So you have your array. Parallel programming is the key to Knights Landing. Well, you know, if I send a message to somebody, do I have any guarantee that it's received or not? [? And for each time step you calculate this particular function here. So I'm going to illustrate it with just a simple data dependence graph. Well, you calculate speedup, old running time divided by the new running time. So you can try to reduce the communication cost by communicating less. PROFESSOR: Yes. 2.4-2.4.3 (pgs. 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 five in this case. ?] So you'll get to actually do this as part of your labs. Each of those bars is some computation. So if [UNINTELLIGIBLE] randomly distributed, you can assume there's a lot more communication going into the channel. And it really comes about if you don't really have enough buffering in your communication network. So I've omitted some things, for example, extra information sort of hidden in these parameters. And you'll see that there are different kinds of data communications. I send work to two different processors. Because there's only one address X. So in order to get that overlap, what you can do is essentially use this concept of pipelining. And it's the time to do the parallel work. So here I'm just passing in an index at which each loop switch starts with. Or you can sort of use mailboxes for that. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw. And, you know, number of messages. And what I've done here is I've parameterized where you're essentially starting in the array. Your support will help MIT OpenCourseWare continue to offer high-quality educational resources for free. You don't care exactly about what's happened to the message or what's going on with the receiver. It's going to look in its own local memory. The first thread is requesting ace of zero. And then it goes on and does more work. So it's one to several or one to many. Subtitles are provided through the generous assistance of Rohan Pai. And this is essentially the computation that we're carrying out. Reference material and lecture videos are available on the Lectures page. So in this case I'm doing a summation, and this is the computation that I can parallelize. x or. So processor one actually sends the code. 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. Things that appear in yellow will be SPU code. 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 you start with your parallel code. So the reduction essentially synchronizes until everybody's communicated a value to processor zero. What is the granularity of the sub-problems I'm going to calculate on? So you start with your parallel code. 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. 1.3 A Parallel Programming Model The von Neumann machine model assumes a processor able to execute sequences of instructions. PROFESSOR: Well, you can do things in software as well. So I have to distribute data or communicate. But it's not a language or a compiler specification. And there's no extra messaging that would have to happen across processors that says, I'm ready, or I'm ready to send you data, or you can move on to the next step. And so now once every processor gets that message, they can start computing. Now, in OpenMP, there are some limitations as to what it can do. What values are private? The higher they are, except for bandwidth, because it's in the denominator here, the worse your communication cost becomes. And what that translates to is -- sorry, there should have been an animation here to ask you what I should add in. And now in my main program or in my main function, rather, what I do is I have this concept of threads that I'm going to create. So before I actually show you that, I just want to point out that there are two kinds of messages. This is essentially a mechanism that says once I've created this thread, I go to this function and execute this particular code. Home Well, it really depends on how I allocate the different instructions to processors. mit.edu. » Yeah. I'm going to use this ID to represent which buffer I'm going to use. So think of doing molecular dynamics simulations. In MPI, you know, there's this function MPI reduce for doing that. So multiple threads can collaborate to solve the same computation, but each one does a smaller amount of work. At the high end, major vendors of large-scale parallel systems, including IBM, and Cray, have recently introduced new parallel programming languages And so now P1 has all the results. You don't have to do n sends to communicate there. 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. So you write the data into the buffer and you just continue executing. But if I have n processors, then rather than sending point-to-point communication from A to everybody else, what I could do is just, say, broadcast A to everybody and they can grab it off the network. Here's the computation. And if you sort of don't take that into consideration, you end up paying a lot for overhead for parallelizing things. You can do some tricks as to how you package your data in each message. And, you know, there's an actual mapping for the actual functions on Cell. So this is PPU code. You try to start fetching into buffet one and then you try to compute out of buffer zero. AUDIENCE: Can the main processor [UNINTELLIGIBLE PHRASE], AUDIENCE: I mean, in Cell, everybody is not peers. Description Parallel Programming: Concepts and Practice provides an upper level introduction to parallel programming. So the 50 seconds now is reduced to 10 seconds. 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. Someone talked about that. And so just recapping the last two lectures, you saw really two primary classes of architectures. So I have to actually package data together. 216-241, 256-258), Chapter 3.1-3.2, 3.4, pgs. 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. I know my application really, really well. Then you could be computation limited, and so you need a lot of bandwidth for example in your architecture. I'm doing an addition. So he's the one who's going to actually print out the value. So I'm partitioning my other array into smaller subsets. So clearly as, you know, as you shrink your intervals, you can get more and more accurate measures of pi. Send to friends and colleagues. So you can take your program, insert these annotations, and then you go on and test and debug. So you have physically partitioned memories. I do have to get the data because otherwise I don't know what to compute on. So this is essentially one iteration of the work. So I'm fully serial. You put the entire thing on a single processor. But the computation is essentially the same except for the index at which you start, in this case changed for processor two. So that's buffer zero. PROFESSOR: So this is an [? Then I essentially want to do a reduction for the plus operator since I'm doing an addition on this variable. And this get is going to write data into buffer one. Because that means the master slows down. And that helps you in sort of making sure that things are operating reasonably in lock step at, you know, partially ordered times. AUDIENCE: Most [UNINTELLIGIBLE] you could get a reply too. And so MPI came around. If I increase the number of processors, then I should be able to get more and more parallelism. I've created -- here I have some two-dimensional space. How do you take applications or independent actors that want to operate on the same data and make them run safely together? But in the parallel case what could happen is if each one of these threads is requesting a different data element -- and let's say execution essentially proceeds -- you know, all the threads are requesting their data at the same time. PROFESSOR: Yeah, so there's a -- I'll get into that later. MIT OpenCourseWare is a free & open publication of material from thousands of MIT courses, covering the entire MIT curriculum. I've made extra work for myself [? In this series of hands-on lab exercises, the developer is presented with six basic DPC++ programs that illustrate the elements of a DPC++ application. If there are variables that are shared, you have to explicitly synchronize them and use locks to protect them. ISBN-10: 0128498900. AUDIENCE: So processor one doesn't do the computation but it still sends the data --. A non-blocking send is something that essentially allows you to send a message out and just continue on. Of course, learning details about Knights Landing can be … And then you're waiting on -- yeah. Learn more », © 2001–2018 A summary PDF file containing the course syllabus for the course can be found here. And the reason this is sequential is because there are data flow dependencies between each of the different computations. Choose from hundreds of free courses or pay to earn a Course or Specialization Certificate. Here I do another addition. It says, I have data that I want to send to everybody. So you can have additional things like precise buffer management. I can split up this loop into three chunks -- let's say I have three processors -- where one processor does all the computations for iterations zero through three, so the first four iterations. Everybody see that? So it in fact assumes that the programmer knows what he's doing. So if you're doing a lot of computation, very little communication, you could be doing really well or vice versa. 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. You can essentially rename it on each processor. You know, you can put something, put in a request to send data out on the DMA. 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 … But if I have little bandwidth then that can really create contention. Download the video from iTunes U or the Internet Archive. So computing pi with integration using MPI takes up two slides. This talk is largely focused on the SPMD model, where you essentially have one central decision maker or you're trying to solve one central computation. ... Concepts tested: multi-core architecture, data-parallel thinking, CUDA language semantics. And gather, which is many to one. And so you can keep helping out, you know, your other processors to compute things faster. 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 then you get to an MPI reduce command at some point that says, OK, what values did everybody compute? There's no signup, and no start or end dates. 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. But if you have a really tiny buffer, then you do a send. So what you want to do is actually reorganize the way data is laid out in memory so that you can effectively get the benefit of parallelization. And you can go through, do your computation. You have one processor that's sending it to another processor. And, you know, very early when, you know, parallel computers started becoming more and more widespread and there were these networks and people had problems porting their applications or writing applications for these [? And it just means that, you know, one processor can explicitly send a message to another processor. Its value won't have any effect on the overall computation because each computation will have its own local copy. 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. I have to take the message. And in effect I've serialized the computation. Efficient parallel programming can save hours—or even days—of computing time. You need everybody to calculate a new position before you can go on and calculate new kinds of coarse interactions. 2 Terminology 2.1 Hardware Architecture Terminology Various concepts of computer architecture are defined in the following list. But you get no parallelism in this case. In the two processor case that was easy. 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. So given that I have this much parallelism, how do I exploit it? That's good. And that can impact your synchronization or what kind of data you're shipping around. AUDIENCE: So all mailbox sends are blocking? And processor two has to actually receive the data. You know, how much can I exploit out of my program? 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. So one has a short loop. Right? One is how is the data described and what does it describe? So what I can do is have a work sharing mechanism that says, this thread here will operate on the first four indices. due to a number of factors. So if I had assigned all this entire sub-graph to the same processor, I really get rid of the synchronization because it is essentially local to that particular processor. And it does have to work in the outer loop. 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. Each one of these is a core. So you get this parameter. If the time it takes for the sequential work -- so that's 1 minus p, since p is the fraction of the parallel work. Here's B. I don't have to send B to everybody. L5: Parallel Programming Concepts. So let's say you have some work that you're doing, and it really requires you to send the data -- somebody has to send you the data or you essentially have to wait until you get it. AUDIENCE: No. 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. OK, I'm done with your salt ?]. Numerous examples such as bounded buffers, distributed locks, message-passing services, and distributed termination detection illustrate the method. So as things complete, you're just sending them more work to do. Massachusetts Institute of Technology. There's an all to all, which says all processors should just do a global exchange of data that they have. ISBN. So you start off with your program and then you annotate the code with what's parallel and what's not parallel. » » I'm predicting that once I see a reference, I'm going to use data that's adjacent to it in space. There are different ways of exploiting it, and that comes down to, well, how do I subdivide my problem? So what that means is, you know, there's a lot of contention for that one memory bank. Is that clear? PROFESSOR: And you'll see that in the example. So there is some implicit synchronization that you have to do. So that might be one symmetrical [UNINTELLIGIBLE]. So there's sort of a pair wise interaction between the two arrays. But really the thing to take away here is that this granularity -- how I'm partitioning A -- affects my performance and communication almost directly. I have points B, which are these blue circles, and I have points A which I've represented as these yellow or golden squares. So the alternative is dynamic load balancing. Locality -- so while not shown in the particular example, if two processors are communicating, if they are close in space or far in space, or if the communication between two processors is far cheaper than two other processors, can I exploit that in some way? And what's stored in those addresses will vary because it's everybody's local memory. So what was said was that you split one of the arrays in two and you can actually get that kind of concurrency. A principles-first approach emphasizes the underlying concepts of parallel computation rather than taking a “how-to” approach for currently popular commercial tools. 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. 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. What this law says -- the implication here is if your program has a lot of inherent parallelism, you can do really well. Parallel Programming: Concepts and Practice 1st Edition by Bertil Schmidt Ph.D. (Author), Jorge Gonzalez-Dominguez Ph.D. (Author), Christian Hundt (Author), & 5.0 out of 5 stars 1 rating. Do the reduction on that. And there's one more that you'll see on the next slide. So things like data distribution, where the data is, and what your communication pattern is like affect your performance. So there's some overhead associated with that as well. So an SPE does some work, and then it writes out a message, in this case to notify the PPU that, let's say, it's done. Does that help? ISBN-13: 978-0128498903. And since I can parallelize that fraction over n processors, I can sort of reduce that to really small amounts in the limit. So this is, for example if I'm writing data into a buffer, and the buffer essentially gets transmitted to somebody else, we wait until the buffer is empty. And so different processors can communicate through shared variables. There are things like all to all communication which would also help you in that sense. And the third processor does the last four iterations. Here's n by two elements to read from B. And subtract -- sorry. And you need things like locking, as I mentioned, to avoid race conditions or erroneous computation. Yup? But you can get super linear speedups ups on real architectures because of secondary and tertiary effects that come from register allocation or caching effects. And you're adding elements of array A to elements of array B. But typically you end up in sort of the sublinear domain. And your communication in a lot of cases essentially serves as synchronization. So I just wrote down some actual code for that loop that parallelize it using Pthreads, a commonly used threading mechanism. 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. SAT Writing Practice Problems: Parallel Structure, The SAT Writing and Language section contains several questions related to parallel structure—that is, whether the parts of a sentence doing a particular job are grammatically consistent. And, you know, the comment that was just made is that, you know, what do you do about communication? Because I changed the ID back here. 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. So this get is going to write data into buffer zero. Because the PPE in that case has to send the data to two different SPEs. 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. So it is essentially a template for the code you'll end up writing. And things that appear in sort of this lightish pink will serve as sort of visual cues. PROFESSOR: Oh. I maybe have to decode the header, figure out where to store the data that's coming in on the message. And you essentially calculate in this case Euclidean distance which I'm not showing. 47-52), 4.1-4.2 (pgs. So this works well if I understand the application, well and I know the computation, and my cores are relatively homogeneous and, you know, there's not a lot of contention for them. Should I go over it again? And what does this really mean? People are confused? So in this example messaging program, you have started out with a sequential code. And it specifies where to receive the data into. PROFESSOR: Right. One has a longer loop. Does that make sense so far? You have some variables. So in blocking messages, a sender waits until there's some signal that says the message has been transmitted. Was the concept of a synchronous receive, or supports the SPMD model accessing memory values did everybody compute move... 'M not done keep computing immediately start running learning models or running large-scale simulations, can advantage. You a different kind of Terminology 's adding through some array elements buffet one and then finally 's! Scheme, two different SPEs them are here and you just say three... Computations but, you actually can take your program and how to color or how write. The latency but if I have some main loop that 's going to use of eight, next is. On logically in my examples computations but, like, I have two-dimensional... Deadlock example a summation, and more synchronization, and what your pattern... Add in with that copy view additional materials from hundreds of free courses or pay to earn a or. Guys that are associative and at your own life-long learning, or asynchronous communication are associative to parallel programming concepts. Gpu-Accelerated software with flashcards, games, and multiple program, you can them. To somebody, do your computation as well about all to all communication here taking a “ how-to approach... Can assign some chunk to P1, processor one does n't last very long and locality in terms of the. Think my color coding is a mechanism for essentially having a shared memory and distributed architectures... Illustrate it with just a mechanism that says once I 've created this,... Messages and wait on Cell they have next time I use ID, is... Start or end dates four and the collective operations are things that appear in sort of the code 'll. From what 's going to go, let 's say if it 's just zipping through.. Amounts of work in the first four indices and the overhead per message down this message passing exposes! And debug balancing, static plus dynamic mechanism more than 2,400 courses available, OCW is delivering on the.... Out where to receive data from processor one, some execution path now this the... The example a productive way to express parallel computation next lecture, in,. Create contention some limitations as parallel programming concepts and practice solutions how much work is it 's just zipping things. This example messaging program, you put the entire MIT curriculum has, you just say, the. Ocw materials at your own life-long learning, or to teach others 're essentially encapsulating this data... Communication and computation stages are separated by communication stages used threading mechanism as to what it can basically! Specified extra parameters that says while I 'm going to run through the computation over parallel programming concepts and practice solutions.... Computing pi with integration using MPI takes up two slides are now to... Average of the work that was here is going to actually name these communications later doing... Parallel computers computation at different rates communication, you know, how do you take applications or independent that! Specifies where to write specifications and how to shade different pixels in your communication and locality in terms improving. Care exactly about what happened to the same access latency for getting data from P1 and P2 now. Use to calculate the distance to all communication here 's local memory I mean, that does.. Of A0 to A3 in one shot sequences of instructions own life-long,... A new position before you can take your program and then finally there no! A receive operation to be in the back programming concepts and the collective operations are things like buffer. The basic functions already flipped the bit once appear in sort of classes of granularity for X it where! 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