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OpenMP and
the PGAS Model
CMSC714 – Sept 15, 2015
Guest Lecturer: Ray Chen
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Last Time: Message Passing
• Natural model for distributed-memory systems
– Remote (“far”) memory must be retrieved before use
– Programmer responsible for specifying:
• Participants (Single peer, collective communication, etc.)
• Data types (MPI_CHAR, MPI_DOUBLE, etc.)
• Logical synchronization
• How about shared-memory systems?
– All processors can directly access any memory
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OpenMP: Open Multi-Processing
• Portable interface for multiple processor systems
– Agnostic to architecture or language
• Alternative to POSIX threads
– Thread management too low-level for HPC applications
– Task parallelism vs. data parallelism
• Not a language
– Extends existing language (C/C++/Fortran)
– Compiler responsible for low-level thread management
– Allows for incremental approach to parallelism
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OpenMP Fork/Join Model
• Single master thread executes until parallel region
• When a parallel region is reached
– At start, N-1 additional threads are spawned (Fork)
– All N threads execute the same code
• Different code paths achieved through unique thread ID
– At end, threads are synchronized via barrier (Join)
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Based on image by A1 licensed under CC BY 3.0
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OpenMP Syntax
• Compiler directives
– Express parallel structure of code
– Clauses modify directives
• Run-time library routines
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#pragma omp <directive> <clauses>
{
// Code region affected by directive.
}
#include <omp.h>
…
int id = omp_get_thread_num();
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OpenMP parallel Directive
• Begin a new parallel region
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#include <stdio.h>
#include <omp.h>
int main(int argc, char* argv[])
{
#pragma omp parallel
{
printf(“Thread %d of %d.\n”,
omp_get_thread_num(),
omp_get_num_threads());
}
return 0;
}
$ gcc -fopenmp a.c
$ ./a.out
Thread 0 of 4
Thread 3 of 4
Thread 2 of 4
Thread 1 of 4
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OpenMP Data Environment
• All variables have a data-sharing attribute– Shared: all threads use the same copy
– Private: all threads use local storage for this variable
– Firstprivate: Like private, but value is initialized on fork
– Lastprivate: Like private, but value updated on join
• In general:– Variables defined inside a parallel region are private
– Variables defined outside a parallel region are shared
– Not always true: more details available in the spec
• Data-sharing attributes can be overridden
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OpenMP Worksharing Constructs
• Used to parallelize loops
– HPC programs contain computationally intensive loops
• Restrictions
– Loop iterations must be independent
– Strict rules for loop initialization, test, and increment
– Premature termination of loops not supported
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#pragma omp loop
for (i = 0; i < MAX; ++i) {
…
}
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OpenMP Example: Finding Primes
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#include <stdio.h>
#include <stdlib.h>
int main(int argc, char* argv[])
{
int cap = atoi(argv[1]);
int count = 0;
int num = 0;
#pragma omp parallel loop reduction(max:num) schedule(dynamic)
for (int i = 0; i < cap; ++i) {
int prime = 1;
for (int j = 0; prime && j * j <= i; ++j)
prime = i % j;
if (prime) {
#pragma omp atomic
++count;
num = i;
}
}
printf(“Prime #%d is %d\n”, count, num);
return 0;
}
Keep a private copy of num for each thread, and copy
the maximum value back when the loop is finished.
Parallelize the following loop.
Use a work queue to
load-balance threads.
Synchronize the next line; count is a shared variable.
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OpenMP Synchronization
Threads pause here until all reach this point
Threads will execute one at a time
Only one thread will ever executeOther threads wait in an implied barrier at end of region
Only the master thread will executeOther threads skip the region without waiting
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#pragma omp barrier
#pragma omp critical
#pragma omp single
#pragma omp master
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OpenMP Sync Puzzles
• Puzzle #1
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int x = 1;
#pragma omp parallel num_threads(2)
{
#pragma omp single
{
++x;
}
printf(“%d\n”, x);
}
2
2
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OpenMP Sync Puzzles
• Puzzle #2
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int x = 1;
#pragma omp parallel num_threads(2)
{
#pragma omp master
{
++x;
}
printf(“%d\n”, x);
}
1
2
2
2
or
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OpenMP Sync Puzzles
• Puzzle #3
access is not automatically
Shared variable
access is not automatically
synchronized.
Behavior is undefined.
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int x = 1;
#pragma omp parallel num_threads(2)
{
++x;
#pragma omp barrier
printf(“%d\n”, x);
}
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Limitations of OpenMP
• Not fool-proof
– Data dependencies, conflicts, race conditions
– Gives you plenty of rope to hang yourself with
• OpenMP and Amdahl’s Law
– What is Dagum and Menon take on this issue?
• Computational Problem Size
– USA #1 supercomputer (Titan) has 32GB/node
– World #1 supercomputer (Tianhe-2) has 64GB/node
– What if our datasets are larger than 64GB?
• “You’re gonna need a bigger boat…”
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MPI + OpenMP
• Programming in MPI + OpenMP is hard
– Necessary for today’s large distributed memory systems
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PGAS Programming Model
• Partitioned Global Address Space
• Presents an abstracted shared address space
– Simplifies the complexity of MPI + OpenMP
• Exposes data/task locality for performance
• Several languages use this model
– UPC, Fortress, HPF, X10, etc.
– Ever heard of these?
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Chapel Themes
• Designed from scratch
– Blank slate as opposed to a language extension
– Allows for clearer, more expressive language semantics
• Multi-resolution design
– Higher-level abstractions built from lower-level ones
– Allows users to mix and match as needed
• Global-view programming
– Data can be declared using global problem size
– Data can be accessed using global indices
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