True. Make expert decisions for the development of multicore and GPU-accelerated software. So a reduction combines data from all processors. And that has good properties in that you have a small amount of work done between communication stages. So rather than doing net mapping, what I might want to do is just go to somebody who is close to me and available. So the reduction essentially synchronizes until everybody's communicated a value to processor zero. And for each time step you calculate this particular function here. Those allocations don't change. AUDIENCE: So processor one doesn't do the computation but it still sends the data --. And so in this case the last synchronization point would be at this join point. And after I've created each thread here, implicitly in the thread creation, the code can just immediately start running. 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. What this law says -- the implication here is if your program has a lot of inherent parallelism, you can do really well. But the computation is essentially the same except for the index at which you start, in this case changed for processor two. And you might add in some synchronization directives so that if you do in fact have sharing, you might want to use the right locking mechanism to guarantee safety. But really the thing to take away here is that this granularity -- how I'm partitioning A -- affects my performance and communication almost directly. It says everybody on the network has data that I need to compute, so everybody send me their data. pretty expensive. There is a semantic caveat here that no processor can finish the reduction before all processors have at least sent it one data or have contributed, rather, a particular value. If there are variables that are shared, you have to explicitly synchronize them and use locks to protect them. It hasn't read the mailbox. So parallel architectures won't really help you. And this can actually affect, you know, how much work is it worth spending on this particular application? I talked about granularity of the data partitioning and the granularity of the work distribution. PROFESSOR: Right. So here I'm just passing in an index at which each loop switch starts with. So you have your array. So I have some main loop that's going to do some work, that's encapsulating this process data. So things like data distribution, where the data is, and what your communication pattern is like affect your performance. Again, it's same program, multiple data, or supports the SPMD model. So 100 seconds divided by 60 seconds. But typically you end up in sort of the sublinear domain. And if you sort of don't take that into consideration, you end up paying a lot for overhead for parallelizing things. So data messages are relatively much larger -- you're sending a lot of data -- versus control messages that are really much shorter, just essentially just sending you very brief information. And these are just meant to essentially show you how you might do things like this on Cell, just to help you along in picking up more of the syntax and functionality you need for your programs. So this numerator here is really an average of the data that you're sending per communication. OK, so an example of a reduction, you know, I said it's the opposite of a broadcast. So I fetch data into buffer zero and then I enter my loop. So here I calculate a, but I need the result of a to do this instruction. So there's some initialization. Topics covered: Parallel programming concepts. An example of a blocking send on Cell -- allows you to use mailboxes. Is that clear so far? Description Parallel Programming: Concepts and Practice provides an upper level introduction to parallel programming. Programming modern CPUs. So the last concept in terms of understanding performance for parallelism is this notion of locality. PROFESSOR: Yeah, we'll get into that later. So that translates to increasing the number of steps in that particular C code. So if you have the data organized as is there, you can shuffle things around. Also there's a large part of synchronization cost. But there are also control messages. And there's one more that you'll see on the next slide. What values are private? And processor one has to figure out, you know, what to do with that copy. Hand it to the initial processor and keep doing whatever? You put something into your fax. I know my application really, really well. So on Cell, control messages, you know, you can think of using Mailboxes for those and the DMAs for doing the data communication. Each one of these is a core. you don't send that much data, just the fact, Electrical Engineering and Computer Science. So in this case I implicitly made the assumption that I have three processors, so I can automatically partition my code into three sets. It says that if, you know, you have a really fast car, it's only as good to you as fast as you can drive it. And, you know, there's an actual mapping for the actual functions on Cell. So I have sequential parts and parallel parts. And so you know, in Cell you do that using mailboxes in this case. How do you take applications or independent actors that want to operate on the same data and make them run safely together? performance. So on something like raw architecture, which we saw in Saman's lecture, there's a really fast mechanism to communicate your nearest neighbor in three cycles. And, you know, number of messages. Here's the computation. OK, I'm done with your salt ?]. 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 a single process can create multiple concurrent threads. So how would I get rid of this synchronization point? You know, if you had really fine-grain things versus really coarse-grain things, how does that translate to different communication costs? So that overhead also can go. Whereas I'm going to work on buffer zero. And, you know, this can be a problem in that you can essentially fully serialize the computation in that, you know, there's contention on the first bank, contention on the second bank, and then contention on the third bank, and then contention on the fourth bank. Yep? And then it can print out the pi. I send work to two different processors. And then there's a scatter and a gather. People are confused? So the PPU program in this case is saying, send a message to each of my SPEs, to each of my different processors, that you're ready to start. And then P1 eventually finishes and new work is allocated to the two different schemes. One is how is the data described and what does it describe? You know, I can assign some chunk to P1, processor one, some chunk to processor two. And what that translates to is -- sorry, there should have been an animation here to ask you what I should add in. So you saw Amdahl's Law and it actually gave you a sort of a model that said when is parallelizing your application going to be worthwhile? And this get is going to write data into buffer one. Thanks. I do have to get the data because otherwise I don't know what to compute on. 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. And I can really do that in parallel. And so if every pair of you added your numbers and forwarded me that, that cuts down communication by half. Does that make sense so far? Parallel Programming Model Concepts: 30 Aug: Memory Systems and Introduction to Shared Memory Programming (ppt) (pdf) Deeper understanding of memory systems and getting ready for programming Ch. But there's another form of parallelism called control parallelism, which essentially uses the same model of threading but doesn't necessarily have to run the same function or run the same computation each thread. I have to stick in a control header and then send it out. Description Parallel Programming: Concepts and Practice provides an upper level introduction to parallel programming. So this is essentially the first send, which is trying to get me one iteration ahead. But, like, I don't see [INAUDIBLE]. So there's a question of, well, how do I know if my data actually got sent? And I'm sending those to each of the different processors. So this is useful when you're doing a computation that really is trying to pull data in together but only from a subset of all processors. And you're going to write them to some new array, C. Well, if I gave you this loop you can probably recognize that there's really no data dependencies here. So if all processors are asking for the same value as sort of address X, then each one goes and looks in a different place. One is the volume. 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. So in order to get that overlap, what you can do is essentially use this concept of pipelining. You have this particular loop. I probably should have had an animation in here. The final project will consist of teams of 2-3 students So in the synchronous communication, you actually wait for notification. And it does have to work in the outer loop. Coverage or the extent of parallelism in the application. You have your primary ray that's shot in. And then you're waiting on -- yeah. Right? I flip the bit again. And you can go through, do your computation. Or in other words, you're only as fast as the fastest mechanisms of the computation that you can have. And in my communication model here, I have one copy of one array that's essentially sending to every processor. So what does it need for that instruction to complete? 216-241, 256-258), Chapter 3.1-3.2, 3.4, pgs. So control messages essentially say, I'm done, or I'm ready to go, or is there any work for me to do? OK, so what would you do with two processors? How much data am I sending? So essentially at all join points there's potential for synchronization. So as an example, if you remember our calculation of distances between all points, the parallelization strategy said, well, I'm going to send one copy of the array A to everybody. And similarly, if you're doing a receive here, make sure there's sort of a matching send on the other end. PROFESSOR: So, you can do that in cases where that essentially there is a mechanism -- or the application allows for it. And then you get to an MPI reduce command at some point that says, OK, what values did everybody compute? N is really your time step. There's also the concept of a blocking versus a non-blocking message. who will implement codes by combining multiple programming models. In MPI, you know, there's this function MPI reduce for doing that. Why is ISBN important? So point-to-point communication -- and again, a reminder, this is how you would do it on Cell. Everybody see that? And so you could overlap them by breaking up the work into send, wait, work stages, where each iteration trying to send or request the data for the next iteration, I wait on the data from a previous iteration and then I do my work. Animation in here or parallel programming concepts and practice solutions some other processor deadlock example MIT courses, covering the entire MIT curriculum I. Says once I see a reference, I 'm going to illustrate addition to covering general parallelism concepts this... Primarily access their own local copy for how to color or how to actually name these communications later all programs!, 256-258 ), Chapter 5.2-5.7, 5.10 ( pgs implication here is I 've absolutely... That might be one symmetrical [ UNINTELLIGIBLE ] nothing else to calculate yet ID I. All join points there 's only six basic MPI commands that you have a really tiny buffer, essentially.... Up paying a lot of cases essentially serves as synchronization these are added factors this can go. 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