Future Trends in Computing X10 Basics Distributed Programming

Transcription

1 Future Trends in Computing X10 Basics Distributed Programming Alexander Pöppl, Emily Ao Mo-Hellenbrand Chair of Scientific Computing in Computer Science

2 X10 Intro Days Agenda: Day 1 10:00 Setup & Hello world 11:00 Part 1: Basic syntax introduction Exercise: sequential Quick Sort 13:00 Lunch Break 14:00 Part 2: Parallel features Exercise: Parallel Monte Carlo Pi Exercise: Parallel Heat Simulation Day 2 10:00 Part 3: PGAS features Exercise: Distributed Monte Carlo Pi Exercise: Distributed Sort 12:00 Lunch Break 13:00 Part 4: Advanced features Exercise: Distributed Heat Simulation

3 Recollection Sequential Programming in X10 - vars vs. vals - Type System - Arrays Parallel Programming in X10 - Activities (async) - Waiting on children - Atomic Statements - Clocks 3

4 Towards PGAS 4

5 Places An X10 place is a repository for data and activities, corresponding loosely to a process or a processor. Places induce a concept of local. The activities running in a place may access data items located at that place with the efficiency of on-chip access. Accesses to remote places may take orders of magnitude longer. (X10 Language Reference) 5

6 Shifting Places One may perform a Place-Shift using the at(p) S statement: f(); at (Place(1)) { g(); h(); Or use the at(p) E expression to compute a value remotely: f(); val x = at (Place(1)) { return g() + 3; h(x); 6

7 Shifting Places Example val a = [1,2,3,4,5,6]; val world = Place.places(); at(world.next(here)) { a(1) = 4; //a == [1,4,3] //a == [1,2,3] [1, 2, 3] new a(1)=4; [1, 2, 4, 3] 7

8 Shifting Places Data As a default, every object that is being referenced in the Block of the at statement needs to be copied deeply to the other place. Deep copying implies copying the object itself and recursively copying every object referenced by it. This copy is independent from the original version. What is captured in methoda, methodb and methodc? Name every copied object. Beware of capturing this! public class Capture { public val data = new Array_2(1024, 1024, 0.0); public val name : String; public def this(name:string) { this.name = name; public def speak(text:string) { Console.OUT.println(text); public def methoda() { val x = 2*3* data(3,5); at (Place(1)) { val y = 3*x; Console.OUT.println("i say " + y); public def methodb() { at (Place(1)) { Console.OUT.println(name); public def methodc() { val x = 2*3* data(3,5); at (Place(1)) { val y = 3*x; speak("i say " +y); 8

9 Global References Sometimes, only a reference to an object is needed remotely - The struct GlobalRef[T] enables this by holding an opaque reference to an object together with the place it is located at. - May be dereferenced only if ref.home == here using ref(). - Get a copy if not if ref references a remote object using ref.getlocalorcopy() - Execute code at the home place of the object referenced by ref using ref.evalathome((t)=>u) [1, 2, 3] ref ref 9

10 Place Shifts Caveats - Unintentional capturing of objects. Everything you reference is captured and copied. This includes the this, if an attribute is used or an instance method is called. - Place shifting to same place still creates copies. - Be wary of copies. When place-shifting, it is easy to end up manipulating temporary copies of the data. val a = [1,2,3]; val home = here; Console.OUT.println(a); // [1,2,3] at (Place(1)) { val partialresult = complexcomputation(); at (home) { a(0) = partialresult; Console.OUT.println(a); // [0,2,3] anothercomputation(); Console.OUT.println(a); // [1,2,3] Console.OUT.println(a); // [1,2,3] 10

11 Exercise: Distributed Monte Carlo Pi (r > 1)? Inside : Outside Pi = # samples inside Total # samples r 11

12 Exercise: Monte Carlo Pi Modify the MontyPi class from yesterday, such that your program can be executed on a distributed-memory architecture. Your program should feature hybrid parallelism, i.e., it makes use of multiple CPU cores as well as multiple threads within each core. 12