Midnight Tiger

Chapel Cray

Team Information

1. Taeyang Chung

Introduction to Chapel

Chapel(Cascade High-Productivity Language) is an alternative parallel programming language that focuses on the productivity of high-end computing systems.

The concept of "Productivity" is somewhat more special that we might think. There are three categories of people, interested in Parallel Programming.

* Student:I want something that is similar to the languages that I learned in school such as c++, c, and Java. I want it to be easy to implement the parallel programming on my code.

* HPC Programmers:I want the full control that gives me more spot to increase the performance.

* Computational Scientists:I want something that I can easily implement my computation without knowing much architecture knowledge.

Chapel: Chapel is the language that easy to implement the parallel computation that similar to other languages with granting full control to the users.

Advantages of Chapel Cray

Other current parallel programming models are limited that they target to specific hardware and they only implement single type of parallelism.

• General Parallelism: Chapel has the goal of supporting any parallel algorithm you can conceive of on any parallel hardware you want to target. In particular, you should never hit a point where you think “Well, that was fun while it lasted, but now that I want to do x, I’d better go back to MPI.”
• Separation of Parallelism and Locality: Chapel supports distinct concepts for describing parallelism (“These things should run concurrently”) from locality (“This should be placed here; that should be placed over there”). This is in sharp contrast to conventional approaches that either conflate the two concepts or ignore locality altogether.
• Multiresolution Design: Chapel is designed to support programming at higher or lower levels, as required by the programmer. Moreover, higher-level features—like data distributions or parallel loop schedules—may be specified by advanced programmers within the language.
• Productivity Features: In addition to all of its features designed for supercomputers, Chapel also includes a number of sequential language features designed for productive programming. Examples include type inference, iterator functions, object-oriented programming, and a rich set of array types. The result combines productivity features as in Python™, Matlab®, or Java™ software with optimization opportunities as in Fortran or C.

Installation Process

• Download chapel-1.12.0.tar.gz on Chapel Hompage

• On commandline, type tar xzf chapel-1.12.0.tar.gz

>tar xzf chapel-1.12.0.tar.gz

• Go to the chapel folder

>cd $CHPL_HOME

• Type gmake or make on command line to build the compiler

>gmake

or

>make

• Set up environment variable (http://chapel.cray.com/docs/1.12/usingchapel/chplenv.html)

How to compile sample code

• How to compile

>chpl -o execution_name file_name

• How to run

>./execution_name

• How to run with the configuration of const variable

>./execution_name --variable_name=value

• Chapel offers some example codes in examples folder. Use one of the files to test.

>cd $CHPL_HOME/examples

How to parallelize your code using Chapel Cray

Iterations in a loop will be executed in parallel.

• forall: At the beginning of the first iteration all the threads will be created.

• Sample Parallel Code using forall

config const n = 10;
forall i in 1..n do
   writeln("Iteration #", i," is executed.");    

• Sample Parallel Code Output using forall

• coforall: A thread will be created at each iteration. It's recommended to use coforall when the inside of loop is big and the iteration size is equal to the total number of logical cores.

• Sample Parallel Code using coforall

config const n = here.numCores;
corforall i in 1..n do
   writeln("Thread #", i,"of ", n);    

• Sample Parallel Code Output using coforall