So multiple threads can collaborate to solve the same computation, but each one does a smaller amount of work. An instruction can specify, in addition to various arithmetic operations, the address of a datum to be read or written in memory and/or the address of the next instruction to be executed. So you might have seen in the previous talk and the previous lecture, it was SIMD, single instruction or same instruction, multiple data, which allowed you to execute the same operation, you know, and add over multiple data elements. So I just wrote down some actual code for that loop that parallelize it using Pthreads, a commonly used threading mechanism. And then computation can go on. PROFESSOR: Yes. True. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw. And I can't do anything about this sequential work either. So one processor can send a single operand to another reasonably fast. And what that translates to is -- sorry, there should have been an animation here to ask you what I should add in. It could be really efficient for sending short control messages, maybe even efficient for sending data messages. You put the entire thing on a single processor. There's some overhead for message. An example of a blocking send on Cell -- allows you to use mailboxes. Everybody can access it. Although you can imagine that in each one of these circles there's some really heavy load computation. So this get here is going to write into ID zero. So I can fetch all the elements of A0 to A3 in one shot. So naturally you can adjust this granularity to sort of reduce the communication overhead. So you start off with your program and then you annotate the code with what's parallel and what's not parallel. So it depends on how you've partitioned your problems. So you essentially stop and wait until the PPU has, you know, caught up. So in a uniform memory access architecture, every processor is either, you can think of it as being equidistant from memory. That clear so far? Learn parallelization concepts and techniques guided by parallel patterns used in real software. So there is some implicit synchronization that you have to do. So to understand performance, we sort of summarize three main concepts that you essentially need to understand. So an example of sort of a non-blocking send and wait on Cell is using the DMAs to ship data out. I know my application really, really well. And so that's shown here: red, blue, and orange. If everybody needs to reach the same point because you're updating a large data structure before you can go on, then you might not be able to do that. Parallel Programming: Concepts and Practice provides an upper level introduction to parallel programming. So in MPI there's this function, MPI broadcast, that does that. And what does this really mean? Or my parallel version is 1.67 times faster. And you add it all up and in the end you can sort of print out what is the value of pi that you calculated. And that has good properties in that you have a small amount of work done between communication stages. » So, you know, guys that are close together can essentially add up their numbers and forward me. And so you can keep helping out, you know, your other processors to compute things faster. And you certainly could do sort of a hybrid load balancing, static plus dynamic mechanism. But then as processor two executes and completes faster than processor one, it takes on some of the additional work from processor one. So an example of a message passing program -- and if you've started to look at the lab you'll see that this is essentially where the lab came from. Again, it's same program, multiple data, or supports the SPMD model. So here you're sending all of A, all of B. Or you have some network in between. And there is really four questions that you essentially have to go through. Because for this instruction to execute, it needs to receive data from P1 and P2. But the computation is essentially the same except for the index at which you start, in this case changed for processor two. I do computations but, you know, computation doesn't last very long. And, you know, number of messages. Are all messages the same? And there's some subset of A. And then it goes on and does more work. So if I have an allocation that just says, well, let's put all these chains on one processor, put these two chains on two different processors, well, where are my synchronization points? And this is essentially the computation that we're carrying out. With more than 2,400 courses available, OCW is delivering on the promise of open sharing of knowledge. Not all prior programs have this kind of, sort of, a lot of parallelism, or embarrassingly parallel computation. And what you might need to do is some mechanism to essentially tell the different processors, here's the code that you need to run and maybe where to start. And what's interesting about multicores is that they're essentially putting a lot more resources closer together on a chip. That's because communication is not often free. machine is ?] So what that means is, you know, there's a lot of contention for that one memory bank. So this is orthogonal really to synchronous versus asynchronous. And this is computation that you want to parallelize. Embedded devices can also be thought of as small Courses And what I've done here is I've parameterized where you're essentially starting in the array. So the last sort of thing I'm going to talk about in terms of how granularity impacts performance -- and this was already touched on -- is that communication is really not cheap and can be quite overwhelming on a lot of architectures. So the computation naturally should just be closer together because that decreases the latency that I need to communicate. So it's either zero or one. AUDIENCE: But also there is pipelining. Or another way, it has the same access latency for getting data from memory. You can essentially rename it on each processor. And you can go through, do your computation. So there is sort of a programming model that allows you to do this kind of parallelism and tries to sort of help the programmer by taking their sequential code and then adding annotations that say, this loop is data parallel or this set of code is has this kind of control parallelism in it. Yeah, because communication is such an intensive part, there are different ways of dealing with it. So I enter this mail loop and I do some calculation to figure out where to write the next data. So the work that was here is now shifted. So in fine-grain parallelism, you have low computation to communication ratio. So you're performing the same computation, but instead of operating on one big chunk of data, I've partitioned the data into smaller chunks and I've replicated the computation so that I can get that kind of parallelism. The next thread is requesting ace of four, the next thread ace of eight, next thread ace of 12. contemporary parallel programmingmodels, So rather than having, you know, your parallel cluster now which is connected, say, by ethernet or some other high-speed link, now you essentially have large clusters or will have large clusters on a chip. So it essentially is changing the factors for communication. Because that means the master slows down. How are the processes identified? Description Parallel Programming: Concepts and Practice provides an upper level introduction to parallel programming. In fact, I think there's only six basic MPI commands that you need for computing. And it has some place that it's already allocated where it's going to write C, the results of the computation, then I can break up the work just like it was suggested. So a simple loop that essentially does this -- and there are n squared interactions, you have, you know, a loop that loops over all the A elements, a loop that loops over all the B elements. One is how is the data described and what does it describe? So the load balancing problem is just an illustration. So in this case this particular send will block because, let's say, the PPU hasn't drained its mailbox. In the two processor case that was easy. If I increase the number of processors or that gets really large, that's essentially my upper bound on how fast programs can work. But the cost model is relatively captured by these different parameters. No guesses? OK. So if you have near neighbors talking, that may be different than two things that are further apart in space communicating. And similarly, if you're doing a receive here, make sure there's sort of a matching send on the other end. And static mapping just means, you know, in this particular example, that I'm going to assign the work to different processors and that's what the processors will do. So if one processor, say P1, wants to look at the value stored in processor two's address, it actually has to explicitly request it. One is the volume. Now what happens here is there's processor ID zero which I'm going to consider the master. OK, so what would you do with two processors? So it's either buffer zero or buffer one. So in coarse-grain parallelism, you sort of make the work chunks more and more so that you do the communication synchronization less and less. So if I have some computation -- let's say it has three parts: a sequential part that takes 25 seconds, a parallel part that takes 50 seconds, and a sequential part that runs in 25 seconds. 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