So point-to-point communication -- and again, a reminder, this is how you would do it on Cell. And you've reached some communication stage. So this is essentially one iteration of the work. » And I'm sending those to each of the different processors. microprocessors has made parallel computing available to the masses. And that can impact your synchronization or what kind of data you're shipping around. So that's showing here. So these are the computations here and these yellow bars are the synchronization points. So suppose that you have a numerical integration method that essentially you're going to use to calculate pi. And so just recapping the last two lectures, you saw really two primary classes of architectures. So an example of that might be the Cell, loosely said, where you have cores that primarily access their own local memory. And this transformation can sort of be done automatically by the compiler. An asynchronous send, it's like you write a letter, you go put it in the mailbox, and you don't know whether it actually made it into the actual postman's bag and it was delivered to your destination or if it was actually delivered. So how would I get rid of this synchronization point? There's also broadcast that says, hey, I have some data that everybody's interested in, so I can just broadcast it to everybody on the network. you don't send that much data, just the fact, Electrical Engineering and Computer Science. Description Parallel Programming: Concepts and Practice provides an upper level introduction to parallel programming. This course is a comprehensive exploration of parallel programming paradigms, AUDIENCE: [UNINTELLIGIBLE PHRASE] two things in that overhead part. Topics covered: Parallel programming concepts. You put the entire thing on a single processor. Because for this instruction to execute, it needs to receive data from P1 and P2. So I have to calculate this distance. just because it was difficult, as you might be finding in terms of programming things with the Cell processor. So as an example of parallelization, you know, straightforward parallelization in a shared memory machine, would be you have the simple loop that's just running through an array. And I'm going to write it to buffer one. So, you know, guys that are close together can essentially add up their numbers and forward me. And you can get into potential deadlock situations. With more than 2,400 courses available, OCW is delivering on the promise of open sharing of knowledge. Learn about condition variables, semaphores, barriers, thread pools, and more. Parallel Programming: Concepts and Practice provides an upper level introduction to parallel programming. Yeah. And that helps you in sort of making sure that things are operating reasonably in lock step at, you know, partially ordered times. But you don't know anything else about what happened to the message along the way. And you're trying to parallelize that over your architecture to get the best performance. 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. So let's say I have processor one and processor two and they're trying to send messages to each other. So this is kind of like your fax machine. Well, you calculate speedup, old running time divided by the new running time. And SPEs can be basically waiting for data, get the computation, send it back. So five in this case. The developer will be presented with the chance to modify the source code in some exercises in order to become familiar with DPC++ program concepts. What is the granularity of the sub-problems I'm going to calculate on? So there's a well-known sort of message passing library specification called MPI that tries to specify all of these different communications in order to sort of facilitate parallel programming. AUDIENCE: [UNINTELLIGIBLE] nothing else to calculate yet. So this is essentially the first send, which is trying to get me one iteration ahead. Also there's a large part of synchronization cost. The master will do only the data management. Everything's independent. So this n array over which it is iterating the A array, is it's only doing half as many. It goes out and you eventually get a beep that says your transmission was OK. Or if it wasn't OK then, you know, you get a message that says, you know, something went wrong. The first thread is requesting ace of zero. Hand it to the initial processor and keep doing whatever? designed for applications that exploit tens of thousands of processors. Well, neither can make progress because somebody has to essentially drain that buffer before these receives can execute. So the 50 seconds now is reduced to 10 seconds. So in terms of communication, you know, if I have two operations and let's say -- this is a picture or schematic of what the MIT raw chip looks like. And then P1 eventually finishes and new work is allocated to the two different schemes. And what I've done here is I've parameterized where you're essentially starting in the array. So in distributed memory processors, to recap the previous lectures, you have n processors. In ray tracing what you do is you essentially have some camera source, some observer. Does that make sense? So what do you talk about [UNINTELLIGIBLE] because you might want to get the next data while you're computing now so that when I'm done I can start sending. 2.4-2.4.3 (pgs. You need everybody to calculate a new position before you can go on and calculate new kinds of coarse interactions. So here it's a similar kind of terminology. So in many numerical algorithms, you can actually use the broadcast and send to broadcast and reduce in place of sends and receives because it really improves the simplicity of your computation. Here's n elements to read from A. architecture, a notion of spatial locality. And if I have, you know, an addition that feeds into a shift, well, I can put the addition here and the shift there, but that means I have a really long path that I need to go to in terms of communicating that data value around. And things that appear in sort of this lightish pink will serve as sort of visual cues. And if it is then I can go on and actually do my work. I need both of these results before I can do this multiplication. And then I do another request for the next data item that I'm going to -- sorry, there's an m missing here -- I'm going to fetch data into a different buffer, right. I can just send, you know, in this case, one particular element. You know, I could fit it on one slide but you couldn't see it in the back. So this is orthogonal really to synchronous versus asynchronous. And given two processors, I can effectively get a 2x speedup. So the next lecture will be, you know, how do I actually parallelize my program? 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. And so you can, you know -- starting from the back of room, by the time you get to me, I only get two messages instead of n messages. And what's interesting about multicores is that they're essentially putting a lot more resources closer together on a chip. Description Parallel Programming: Concepts and Practice provides an upper level introduction to parallel programming. P2 is really fast so it's just zipping through things. And so you know, in Cell you do that using mailboxes in this case. productive way to express parallel computation. One is coverage, or in other words, how much parallelism do I actually have in my application? So you adjust your granularity. David says yes. But as I'll show -- well, you won't see until the next lecture -- there are dependencies, for example, that might preclude you from doing that. And so there's this law which is really a demonstration of diminishing returns, Amdahl's Law. The first one, your parallel pragma, I call the data parallel pragma, really says that you can execute as many of the following code block as there are processors or as many as you have thread contexts. And it often helps to have a standard because if everybody implements the same standard specification, that allows your code to be ported around from one architecture to the other. This concept module will introduce a core of parallel computing notions that CS majors and minors should know in preparation for the era of manycore computing, including parallelism categories, concurrency issues and solutions, and programming strategies. So now the next time I use ID, which is here, I'm trying to get the data. 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. Parallel programming is the key to Knights Landing. One is the volume. 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. 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. 216-241, 256-258), Chapter 3.1-3.2, 3.4, pgs. And so that's what the synchronization is doing here. You know, there needed to be ways to sort of address the spectrum of communication. And so now once every processor gets that message, they can start computing. In recent times, CPU clock speeds have stagnated andmanufacturers have shifted their focus to increasing core counts. MIT OpenCourseWare is a free & open publication of material from thousands of MIT courses, covering the entire MIT curriculum. And I have some work going to processor two. So it in fact assumes that the programmer knows what he's doing. What values are private? And the collective operations are things that are associative. And so a potential solution is, well, you actually increase your buffer length. So here you're sending all of A, all of B. PROFESSOR: Right. Parallel Programming: Concepts and Practiceprovides an upper level introduction to parallel programming. PROFESSOR: So this is an [? I need all those results before I do final substraction and produce my final result. So in this case you're essentially encapsulating this particular loop here. So this is an example of a data parallel computation. Yeah, yeah, sorry. concreting ?] The computation is done and you can move on. The for loop is also -- I can do this work sharing on it. And subtract -- sorry. 2 Terminology 2.1 Hardware Architecture Terminology Various concepts of computer architecture are defined in the following list. ... Concepts tested: multi-core architecture, data-parallel thinking, CUDA language semantics. So this is in contrast to what I'm going to focus on a lot more in the rest of our talk, which is distributed memory processors and programming for distributed memories. Leveraging multiple cores is easy for most serverapplications, where each thread can independently handle a separate clientrequest, but is harder on the desktop — because it typically requires that youtake your computationally intensive code … Or you have some network in between. A summary PDF file containing the course syllabus for the course can be found here. But you get no parallelism in this case. So what I've shown you with that first example was the concept of, or example of, data parallelism. 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. So parallel architectures won't really help you. You know, you put it in the mailbox. And you need things like atomicity and synchronization to be able to make sure that the sharing is done properly so that you don't get into data race situations where multiple processors try to update the same data element and you end up with erroneous results. And what does this really mean? There are things like all to all communication which would also help you in that sense. So the speedup can tend to 1 over 1 minus p in the limit. So it's one to several or one to many. So clearly as, you know, as you shrink your intervals, you can get more and more accurate measures of pi. Whereas, you know, you could have specified extra parameters that says, you know, I'm sending you A. And if we consider how the access patterns are going to be generated for this particular loop, well, in the sequential case I'm essentially generating them in sequence. 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. And then you may need some way of sort of synchronizing these different processors that say, I'm done, I can move on to the next computation steps. projects to express 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. Are you still confused? So it has to store it somewhere. and a final project. And this will feel a lot more like programming for the Cell as you get more and more involved in that and your projects get more intense. Essentially add up their numbers and forward me structured as lectures, you really... 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