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# [mailto:xweng11@myseneca.ca?subject=GPU621 Xi Weng]
# [mailto:hbhuang2@myseneca.ca?subject=GPU621 Ivan Huang]
# [mailto:yli593@myseneca.ca?subject=GPU621 Yu (Jackie) Li]
# [mailto:xweng11@myseneca.ca;hbhuang2@myseneca.ca;yli593@myseneca.ca?subject=GPU621 eMail All]
=== Language Basics ===
==== Variable: Variables ====
config var myVar2myVar4: bool = false; //./variable --myVariable=true
==== Procedures: ====
A procedure is like a function in C++ that can be used or called inside the program.
proc factorialplus(x: int) : int
{
}
==== Classes: ====
A class is a type of collection that collects ''variables'', ''procedures'' and ''iterators''. We use '''class''' keyword to declare a new class typeand use '''new''' keyword to create its instance.
class myClass {
}
}
var foo = new myClass(0, 1);
foo.print();
==== Records: ====
Records are similar to classes, . '''Records''' doesn't support inheritance and virtual dispatch but there it supports copy constructor and copy assignment. And record variables refer to a distinct memory. === Iterators === Iterators are several important differencesdeclared like procedure in Chapel. Unlike processes, iterators typically return a sequence of values. We declared the '''yield''' statement inside the iterator bodies, it returns a value and suspends the execution. iter fibonacci(n: int):int { var i1 = 0, i2 = 1; for i in 1..#n { yield i1; var i3 = i1 + i2; i1 = i2; i2 = i3; } } config const n =10; for idVar in fibonacci(n) do writeln(" - ", idVar); ==== Parallel Iterators ====
var i1 = 0, i2 = 1;
yield i1;
var i3 = i1 + i2;
i1 = i2;
i2 = i3;
}
}
config const n =10;
for (i,idVar) in zip(0..n, fibonacci(n)) do
writeln("#", i, " - ", idVar);
===== Leader-follower iterator =====
forall (a, b, c) in zip(A, B, C) do
//in here, A is the leader; and A, B, C is followers
'''leader iterator''' will create the parallel tasks for its forall loop and set iterations to each parallel task.
'''follower iterator''' will take input and iterate over it; create opposite results in order.
=== Task Parallelism ===
==== basic ====
Three basic parallel features:
'''begin:''' it creates a independent thread. Unable to confirm who will execute first.
'''cobeign:''' it creates a block of tasks. The main thread will continue until the statements inside are finished.
'''coforall:''' it is a loop of cobegin syntax in which each iteration is a separate task. The main thread will also continue until the loop is finished. The order of execution within each iteration is determined, and the order of iteration will execute first is not determined.
An example code:
const numTasks = here.numPUs();
cobegin {
writeln("Hello from task 1 of 2");
writeln("Hello from task 2 of 2");
}
coforall tid in 1..numTasks do
writef("Hello from task %n of %n\n", tid, numTasks);
==== Sync/Single ====
Sync and single are type qualifiers that end with '''$''' when you declare it.
They have an associated state (full/empty); when you declare it with an initializing expression, the statement will be ''full''; otherwise it is ''empty'' and the value of that variable is the default value of its type.
'''empty''' is the only state that you can write a value to it, and after writing it changes to '''full'''.
'''full''' is the only state that you can read the value from a variable; After reading, '''sync''' variable will change to '''empty''' and '''single''' variable doesn't change.
===== Methods for sync/single variables: =====
* For both sync and single variables:
** '''''isFull''''' - return '''true''' if variable's statement is ''full''.
** '''''writeEF()''''' - it will block until variable's statement is ''empty'', then set the argument value.
** '''''readFF()''''' - it will block until variable's statement is ''full'', then read the value and return the '''value'''. sync and single variable cannot directly output, use this method to output the value.
** '''''readXX()''''' - return the '''value''' regardless variable's statement.
* For sync variables only:
** '''''reset()''''' - set the value to its type's default value and set statement to '''empty'''.
** '''''readFE()''''' - it will block until variable's statement is ''full'', then read the value and return the '''value'''. using this method to output the value.
** '''''writeXF()''''' - set the argument value without block the execution.
** '''''writeFF()''''' - it will block until variable's statement is ''full'', then set the argument value without change the state.
An example code:
var sy$: sync int = 2;
var getSy = sy$;
writeln("Default value: ", getSy);
var done$: sync bool;
writeln("Create thread");
begin {
var getNewsy = sy$;
writeln("In child thread, sy=", getNewsy);
done$ = true;
}
writeln("Thread created");
var result = done$.readXX();
writeln("Thread finished? ", result);
sy$ = 20;
writeln("Thread finished? ", done$.readFE());
=== Locality ===
The built-in type locale is used to represent locales in Chapel. When a task is trying to access a variable within the same locale, the cost is less compared to accessing a variable from another locale.
'''numLocales:''' a built-in variable which returns the number of locales for the current program as an integer.
'''Locales array:''' a built-in array, which stores the locale values for the program as elements in the array. The ID of each locale value is its index in the Locales array.
'''Here:''' a built-in variable which returns the locale value that the current task is running on. In Chapel, all programs begin execution from locale #0.
=== Data Parallelism ===
'''Forall-loop:''' important concept in Chapel for data parallelism. Like the coforall-loops, the forall-loop is also a parallel version of a for-loop in Chapel. The motivation behind forall-loops is to be able to handle a large number of iterations without having to create a task for every iteration like the coforall-loop.
const n = 1000000;
var iterations: [1..n] real;
Each task will be responsible for a single iteration. Significant performance hit caused by creating, scheduling, and destroying each individual task which will outweigh the benefits of each task handling little computation.
coforall it in iterations do {
it += 1.0;
}
Automatically creates an appropriate number of tasks specific to each system and assigns an equal amount of loop iterations to each task. The number of tasks created is generally based on the number of cores that the system’s processor has. For example: a quad-core processor running this program with 1,000,000 iterations would cause 4 tasks to be created and assign 250,000 iterations to each individual task.
forall it in iterations do {
it += 1.0;
}
[[File:Chapel_loops.png]]
=== Library Utilities ===
'''Timer:''' a timer is part of the time module which can be imported using the following statement: use Time; /* Create a Timer t */ var t: Timer; t.start(); writeln(“Operation start”); sleep(5); writeln(“Operation end”); t.stop(); /* return time in milliseconds that was recorded by the timer */ writeln(t.elapsed(TimeUnits.milliseconds)); t.clear(); '''List Module''' '''List:''' the list type can be imported using the following statement: use List; var new_list: list(int) = 1..5; writeln(new_list); '''Output: [1, 2, 3, 4, 5]''' for i in 6..10 do { new_list.append(i); } writeln(new_list); '''Output: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]''' /* parSafe needs to be set to true if the list is going to be used in parallel */ var new_list2: list(int, parSafe=true); forall i in 0..5 with (ref new_list2) { new_list2.append(i); } writeln(new_list2); '''Output: [0, 1, 2, 3, 4, 5]''' '''sort():''' used to sort the list in ascending order. '''pop(index):''' used to pop the element at the specified index. '''clear():''' used to clear all elements from the list. '''indexOf(value):''' used to retrieve the index of the value specified, returns -1 if not found. '''insert(index, value):''' used to insert the value at the specified index. == Pros Comparison to OpenMP == // Chapel is short and Cons of Using The concise. // OpenMP code for(i = 0 ; i<niter; i++) { start_time(); #pragma omp parallel for for(…) {} } stop_time(); // Chapel code for i in 1..niter { start_time(); forall .. {} } stop_time(); } //Hello World OpenMP #include <iostream> #include <omp.h> #include <chrono> using namespace std::chrono; // report system time void reportTime(const char* msg, steady_clock::duration span) { auto ms = duration_cast<milliseconds>(span); std::cout << msg << " - took - " << ms.count() << " milliseconds" << std::endl; } int main() { steady_clock::time_point ts, te; ts = steady_clock::now(); #pragma omp parallel { int tid =omp_get_thread_num(); int nt = omp_get_num_threads(); printf("Hello from task %d of %d \n", tid, not); } te =steady_clock::now(); reportTime("Integration", te - ts); }
== temp =Cons===* lack of a native Windows version.* Adoption, hard to gain user compare to another parallel programming platform.* User base is very small. * It certainly will not be used in the IT industry for the near future(or ever) unless you are opening your own company and decided to try Chapel.* a small number of contributors to this open-source project. Much less support and update compare to other IT giants.* Projects based on Chapel is very little.* It's more for research projects than products. * the package is easy to install, but not as easy as other tools like OpenMP/MPI.* there’s no central place where other people could look for your work if you wanted to have it as an external package.* users have no much reason to start trying the language, given better options like C++ & OpenMP/MPI.
= References =
* The Chapel Overview Talk Video: https://youtu.be/ko11tLuchvg
* The Chapel Overview Talk Slide: https://chapel-lang.org/presentations/ChapelForHPCKM-presented.pdf
* Comparative Performance and Optimization of Chapel: https://chapel-lang.org/CHIUW/2017/kayraklioglu-slides.pdf
* The Parallel Research Kernels: https://www.nas.nasa.gov/assets/pdf/ams/2016/AMS_20161013_VanDerWijngaart.pdf