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Paraguin Compiler

Paraguin Compiler. Version 2.1. Introduction. The Paraguin Compiler is a compiler that I am developing at UNCW (by myself basically) It is based on the SUIF Compiler infrastruction Using pragma s the user can direct the compiler (compiler directives) to produce and MPI program

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Paraguin Compiler

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  1. Paraguin Compiler Version 2.1

  2. Introduction • The Paraguin Compiler is a compiler that I am developing at UNCW (by myself basically) • It is based on the SUIF Compiler infrastruction • Using pragmas the user can direct the compiler (compiler directives) to produce and MPI program • User Manual can be accessed at: • http://people.uncw.edu/cferner/Paraguin/userman.pdf

  3. SUIF Compiler System • Created by the SUIF Compiler Group at Stanford (suif.stanford.edu) • SUIF is an open source compiler intended to promote research in compiler technology • Paraguin is built using the SUIF compiler

  4. Compiler Directives • The Paraguin compiler is a source to source compiler • It transforms a sequential program into a parallel program suitable for execution on a distributed-memory system • The result is a parallel program with calls to MPI routines • Parallelization is not automatic; but rather directed via pragmas

  5. Compiler Directives • The advantage to using pragmas is that other compilers will ignore them • You can provide information to Paraguin that is ignored by other compilers, say gcc • You can create a hybrid program using pragmas for different compilers • Syntax: #pragma paraguin <type> [<parameters>]

  6. Running a Parallel Program • When your parallel program is run, you specify how many processors you want on the command line (or in a job submission file) • Processes (1 per processor) will be given a rank, which is unique, in the range [0 .. NP-1], where NP is the number of processors. • Process 0 is considered to be the master.

  7. Parallel Region … #pragma paraguin begin_parallel … #pragma paraguin end_parallel … Code inside of the parallel region is executed by all processors Code outside of the parallel region is executed by the master process (with rank = 0) only. All other processors do not execute this code.

  8. Hello World #ifdef PARAGUIN typedef void* __builtin_va_list; #endif #include <stdio.h> int __guin_rank = 0; int main(int argc, char *argv[]) { char hostname[256]; printf("Master process %d starting.\n", __guin_rank); #pragma paraguin begin_parallel gethostname(hostname, 255); printf("Hello world from process %3d on machine %s.\n", __guin_rank, hostname); #pragma paraguin end_parallel printf("Goodbye world from process %d.\n", _guin_rank); }

  9. Explanation of Hello World #ifdef PARAGUIN typedef void* __builtin_va_list; #endif This is here to deal with an incompatibility issue between the SUIF compiler and gcc. Don’t worry about it, but just put it into your program.

  10. Explanation of Hello World This is a predefined Paraguin identifier. We are allowed to declare it and even initialize it, but it should not be modified. #include <stdio.h> int __guin_rank = 0; int main(intargc, char *argv[]) { printf("Master thread %d starting.\n", __guin_rank); … The reason for doing this is so that we can compile this program with gcc (with no modification to the source code) to create a sequential version of the program.

  11. Explanation of Hello World #pragma paraguin begin_parallel gethostname(hostname, 255); printf("Hello world from process %3d on machine %s.\n", __guin_rank, hostname); #pragma paraguin end_parallel This defines a region to be executed by all processors. Outside of this region, only the master process executes the statements.

  12. Explanation of Hello World printf("Master process %d starting.\n", __guin_rank); #pragma paraguin begin_parallel Only the master process (with rank = 0) executes the code outside a parallel region. The other processors skip it. PE 0 PE 1 PE 2 PE 3 PE 4 PE 5 Execute Skip

  13. Explanation of Hello World #pragma paraguin end_parallel printf("Goodbye world from process %d.\n", _guin_rank); } Only the master process (with rank = 0) executes the code outside a parallel region. The other processors skip it. PE 0 PE 1 PE 2 PE 3 PE 4 PE 5 Execute Skip

  14. Compiling Running Result of Hello World $ scc -DPARAGUIN -D__x86_64__ -I/opt/openmpi/include/ -cc mpicc helloWorld.c -o helloWorld $ mpirun –np 8 hello.out Master process 0 starting. Hello world from process 0 on machine compute-1-5.local. Goodbye world from process 0. Hello world from process 1 on machine compute-1-5.local. Hello world from process 2 on machine compute-1-5.local. Hello world from process 3 on machine compute-1-5.local. Hello world from process 4 on machine compute-1-1.local. Hello world from process 5 on machine compute-1-1.local. Hello world from process 6 on machine compute-1-1.local. Hello world from process 7 on machine compute-1-1.local. All on one line

  15. Notes on pragmas • Many times you need an extra semicolon (;) in front of the pragma statements. • The reason is to insert a NOOP instruction into the code to which the pragmas are attached • SUIF attaches the pragmas to the last instruction, which may be deeply nested. • This makes it difficult for Paraguin to find the pragmas • Solution: insert a semicolon on a line by itself before a block of pragma statements

  16. Incorrect Location of Pragma for (i = 0; i < n; i++) for (j = 0; j < n; j++) a[i][j] = 0; } } #pragma paraguin begin_parallel for (i = 0; i < n; i++) for (j = 0; j < n; j++) a[i][j] = 0; #pragma paraguin begin_parallel } } This code Actually appears like this:

  17. Solution for (i = 0; i < n; i++) for (j = 0; j < n; j++) a[i][j] = 0; } } ; #pragma paraguin begin_parallel Solution: put a semicolon in front of pragma Usually, it is needed after a nesting (e.x. for loop nest, while loop nest, etc.)

  18. More on Parallel Regions The parallel region pragmas must be at the topmost nesting within a function. int f () { #pragma paraguin begin_parallel … #pragma paraguin end_parallel } int g() { #pragma paraguin begin_parallel ... if (a < b) #pragma paraguin end_parallel This is an error

  19. Parallel Regions Related to Functions int f () { #pragma paraguin begin_parallel … #pragma paraguin end_parallel } int main() { #pragma paraguin begin_parallel f(); #pragma paraguin end_parallel f(); If a function is to be executed in parallel, it must have it’s own parallel region, and the call to it must also be in a parallel region This one will execute in parallel This one will execute sequentially, regardless of its own parallel regions.

  20. Initializations • Initializations of variables are executable statements (as opposed to the declaration) • Therefore, then need to be within a parallel region int f () { int a = 23, b; #pragma paraguinbegin_parallel b = 46; … #pragma paraguin end_parallel } a will be initialized on the master only because it is outside a parallel region b will be initialized on all processors

  21. Parallel Constructs • All of these must be within a parallel region (some would deadlock if not): • #pragma paraguin barrier • #pragma paraguin forall • #pragma paraguin bcast • #pragma paraguin scatter • #pragma paraguin gather • #pragma paraguin reduce

  22. Barrier • A barrier is a point at which all processors stop until they all arrive at the same point, after which they may proceed • It’s like a rendezvous PE 0 PE 1 PE 2 PE 3 PE 4 PE 5

  23. Barrier … #pragma paraguin barrier …

  24. Parallel For (or forall) • To execute a for loop in parallel: #pragma paraguinforall [chunksize] • Each processor will execute a different partition of the iterations (call the iteration space) • The partitions will be no larger than chunksize number of iterations • Default chunksize • Where n is the number of iterations and NP is the number of processors

  25. Parallel For (or forall) • For example consider: #pragma paraguin forall for (i = 0; i < n; i++) { <body> • Suppose n = 13. The iteration space is

  26. Parallel For (or forall) • Also suppose we have 4 processors. • Default chunksize is • The iteration space will be executed by the 4 processors as:

  27. Parallel For (other notes) • Not that the for loop that is executed as a forall must be a simple for loop: • The increment must be positive 1 (and the upper bound must be greater than the lower bound) • The loop termination must use either < or <= • A nested for loop can be a forall: for (i = 0; i < n; i++) { #pragmaparaguinforall for (j = 0; j < n; j++) { • However, foralls cannot be nested

  28. How to transform for loops to simple for loops • Count down loop for (i = n-1; i >=0; i--) { … • Nested loops for (i = 0; i < n; i++) { for (j = 0; j < n; j++) { … #pragma paraguin forall for (tmp = 0; tmp < n; tmp++) { i = n – tmp – 1; … #pragma paraguin forall for (tmp = 0; tmp < n*n; tmp++) { i = tmp / n; j = tmp % n; …

  29. Parallel For (other notes) • If the user provides a chunksize, then each processor cycles through chunksize iterations in a cyclic fashion • Specifying a chunksize of 1 is cyclic scheduling (better load balancing)

  30. Broadcast • Broadcasting data sends the same data to all processor from the master #pragma paraguinbcast <list of variables> • Broadcast is likely to be faster than individual message int a, b[N][M], n; char *s = “hello world”; n = strlen(s) + 1; #pragma paraguinbegin_parallel #pragma paraguinbcast a b n s( n )

  31. Broadcast int a, b[N][M], n; char *s = “hello world”; n = strlen(s) + 1; #pragma paraguinbegin_parallel #pragma paraguinbcast a b n s( n ) • Variable ais a scalar and b is an array, but the correct number of bytes are broadcast • N*M*sizeof(int) bytes are broadcast for variable b.

  32. Broadcast int a, b[N][M], n; char *s = “hello world”; n = strlen(s) + 1; #pragma paraguinbegin_parallel #pragma paraguinbcast a b n s( n ) • Variable sis a string or a pointer. There is no way to know how big the data actually is • Pointers require a size (such as s( n )) • If the size is not given then only one character will be broadcast

  33. Broadcast inta, b[N][M], n; char *s = “hello world”; n = strlen(s) + 1; #pragma paraguinbegin_parallel #pragma paraguinbcast a b n s( n ) • Notice that s and n are initialize on the master only • 1 is added to strlen(s) to include null character • Variable n must be broadcast BEFORE variable s • Put spaces between parentheses and size (e.g. ( n ))

  34. Scatter • Scattering data divides up data that resides on the master among the other processors #pragma paraguin scatter <list of variables>

  35. Scatter void f(int *A, int n) { int B[N]; … // Initialize B somehow #pragma paraguin begin_parallel #pragma paraguin scatter A( n ) B ... • Same thing applies for pointers with scatter as with broadcast. The size must be given. • Only arrays should be scatter (it makes no sense to scatter a scalar).

  36. Scatter • The default chunksize is • where N is the number of rows and NP is the number of processors • Notice that the rows are scattered, not columns • User defined chunksize is not yet implemented

  37. Gather • Gather works just like Scatter except that the data moves in the opposite direction #pragma paraguin gather <list of variables>

  38. Gather • Gather is the collection of partial results back to the master • The default chunksize is • where N is the number of rows and NP is the number of processors • User defined chunksize is not yet implemented

  39. Reduction • A reduction is when a binary commutative operator is applied to a collection of values producing a single value #pragma paraguin reduce <op> <source> <result> • Where • <op> is the operator • <source> is the variable with the data to be reduced • <result> is the variable that will hold the answer

  40. Reduction • For example, applying summation to the following values: • Produces the single value of 549 • MPI does not specify how reduction should be implemented; however, …

  41. Reduction • A reduction could be implemented fairly efficiently on multiple processor using a tree • In which case the time is O(log(NP))

  42. Reduction • Available operators that can be used in a reduction:

  43. Reduction double c, result_c; ... #pragma paraguin begin_parallel ... // Each processor assigns some value to the variable c ... #pragma paraguin reduce sum c result_c // The variable result_c on the master now holds the result // of summing the values of the variable c on all the // processors ...

  44. Reducing an Array • When a reduction is applied to an array, the corresponding values in the same relative position in the array are reduced across processors double c[N], result_c[N]; ... #pragma paraguin begin_parallel ... // Each processor assigns N values to the array c ... #pragma paraguin reduce sum c result_c ...

  45. Reducing an Array

  46. Next Topic • Patterns: • Scatter/Gather • Stencil

  47. Questions?

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