1], which wasn't looked at in findMax(v, n-1). If the very last element is bigger, we return that, else the max of the first n-1 elements. That's the most important thing about recursion: Don't try to figure out what the recursion does in detail. Assume your method does the right thing for some different case (usually a smaller n of some sorts), then build the solution for your one current n using that assumption. All you have to do is cover the corner cases (like 0 or 1) and solve the n case using the n-1 case. By the way, this method doesn't work too well if you call it with n < 1.
The part I bolded is the part I have a problem with understanding how it does. I assume I will have to write a recursion of similar level of complexity or higher on the exam thus need to understand them so I can do similar things. (Perhaps it is only recursion through binary trees but one can never know.)
I know it would not work with n < 1. As an example it doesn't really cover everything that could go wrong as that would make for very long examples. I prefer this style instead of them adding another if with an invalidinput or similar to it. 
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On January 01 2017 20:14 Yurie wrote:
The part I bolded is the part I have a problem with understanding how it does. I assume I will have to write a recursion of similar level of complexity or higher on the exam thus need to understand them so I can do similar things. (Perhaps it is only recursion through binary trees but one can never know.)
The trick is that you don't have to understand how it does that. It's a call to your recursive method, but with a smaller n as input. You just assume it does what it promises to do.
If it helps, we can look at it from the other direction - from n=1 upwards: If n=1, you have an obvious solution. For n=2, we build our method around using the result of findMax(v, 2-1). Which we know to be correct. This makes findMax(v, 2) return the correct result. Next we determine that findMax(v, 3) is correct because findMax(v, 3-1) is correct. Continue that infinitely and you have proof by induction. Continue it until int.MaxValue and you know your recursion is correct.
But I find that the 0 to infinity approach isn't very helpful when writing recursive methods. It helps verify that the concept works. When I write recusive methods, I just assume that my method actually does what it promises to do. I don't care how it does, all that matters is that the result is correct. Then I wrap it with the scaffolding of taking care of corner cases and solving the n case by using the n-1 case.
Sometimes you have more complicated patterns, like solving both the 0 and 1 corner cases and then solving n by using n-2, or solving n by using both n-1 and n-2. Whatever it is, you firmly trust that your method does the right thing for any n smaller than the current n.
It seems like many people have trouble with this at first. I too think that it's a rather unintuitive way of thinking. But in the end it's really quite the consistent pattern. It just takes practice, I guess.
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Have you seen proof by induction in logic classes? It is generally very similar. Basically the way recursion works is:
solve for a very trivial case. In your case, that is the maximum of 1 number.
solve the "inductive step": assume the smaller case is solved (in your situation, the line temp = findmax(v, n-1) , and solve for the current step.
Proof by induction is similar, but you prove some theorem holds in the trivial case and the inductive step, rather than solving some problem.
As an example of how this works, lets work through your code sample for the array v=[1,2,3,1] and n=2.
The first iteration, n != 1, so we end up in the else step. The first thing that your code then does is call findmax with: v as above, but with n -1, so in the recursive call, n=1.
In this iteration, we see that n==1, and thus we return v[0]. This means that in the original first call, temp is assigned the value of v[0], so temp =1. Now the rest of the else clause is executed: we test whether v[n -1] > temp. So n = 2 (remember, we're back in the first call), so we test wheter v[1] > temp. v[1] = 2, and temp = 1, so yes, v[1] > temp. We therefore return the value of v[1] (which is 2) and the function returns.
Now as an exercise for yourself, run through it for n=4. What happens?
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Thank you, both of you. I think I get it now. Do for n=0 or 1, whichever is the base case. Go another step and create that with a decrease in n as part of it. Then judge if that holds for future n and that you did not miss a base case and then it is recursive.
So for 4 it goes another step (n=3) and finds 3, setting temp to that. Next step it goes to n=4 and finds 1, which is smaller. Temp is still 3 and returned since there are no further recursions to do.
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On January 01 2017 22:22 Yurie wrote:
Thank you, both of you. I think I get it now. Do for n=0 or 1, whichever is the base case. Go another step and create that with a decrease in n as part of it. Then judge if that holds for future n and that you did not miss a base case and then it is recursive.
So for 4 it goes another step (n=3) and finds 3, setting temp to that. Next step it goes to n=4 and finds 1, which is smaller. Temp is still 3 and returned since there are no further recursions to do.
https://vimeo.com/24716767
This is a nice introduction to basic recursion.
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On December 29 2016 23:43 mantequilla wrote:do you guys know a better way?
You could try to avoid using callbacks unless you're actually forced to (e.g. cus you're using JavaScript). Check out Futures instead. Arguably cleaner to use.
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On January 01 2017 03:58 Targe wrote:Show nested quote +On December 31 2016 08:49 spinesheath wrote:On December 31 2016 07:46 Targe wrote:
the problem i believe requires me to synchronise all threads with every iteration (with each loop threads need the new values updated by other threads or they wont be using the right values), for the shared memory implementation i had a barrier at the end of every loop and after the threads checked whether the required precision was reached.
i need to come up with a way of passing the updated information between the threads with as little overhead as possible It sounds like you probably have some wiggle room. I imagine this as a 2D Array, with 4 neighbours top/bottom/left/right. Naively you would probably split a 40x40 Array into 16 10x10 Arrays with an extra ring outside for synchronization, so 16 12x12 Arrays. But maybe you could also use 14x14 arrays and synchronize 2 rings every 2 iterations. Depending on the concrete environment that could improve performance. I don't know if this is even remotely close to your actual problem, but I would suspect that there are some options whatever it is exactly. Look for less intuitive chunks to cut your problem into and compare them to what you have. i dont know why i didnt think of splitting it into smaller arrays, ive been splitting the work for each thread by the first X elements in the array (where X is the total number of elements divided by the number of arrays), thats pretty eye opening for possible solutions thanks! trying that route i would have to do some correctness testing to ensure that the correct results are achieved Show nested quote +On December 31 2016 18:49 RoomOfMush wrote:On December 31 2016 07:46 Targe wrote:On December 31 2016 05:00 Mr. Wiggles wrote:On December 31 2016 03:56 Targe wrote:On December 31 2016 03:12 Mr. Wiggles wrote:On December 31 2016 02:36 Targe wrote:
is anyone familiar with MPI?
im trying to determine whether the communication overhead is greater than the overhead of retrieving data from another node in a massively parallel computer
I've used MPI a little bit on relatively small (~8 machine) clusters as part of some course work. Would you mind explaining your problem a little more? I'm not quite sure what you're asking from what you've written here. im trying to decide how to approach a problem (need to write a program that repeatedly replaces the values in an array with the value of its 4 neighbours, with the exception of boundary values, until the values of the array settle to within a given precision) i think i have access to 4 nodes (16 cores per node) and need to come up with and test a solution to the above problem previously i wrote a program as a solution to the same problem but for an environment with shared memory rather than distributed memory e: the idea is for the program to scale as best as possible with the number of threads so im interested in what has the most overhead MPI itself doesn't have that much overhead, as the major implementations are mature and optimized. Most of your overhead is going to come from communication and synchronization costs in your program. This is all very workload dependent, so I can give my thoughts, but I can only really outline what to look at. There's obviously going to be a cost if you have to transfer data back and forth between nodes, but depending on the length of the computation and how fast you can transfer data between nodes, this might be amortized. Similarly, any time your program has to perform some kind of synchronization/communication you're going to pay an overhead based on the communication latency between your nodes. So, your observed speedups are going to depend a lot on how much synchronization and communication needs to occur between your nodes. If you can just partition your dataset between all the nodes and let them chug away, you're likely to see good speedups. If your nodes need to constantly communicate between each other, you might hit a bottleneck. Depending on what synchronization primitives you're using in your shared-memory program, porting to MPI may be relatively straightforward. For example, if synchronization is barrier-based, pthread_barrier_wait() transfers directly to MPI_Barrier(). MPI provides some higher-level functions which can make porting a bit nicer, but is generally pretty low-level. If you're just interested in making one problem instance run as fast as possible, looking at distributed memory frameworks makes sense. Depending on your workload, it might also make sense to just run four different instances on each available node. In this case, it depends on if you care about throughput or response time for a single problem instance. All in all, I can only say that MPI doesn't have much inherent overhead, and that the choice to use it basically depends on your problem and what your workload looks like. If your problem exhibits coarse granularity and doesn't require much synchronization overhead, I'd say go for it and measure what speedups you see. If there's a large amount of synchronization required, then you might not see good speedups, even if you're giving additional resources to the program. sorry for being confusing, by overhead i meant to say the overhead of communication, your post is still informative though. the problem i believe requires me to synchronise all threads with every iteration (with each loop threads need the new values updated by other threads or they wont be using the right values), for the shared memory implementation i had a barrier at the end of every loop and after the threads checked whether the required precision was reached. i need to come up with a way of passing the updated information between the threads with as little overhead as possible I assume the size of your problem is sufficiently big to even think about spreading it over multiple processors. In that case I would suggest calculating the values that need to be shared first, then start the communication while simultaneously working on the values that dont need to be shared. Ideally the messages from the other machines will arrive before you have finished with all the local calculations and your communication overhead becomes zero. size of the problem is up to me (but yes i choose sizes large enough for splitting the work to be worth it) ill look to see if what you suggested is possible, my curent understanding is that whilst sending/receiving a message a thread doesnt progress through its logic? also as MPI requires both the sending thread and the receiving thread to call a send/receive function at the send time ill have to have some sort of master/slave system in place for synchronisation? but having 1 master for 10+ slaves seems like a major bottleneck as it will have to wait for each thread?
Communications in MPI are done through what they call "communicators", which essentially define groups of processes which can communicate together.
If you wanted to do master/slave, you would just use MPI_COMM_WORLD (global communicator) to have communication which spans all MPI processes in the system. Alternatively, to perform communications between specific subsets of processes, you'd have to manually set up new communicators, either with MPI_Comm_split() or by manually creating groups and then calling MPI_Comm_create(). This might make sense depending on exactly how you split the work. For example, if you have several processes which need to communicate only amongst themselves when doing work, it probably makes sense to create a communicator for them.
Lastly, you can also do non-blocking communication in MPI if that makes more sense for your use case. this is done through the MPI_I* (MPI_Irecv(), MPI_Isend(), etc.) functions. These functions generally return a handle to a communication request which can be checked later with MPI_Wait()/MPI_Test() when you're ready to consume output.
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United Kingdom14103 Posts
On January 02 2017 07:43 Mr. Wiggles wrote:Show nested quote +On January 01 2017 03:58 Targe wrote:On December 31 2016 08:49 spinesheath wrote:On December 31 2016 07:46 Targe wrote:
the problem i believe requires me to synchronise all threads with every iteration (with each loop threads need the new values updated by other threads or they wont be using the right values), for the shared memory implementation i had a barrier at the end of every loop and after the threads checked whether the required precision was reached.
i need to come up with a way of passing the updated information between the threads with as little overhead as possible It sounds like you probably have some wiggle room. I imagine this as a 2D Array, with 4 neighbours top/bottom/left/right. Naively you would probably split a 40x40 Array into 16 10x10 Arrays with an extra ring outside for synchronization, so 16 12x12 Arrays. But maybe you could also use 14x14 arrays and synchronize 2 rings every 2 iterations. Depending on the concrete environment that could improve performance. I don't know if this is even remotely close to your actual problem, but I would suspect that there are some options whatever it is exactly. Look for less intuitive chunks to cut your problem into and compare them to what you have. i dont know why i didnt think of splitting it into smaller arrays, ive been splitting the work for each thread by the first X elements in the array (where X is the total number of elements divided by the number of arrays), thats pretty eye opening for possible solutions thanks! trying that route i would have to do some correctness testing to ensure that the correct results are achieved On December 31 2016 18:49 RoomOfMush wrote:On December 31 2016 07:46 Targe wrote:On December 31 2016 05:00 Mr. Wiggles wrote:On December 31 2016 03:56 Targe wrote:On December 31 2016 03:12 Mr. Wiggles wrote:On December 31 2016 02:36 Targe wrote:
is anyone familiar with MPI?
im trying to determine whether the communication overhead is greater than the overhead of retrieving data from another node in a massively parallel computer
I've used MPI a little bit on relatively small (~8 machine) clusters as part of some course work. Would you mind explaining your problem a little more? I'm not quite sure what you're asking from what you've written here. im trying to decide how to approach a problem (need to write a program that repeatedly replaces the values in an array with the value of its 4 neighbours, with the exception of boundary values, until the values of the array settle to within a given precision) i think i have access to 4 nodes (16 cores per node) and need to come up with and test a solution to the above problem previously i wrote a program as a solution to the same problem but for an environment with shared memory rather than distributed memory e: the idea is for the program to scale as best as possible with the number of threads so im interested in what has the most overhead MPI itself doesn't have that much overhead, as the major implementations are mature and optimized. Most of your overhead is going to come from communication and synchronization costs in your program. This is all very workload dependent, so I can give my thoughts, but I can only really outline what to look at. There's obviously going to be a cost if you have to transfer data back and forth between nodes, but depending on the length of the computation and how fast you can transfer data between nodes, this might be amortized. Similarly, any time your program has to perform some kind of synchronization/communication you're going to pay an overhead based on the communication latency between your nodes. So, your observed speedups are going to depend a lot on how much synchronization and communication needs to occur between your nodes. If you can just partition your dataset between all the nodes and let them chug away, you're likely to see good speedups. If your nodes need to constantly communicate between each other, you might hit a bottleneck. Depending on what synchronization primitives you're using in your shared-memory program, porting to MPI may be relatively straightforward. For example, if synchronization is barrier-based, pthread_barrier_wait() transfers directly to MPI_Barrier(). MPI provides some higher-level functions which can make porting a bit nicer, but is generally pretty low-level. If you're just interested in making one problem instance run as fast as possible, looking at distributed memory frameworks makes sense. Depending on your workload, it might also make sense to just run four different instances on each available node. In this case, it depends on if you care about throughput or response time for a single problem instance. All in all, I can only say that MPI doesn't have much inherent overhead, and that the choice to use it basically depends on your problem and what your workload looks like. If your problem exhibits coarse granularity and doesn't require much synchronization overhead, I'd say go for it and measure what speedups you see. If there's a large amount of synchronization required, then you might not see good speedups, even if you're giving additional resources to the program. sorry for being confusing, by overhead i meant to say the overhead of communication, your post is still informative though. the problem i believe requires me to synchronise all threads with every iteration (with each loop threads need the new values updated by other threads or they wont be using the right values), for the shared memory implementation i had a barrier at the end of every loop and after the threads checked whether the required precision was reached. i need to come up with a way of passing the updated information between the threads with as little overhead as possible I assume the size of your problem is sufficiently big to even think about spreading it over multiple processors. In that case I would suggest calculating the values that need to be shared first, then start the communication while simultaneously working on the values that dont need to be shared. Ideally the messages from the other machines will arrive before you have finished with all the local calculations and your communication overhead becomes zero. size of the problem is up to me (but yes i choose sizes large enough for splitting the work to be worth it) ill look to see if what you suggested is possible, my curent understanding is that whilst sending/receiving a message a thread doesnt progress through its logic? also as MPI requires both the sending thread and the receiving thread to call a send/receive function at the send time ill have to have some sort of master/slave system in place for synchronisation? but having 1 master for 10+ slaves seems like a major bottleneck as it will have to wait for each thread? Communications in MPI are done through what they call "communicators", which essentially define groups of processes which can communicate together. If you wanted to do master/slave, you would just use MPI_COMM_WORLD (global communicator) to have communication which spans all MPI processes in the system. Alternatively, to perform communications between specific subsets of processes, you'd have to manually set up new communicators, either with MPI_Comm_split() or by manually creating groups and then calling MPI_Comm_create(). This might make sense depending on exactly how you split the work. For example, if you have several processes which need to communicate only amongst themselves when doing work, it probably makes sense to create a communicator for them. Lastly, you can also do non-blocking communication in MPI if that makes more sense for your use case. this is done through the MPI_I* (MPI_Irecv(), MPI_Isend(), etc.) functions. These functions generally return a handle to a communication request which can be checked later with MPI_Wait()/MPI_Test() when you're ready to consume output.
im really unfamiliar with MPI, this will be my first program using it and this is all super helpful info
MPI_Isend() seems like its what i want to use, i cant see whether it works like java thread start where you initialiaze, then call start later or if you just call it then the thread thats receiving accepts the data when its ready?
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lol, almost done with my brute force TSP solver... gotta debug this
right now it's outputting a seemingly random solution every time
which looks really neat if i just sit and click it over and over but isn't what I want haha
edit: well this is odd. it's finding the correct amount of pathways (n-1)!, but it's not finding the optimal solution for some reason. that or its checking the same pathways twice but that seems unlikely since it's finding the right amount of pathways
edit2: it's not storing the nodes correctly, well here I go into the deep dark tunnels of debugging this
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AHAHA yes
got it working!
This is probably absolutely nothing to some of you and I am sure my code isn't great (god it looks complicated) but for me this is the hardest thing I have done in programming, lol
Solves under 10 cities very fast. 10 cities takes like 10 seconds.. well maybe slightly faster
anyways, very cool I am happy
most important new thing I learned: using constructors in java data structures when making copies. super handy! and necessary to avoid concurrent modification when using recursion.
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What resources would You guys recommend to someone trying to learn C ?
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On January 03 2017 00:20 Silvanel wrote:
What resources would You guys recommend to someone trying to learn C ?
Get this book. By far the least daunting experience. It's also very good and will get you started real nice: https://en.wikipedia.org/wiki/The_C_Programming_Language
You can compliment it with this: https://learncodethehardway.org/c/
Unfortunately ZAS doesn't provide his content for free any more (you can still get it on EvilZone if that's your thing), some people have also criticized it, but I've found his introduction to some of the other tools you should be using alongside C very useful for someone not familiar with it (Make and Valgrind primarily).
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On January 02 2017 08:43 Targe wrote:Show nested quote +On January 02 2017 07:43 Mr. Wiggles wrote:On January 01 2017 03:58 Targe wrote:On December 31 2016 08:49 spinesheath wrote:On December 31 2016 07:46 Targe wrote:
the problem i believe requires me to synchronise all threads with every iteration (with each loop threads need the new values updated by other threads or they wont be using the right values), for the shared memory implementation i had a barrier at the end of every loop and after the threads checked whether the required precision was reached.
i need to come up with a way of passing the updated information between the threads with as little overhead as possible It sounds like you probably have some wiggle room. I imagine this as a 2D Array, with 4 neighbours top/bottom/left/right. Naively you would probably split a 40x40 Array into 16 10x10 Arrays with an extra ring outside for synchronization, so 16 12x12 Arrays. But maybe you could also use 14x14 arrays and synchronize 2 rings every 2 iterations. Depending on the concrete environment that could improve performance. I don't know if this is even remotely close to your actual problem, but I would suspect that there are some options whatever it is exactly. Look for less intuitive chunks to cut your problem into and compare them to what you have. i dont know why i didnt think of splitting it into smaller arrays, ive been splitting the work for each thread by the first X elements in the array (where X is the total number of elements divided by the number of arrays), thats pretty eye opening for possible solutions thanks! trying that route i would have to do some correctness testing to ensure that the correct results are achieved On December 31 2016 18:49 RoomOfMush wrote:On December 31 2016 07:46 Targe wrote:On December 31 2016 05:00 Mr. Wiggles wrote:On December 31 2016 03:56 Targe wrote:On December 31 2016 03:12 Mr. Wiggles wrote:On December 31 2016 02:36 Targe wrote:
is anyone familiar with MPI?
im trying to determine whether the communication overhead is greater than the overhead of retrieving data from another node in a massively parallel computer
I've used MPI a little bit on relatively small (~8 machine) clusters as part of some course work. Would you mind explaining your problem a little more? I'm not quite sure what you're asking from what you've written here. im trying to decide how to approach a problem (need to write a program that repeatedly replaces the values in an array with the value of its 4 neighbours, with the exception of boundary values, until the values of the array settle to within a given precision) i think i have access to 4 nodes (16 cores per node) and need to come up with and test a solution to the above problem previously i wrote a program as a solution to the same problem but for an environment with shared memory rather than distributed memory e: the idea is for the program to scale as best as possible with the number of threads so im interested in what has the most overhead MPI itself doesn't have that much overhead, as the major implementations are mature and optimized. Most of your overhead is going to come from communication and synchronization costs in your program. This is all very workload dependent, so I can give my thoughts, but I can only really outline what to look at. There's obviously going to be a cost if you have to transfer data back and forth between nodes, but depending on the length of the computation and how fast you can transfer data between nodes, this might be amortized. Similarly, any time your program has to perform some kind of synchronization/communication you're going to pay an overhead based on the communication latency between your nodes. So, your observed speedups are going to depend a lot on how much synchronization and communication needs to occur between your nodes. If you can just partition your dataset between all the nodes and let them chug away, you're likely to see good speedups. If your nodes need to constantly communicate between each other, you might hit a bottleneck. Depending on what synchronization primitives you're using in your shared-memory program, porting to MPI may be relatively straightforward. For example, if synchronization is barrier-based, pthread_barrier_wait() transfers directly to MPI_Barrier(). MPI provides some higher-level functions which can make porting a bit nicer, but is generally pretty low-level. If you're just interested in making one problem instance run as fast as possible, looking at distributed memory frameworks makes sense. Depending on your workload, it might also make sense to just run four different instances on each available node. In this case, it depends on if you care about throughput or response time for a single problem instance. All in all, I can only say that MPI doesn't have much inherent overhead, and that the choice to use it basically depends on your problem and what your workload looks like. If your problem exhibits coarse granularity and doesn't require much synchronization overhead, I'd say go for it and measure what speedups you see. If there's a large amount of synchronization required, then you might not see good speedups, even if you're giving additional resources to the program. sorry for being confusing, by overhead i meant to say the overhead of communication, your post is still informative though. the problem i believe requires me to synchronise all threads with every iteration (with each loop threads need the new values updated by other threads or they wont be using the right values), for the shared memory implementation i had a barrier at the end of every loop and after the threads checked whether the required precision was reached. i need to come up with a way of passing the updated information between the threads with as little overhead as possible I assume the size of your problem is sufficiently big to even think about spreading it over multiple processors. In that case I would suggest calculating the values that need to be shared first, then start the communication while simultaneously working on the values that dont need to be shared. Ideally the messages from the other machines will arrive before you have finished with all the local calculations and your communication overhead becomes zero. size of the problem is up to me (but yes i choose sizes large enough for splitting the work to be worth it) ill look to see if what you suggested is possible, my curent understanding is that whilst sending/receiving a message a thread doesnt progress through its logic? also as MPI requires both the sending thread and the receiving thread to call a send/receive function at the send time ill have to have some sort of master/slave system in place for synchronisation? but having 1 master for 10+ slaves seems like a major bottleneck as it will have to wait for each thread? Communications in MPI are done through what they call "communicators", which essentially define groups of processes which can communicate together. If you wanted to do master/slave, you would just use MPI_COMM_WORLD (global communicator) to have communication which spans all MPI processes in the system. Alternatively, to perform communications between specific subsets of processes, you'd have to manually set up new communicators, either with MPI_Comm_split() or by manually creating groups and then calling MPI_Comm_create(). This might make sense depending on exactly how you split the work. For example, if you have several processes which need to communicate only amongst themselves when doing work, it probably makes sense to create a communicator for them. Lastly, you can also do non-blocking communication in MPI if that makes more sense for your use case. this is done through the MPI_I* (MPI_Irecv(), MPI_Isend(), etc.) functions. These functions generally return a handle to a communication request which can be checked later with MPI_Wait()/MPI_Test() when you're ready to consume output. im really unfamiliar with MPI, this will be my first program using it and this is all super helpful info MPI_Isend() seems like its what i want to use, i cant see whether it works like java thread start where you initialiaze, then call start later or if you just call it then the thread thats receiving accepts the data when its ready?
I'm not super familiar with Java, but I think it's closer to the second scenario. You would call MPI_Isend() on the sending node, and after that point, you can't touch the buffer containing the sent data until you've checked for completion with one of MPI_Wait()/MPI_Test() or their multi-handle equivalents. Since the send is non-blocking, the send buffer can't be touched until you've verified that the send has completed by calling one of those functions. Modifying the send buffer before the send completes would cause incorrect (undefined?) behaviour.
Similarly on the receiving end, you would call MPI_Irecv(), but have to query the handle to make sure that the receive has completed before being able to consume the output. In a master/slave program, you could have the master perform an MPI_Irecv() for each expected slave message, and then loop using MPI_Waitany() over the returned handles to consume input as it arrives. This might make sense if you expect differing send times for each slave, or if each slave is simply streaming data back to the master, and it makes more sense to consume this data as it arrives than to wait for all of it.
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On January 03 2017 00:20 Silvanel wrote:
What resources would You guys recommend to someone trying to learn C ?
This might be too hard if you have no programming experience, but if you do, use this:
http://icube-icps.unistra.fr/index.php/File:ModernC.pdf
This book focuses on how to avoid many of C’s pitfalls and teaches many good habits that are not possible in older versions of the language.
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On January 03 2017 20:37 Biolunar wrote:Show nested quote +On January 03 2017 00:20 Silvanel wrote:
What resources would You guys recommend to someone trying to learn C ? This might be too hard if you have no programming experience, but if you do, use this: http://icube-icps.unistra.fr/index.php/File:ModernC.pdfThis book focuses on how to avoid many of C’s pitfalls and teaches many good habits that are not possible in older versions of the language.
thanks for the cool book. the information about c and c++ is so overpopulated you can't find up to date modern knowledge about the languages without running into outdated complex content. even basic things like strings have so much information clutter when it comes to c and cpp.
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Fuck material design and fuck DataTables. Fuck front-end. Fuck!
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On January 04 2017 01:40 Manit0u wrote:
Fuck material design and fuck DataTables. Fuck front-end. Fuck!
You sound like me haha.
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Hyrule19087 Posts
Old datatables was awful. New datatables is pretty nice
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EPT
