And then you have a step that essentially tells you how many times you're going to do this computation. So that's still going to take 25 seconds. Courses But, like, I don't see [INAUDIBLE]. In addition to covering general parallelism concepts, this text teaches practical programming skills for both shared memory and distributed memory architectures. PROFESSOR: Right. And there's, you know, you can think of a synchronous send and a synchronous receive, or asynchronous communication. And then after you've waited and the data is there, you can actually go on and do your work. Well, if you gave me four processors, I can maybe get done four times as fast. And there is really four questions that you essentially have to go through. And what's interesting about multicores is that they're essentially putting a lot more resources closer together on a chip. By dividing up the work I can get done in half the time. 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. So I just wrote down some actual code for that loop that parallelize it using Pthreads, a commonly used threading mechanism. And you're trying to figure out, you know, how to color or how to shade different pixels in your screen. 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 typically you end up in sort of the sublinear domain. And if one processor asks for the value stored at address X, everybody knows where it'll go look. And so you know, in Cell you do that using mailboxes in this case. AUDIENCE: So processor one doesn't do the computation but it still sends the data --. PROFESSOR: So in terms of tracing, processor one sends the data and then can immediately start executing its code, right? The method also covers how to write specifications and how to use them. So I send the first array elements, and then I send half of the other elements that I want the calculations done for. That was really examples of point-to-point communication. Concepts for Concurrent Programming Fred B. Schneider 1 Department of Computer Science Cornell University Ithaca, New York, U.S.A 14853 Gregory R. Andrews 2 Department of Computer Science University of Arizona Tucson, Arizona, U.S.A. 85721 Abstract. Each processor has local memory. So depending on how I partition, I can really get really good overlap. And processor two has to actually receive the data. And subtract -- sorry. and software, due 11:59PM, Thurs., Dec. 13. So let's say I have processor one and processor two and they're trying to send messages to each other. I can fetch all the elements of A4 to A7 in one shot. So how do I identify that processor one is sending me this data? Because that means the master slows down. It should be more, right? So there's dynamic parallelism in this particular example. Because the PPE in that case has to send the data to two different SPEs. I send work to two different processors. The computation is done and you can move on. I don't have the actual data. > 213- Concepts of Programming Languages ,7ed,by Robert Sebesta > 214- Principles of Macroeconomics ,u/e,N. And this instruction here essentially flips the bit. OK. But it's not a language or a compiler specification. There's a cost induced per contention. Your support will help MIT OpenCourseWare continue to offer high-quality educational resources for free. If I increase the number of processors, then I should be able to get more and more parallelism. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw. And if I'm receiving data how do I know who I'm receiving it from? And so different processors can communicate through shared variables. I can split it up. And then once all the threads have started running, I can essentially just exit the program because I've completed. And then computation can go on. And that allows me to essentially improve performance because I overlap communication. 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. Does that make sense? 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. You put the entire thing on a single processor. the threads. Now you get it? And then you can finalize, which actually makes sure the computation can exit. 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. If there's a lot of contention for some resources, then that can affect the static load balancing. And lastly, what I'm going to talk about in a couple of slides is, well, I can also improve it using some mechanisms that try to increase the overlap between messages. So this is the actual code or computation that we want to carry out. Hand it to the initial processor and keep doing whatever? So it in fact assumes that the programmer knows what he's doing. Why is ISBN important? This isproblematic for us as programmers because our standard single-threaded codewill not automatically run faster as a result of those extra cores. So the last concept in terms of understanding performance for parallelism is this notion of locality. Because there's only one address X. And there are different kinds of communication patterns. And so in this case I have a work queue. So in blocking messages, a sender waits until there's some signal that says the message has been transmitted. 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. One is how is the data described and what does it describe? So there is some implicit synchronization that you have to do. So if there's a lot of congestion on your road or, you know, there are posted speed limits or some other mechanism, you really can't exploit all the speed of your car. And then if I have n processors, then what I might do is distribute the m's in a round robin manner to each of the different processes. Then I essentially want to do a reduction for the plus operator since I'm doing an addition on this variable. An example of a blocking send on Cell -- allows you to use mailboxes. Now, in OpenMP, there are some limitations as to what it can do. PROFESSOR: Right. ), Learn more at Get Started with MIT OpenCourseWare, MIT OpenCourseWare makes the materials used in the teaching of almost all of MIT's subjects available on the Web, free of charge. So there's sort of a pair wise interaction between the two arrays. Introduction to parallel algorithms and correctness (ppt), Parallel Computing Platforms, Memory Systems and Models of Execution (ppt), Memory Systems and Introduction to Shared Memory Programming (ppt), Implementing Domain Decompositions in OpenMP, Breaking Dependences, and Introduction to Task Parallelism (ppt), Course Retrospective and Future Directions for Parallel Computing (ppt), OpenMP, Pthreads and Parallelism Overhead/Granularity, Sparse Matrix Vector Multiplication in CUDA, (Dense matvec CUDA code: dense_matvec.cu), MEB 3466; Mondays, 11:00-11:30 AM; Thursdays, 10:45-11:15 AM or by appointment, Ch. came, ?] And then you're waiting on -- yeah. So imagine there's a one here. And there's locality in your communication and locality in your computation. So assume more than that. Learn parallel programming faster and at your own pace. So I'll get into that a little bit later. I don't know if that's a reasonably long time or a short time. And it works reasonably well for parallel architectures for clusters, heterogeneous multicores, homogeneous multicores. So here you're sending all of A, all of B. So there's a question of, well, how do I know if my data actually got sent? AUDIENCE: Most [UNINTELLIGIBLE] you could get a reply too. So what are some synchronization points here? There's an all to all, which says all processors should just do a global exchange of data that they have. and providing context with a small set of parallel algorithms. And you can't quite do scatters and gathers. And so you get the performance benefits. So this really is an overview of sort of the parallel programming concepts and the performance implications. And then I start working. AUDIENCE: [UNINTELLIGIBLE PHRASE] two things in that overhead part. Is that clear so far? Explore our catalog of online degrees, certificates, Specializations, & MOOCs in data science, computer science, business, health, and dozens of other topics. architecture, a notion of spatial locality. This is one. So clearly as, you know, as you shrink your intervals, you can get more and more accurate measures of pi. And I can't do anything about this sequential work either. Most significantly, the advent of multi-core Now what happens here is there's processor ID zero which I'm going to consider the master. So communication factors really change. And I'm sending those to each of the different processors. 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. You know, there needed to be ways to sort of address the spectrum of communication. And in effect I've serialized the computation. And what's going to happen is each processor is going to run through the computation at different rates. Things that appear in yellow will be SPU code. Learn vocabulary, terms, and more with flashcards, games, and other study tools. Download the video from iTunes U or the Internet Archive. Most shared memory architectures are non-uniform, also known as NUMA architecture. Then you could be computation limited, and so you need a lot of bandwidth for example in your architecture. And so now P1 has all the results. Yup? So these are the computations here and these yellow bars are the synchronization points. So that takes me some work on the receiver side. And the programmer is largely responsible for getting the synchronization right, or that if they're sharing that they get those dependencies protected correctly. So what do I mean by that? A non-blocking send is something that essentially allows you to send a message out and just continue on. You can have some collective operations. 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 what you are essentially trying to do is break up the communication and computation into different stages and then figure out a way to overlap them so that you can essentially hide the latency for the sends and the receives. So I have sequential parts and parallel parts. There's no real issues with races or deadlocks. "At the highest level, we're looking at 'scaling out' (vs. 'scaling up,' as in frequency), with multicore architecture. So if you look at the fraction of work in your application that's parallel, that's p. And your number of processors, well, your speedup is -- let's say the old running time is just one unit of work. Well, it really depends on how I allocate the different instructions to processors. I maybe have to decode the header, figure out where to store the data that's coming in on the message. So an example of sort of a non-blocking send and wait on Cell is using the DMAs to ship data out. And given two processors, I can effectively get a 2x speedup. PROFESSOR: Yeah, we'll get into that later. There are things like all to all communication which would also help you in that sense. And what you could do is you can have a process -- this is a single application that forks off or creates what are commonly called the threads. Parallel Programming: Concepts and Practice provides an upper level introduction to parallel programming. And in a shared memory processor, since you're communicating -- since there's only a single memory, really you don't need to do anything special about the data in this particular example, because everybody knows where to go look for it. Freely browse and use OCW materials at your own pace. So the coarse-grain, fine-grain grain parallelism granularity issue comes to play. There will be other HPC training sessions discussing MPI and OpenMP in more detail. But you get no parallelism in this case. Code segments for sections within the book Operating System Concepts.Also includes solutions to exercises and some special … So they can hide a lot of latency or you can take advantage of a lot of pipelining mechanisms in the architecture to get super linear speedups. Send to friends and colleagues. So that might be one symmetrical [UNINTELLIGIBLE]. Just to give you a little bit of flavor for, you know, the complexity of -- the simple loop that we had expands to a lot more code in this case. If you have a lot of buffering, then you may never see the deadlock. So control messages essentially say, I'm done, or I'm ready to go, or is there any work for me to do? Description Parallel Programming: Concepts and Practice provides an upper level introduction to parallel programming. Learn about condition variables, semaphores, barriers, thread pools, and more. And that can impact your synchronization or what kind of data you're shipping around. And what that means is that somebody has read it on the other end or somebody has drained that buffer from somewhere else. And I understand sort of different computations. So you pointed this out. AUDIENCE: Also there are volume issues. Everybody has data that needs to essentially get to the same point. If your algorithm is sequential, then there's really nothing you can do for programming for performance using parallel architectures. This is essentially a mechanism that says once I've created this thread, I go to this function and execute this particular code. Are all messages the same? Does that make sense so far? This is one of over 2,200 courses on OCW. So you've seen pipelining in superscalar. Made for sharing. David says yes. So it has to store it somewhere. So you adjust your granularity. 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