1. APIs FOR
PARALLEL PROGRAMMING
By:
SURINDER KAUR
2012CS13

2. PARRALLEL PROGRAMMMING MODELS:
• SHARED MEMORY MODEL:
•Programs are executed on one or more processors
that share some or all of the available memory.
•OpenMP is based on this model.
• MESSAGE PASSING MODEL:
•Programs are executed by one or more processes
that exchange message whenever one of them needs
data produced by the other.
•MPI is based on this model.

3. OpenMP
OPEN MULTIPROCESSING

4. WHAT THEY ARE FOR
• Shared
memory API that supports multi-platform
programming in C/C++ and Fortan.
• Data is shared among the threads and is visible to all of
them.
• Private data is thread specific data.
• Values of shared data must be made available to all
threads at synchronization points.

5. EXECUTION MODEL
• Fork-Join model.
Initial thread
Fork
Team of threads
Join
Initial thread

6. • OpenMP model supports incremental parallelization.
• In this approach a sequential program is transformed into
parallel program one block of code at a time.
Master Thread
Fork
Join
Time
Master Thread
Fork
Join
Master Thread

7. LANGUAGE FEATURES
• Directives
• Library functions
• Environment variables
• Constructs
• Clauses

8. DIRECTIVES
• Parallel construct
#pragma omp parallel [clause,clause,…]
structured block
• Work sharing constuct
• Loop construct
#pragma omp for [clause, clause,…]
for loop
• Section construct
#pragma omp sections [clause,clause,…]
{
#pragma omp section
structured block
#pragma omp section
structured block
}

9. •Single construct
#pragma omp single [clause, clause,…]
structured block
•Work sharing construct
• Combined parallel work sharing construct
#pragma omp parallel
{ #pragma omp parallel for
#pragma omp for for loop
for loop
}

10. SYNCHRONIZATION
CONSTRUCTS
• Barrier construct
#pragma omp barrier
• Ordered construct
#pragma omp ordered
• Critical construct
#pragma omp critical [name]
structured block
• Atomic construct
#pragma omp atomic
statement
• Master construct
#pragma omp master
structured block
• Locks

11. CLAUSES
• Shared clause
shared(list)
• Private clause
private(list)
• Lastprivate clause
lastprivate(list)
• Firstprivate clause
firstprivate(list)
• Default clause
default(none/shared)

12. • Num_thread clause
num_threads(integer_expression)
•Ordered clause
ordered
•Reduction clause
reduction(operator:list)
• Copyin clause
copyin(list)
• Copyprivate clause
copyprivate(list)
•Nowait clause
•Schedule clause
• If clause
if (expression)

13. OTHER DIRECTIVE
• Flush directive
#pragma omp flush [(list)]
• Threadprivate direcive
#pragma omp threadprivate (list)

14. ENVIRONMENT VARIABLE
• OMP_NUM_THREADS
setenv OMP_NUM_THREADS <int>
• OMP_DYNAMIC
setenv OMP_DYNAMIC TRUE
• OMP_NESTED
setenv OMP_NESTED TRUE
• OMP_SCHEDULE

15. ADVANTAGES
• Structural parallel programming.
• Simple to use.
• Runs on various platforms
• Portability.
• Incremental parallelism.
• Unified code for both serial and parallel applications..
• Data layout and decomposition is handled automatically by
directives.
• Both coarse-grained and fine-grained parallelism are possible

16. DISADVANTAGES
• Since it relies on compiler to detect and exploit parallelism in
application, which may degrade its performance.
• Race condition may arise.
• Currently only runs efficiently in shared-memory
multiprocessor platforms.
• Compiler support is required.
• Scalability is limited by memory architecture.

17. USES
• Grid computing
• Wave, weather and ocean code:
• LAMBO, Limited Area Model BOlogna, is a grid-point primitive equations
model.
• The Wave Model, WA.M., describes the sea state at a certain time in a
certain position as the overlapping of many sinusoidals with different
frequencies.
• Modular Ocean Model, M.O.M., solves the primitive equations under
hydrostatic, Boussinesq and rigid lid approximations.

18. FUTURE WORK
• OpenMP, the de facto standard for parallel programming on
shared memory systems, continues to extend its reach
beyond pure HPC to include embedded systems, real time
systems, and accelerators.
• Release Candidate 1 of the OpenMP 4.0 API specifications
currently under development is now available for public
discussion. This update includes thread affinity, initial support
for Fortran 2003, SIMD constructs to vectorize both serial and
parallelized loops , user-defined reductions, and sequentially
consistent atomics.
• More new features, in a final Release Candidate 2, to appear
sometime in the first Quarter of 2013, followed by the finalized
full 4.0 API specifications soon thereafter.

19. MPI
MESSAGE PASSING
INTERFACE

20. WHAT THEY ARE FOR
• Library based model for interprocess communication.
• Various executing processes communicate via message passing.
• The message can either be
• DATA message
• CONTROL message
• IPC can either be
•SYNCHRONOUS
•ASYNCHRONOUS

21. ASSOCIATED TERMS
• Group:
Set of processes that communicate with one
another.
• Context:
It is the frequency of the communication.
• Communicator:
Central object for communication in MPI. Each
communicator is associated with a group and a
context.

22. COMMUNICATION MODES
• Standard:
Communication completes as the message is sent out by the
sender.
• Synchronous :
Communication completes when the acknowledgement from sender is
received by the sender.
• Buffered:
Communication completes as the sender generates the message and
stores in the buffer.
• Ready Send:
Communication completes immediately, if the receiver is ready for
the message it will get it, otherwise the message is dropped
silently.

23. BLOCKING AND NONBLOCKING
• Blocking
The program halts until the communication is
completed.
• Non-Blocking
The program will continue its execution without
waiting for the communication to be completed.

24. MPI CALLS
• MPI_INIT:
MPI Init(int *argc, char ***argv)
MPI_Init( &argc, &argv)
• MPI_COMM_SIZE:
MPI Comm size(MPI Comm comm, int *size)
MPI_Comm_size(MPI_COMM_WORLD, &numprocs)
• MPI_COMM_RANK:
MPI Comm rank(MPI Comm comm, int *rank)
MPI_Comm_rank( MPI_COMM_WORLD, &rank)

25. • MPI _SEND:
MPI Send(void* buf, int count, MPI Datatype datatype,
int dest, int tag, MPI Comm comm)
MPI_Send( buff, BUFSIZE, MPI_CHAR, 0, TAG,
MPI_COMM_WORLD)
• MPI_RECV:
MPI Recv(void* buf, int count, MPI Datatype datatype,
int source, int tag, MPI Comm comm, MPI Status
*status)
MPI_Recv(buff, BUFSIZE, MPI_CHAR, i, TAG,
MPI_COMM_WORLD, &stat)
• MPI_FINALIZE:
• MPI Finalize()

26. MPI DATA TYPES
• MPI_INT
• MPI_FLOAT
• MPI_BYTE
• MPI_CHAR

27. ADVANTAGES
•Highly expressive
•Enable efficient parallel programming
•Excellent portability
•Comprehensive set of library routines
•Language independent
• Provide access to advanced parallel hardware

28. DISADVANTAGES
•Require more effort for developing code as compared to the
OpenMP.
•Communicators, which are much used in MPI programming, are
not entirely implemented. The underlying communicator system is
present, but is presently restricted to only the global
environment MPI_COMM_WORLD.
•Debugging the code is difficult.
•Communication overhead.

29. USES
• High performance computing
• Grid computing

30. FUTURE WORK
MPI-1:
• a static runtime environment
MPI-2.
• Parallel I/O
• Dynamic process management
• Remote memory operations
• Specifies over 500 functions
• Language bindings for ANSI C, ANSI C++, and ANSI Fortran (Fortran90)
• Object interoperability
Presently it is most widely used API for parallel programming. Hence some
aspects of MPI's future appear solid; others less so.
The MPI Forum reconvened in 2007, to clarify some MPI-2 issues and explore
developments for a possible MPI-3.
With greater internal concurrency (multi-core), better fine-grain concurrency
control (threading, affinity), and more levels of memory hierarchy
multithreading programs can take advantage of these developments more easily
. So developing concurrency completely within MPI is an opportunity for the
standard MPI-3. Also incorporating fault tolerance within the standard is also an
important issue.