This document discusses MPI (Message Passing Interface) and OpenMP for parallel programming. MPI is a standard for message passing parallel programs that requires explicit communication between processes. It provides functions for point-to-point and collective communication. OpenMP is a specification for shared memory parallel programming that uses compiler directives to parallelize loops and sections of code. It provides constructs for work sharing, synchronization, and managing shared memory between threads. The document compares the two approaches and provides examples of simple MPI and OpenMP programs.
This document provides an overview of MPI (Message Passing Interface), which is a standard for parallel programming using message passing. The key points covered include:
- MPI allows programs to run across multiple computers in a distributed memory environment. It has functions for point-to-point and collective communication.
- Common MPI functions introduced are MPI_Send, MPI_Recv for point-to-point communication, and MPI_Bcast, MPI_Gather for collective operations.
- More advanced topics like derived data types and examples of Poisson equation and FFT solvers are also briefly discussed.
The Message Passing Interface (MPI) allows parallel applications to communicate between processes using message passing. MPI programs initialize and finalize a communication environment, and most communication occurs through point-to-point send and receive operations between processes. Collective communication routines like broadcast, scatter, and gather allow all processes to participate in the communication.
The document appears to be a block of random letters with no discernible meaning or purpose. It consists of a series of letters without any punctuation, formatting, or other signs of structure that would indicate it is meant to convey any information. The document does not provide any essential information that could be summarized.
The document provides an introduction to Message Passing Interface (MPI), which is a standard for message passing parallel programming. It discusses key MPI concepts like communicators, data types, point-to-point and collective communication routines. It also presents examples of common parallel programming patterns like broadcast, scatter-gather, and parallel sorting and matrix multiplication. Programming hints are provided, along with references for further reading.
Hybrid parallel programming uses both message passing (e.g. MPI) and shared memory parallelism (e.g. OpenMP). MPI is used to distribute work across multiple computers while OpenMP parallelizes work within each computer across multiple cores. This approach can improve performance over MPI-only for problems where communication between computers is expensive compared to synchronization within a computer. However, for matrix multiplication experiments, a hybrid MPI-OpenMP approach did not show better performance than MPI-only. Larger problem sizes or different algorithms may be needed to realize benefits of the hybrid approach.
High Performance Computing Workshop for IHPC, Techkriti'13
Supercomputing Blog contains the codes -
http://ankitmahato.blogspot.in/search/label/Supercomputing
Credits:
https://computing.llnl.gov/
http://www.mcs.anl.gov/research/projects/mpi/
This document provides an introduction and overview of MPI (Message Passing Interface). It discusses:
- MPI is a standard for message passing parallel programming that allows processes to communicate in distributed memory systems.
- MPI programs use function calls to perform all operations. Basic definitions are included in mpi.h header file.
- The basic model in MPI includes communicators, groups, and ranks to identify processes. MPI_COMM_WORLD identifies all processes.
- Sample MPI programs are provided to demonstrate point-to-point communication, collective communication, and matrix multiplication using multiple processes.
- Classification of common MPI functions like initialization, communication, and information queries are discussed.
Message Passing Interface (MPI) is a language-independent communications protocol used to program parallel computers. Both point-to-point and collective communication are supported.
MPI "is a message-passing application programmer interface, together with protocol and semantic specifications for how its features must behave in any implementation." So, MPI is a specification, not an implementation.
MPI's goals are high performance, scalability, and portability.
OpenMP (Open Multi-Processing) is an application programming interface (API) that supports multi-platform shared memory multiprocessing programming in C, C++, and Fortran, on most platforms, processor architectures and operating systems, including Solaris, AIX, HP-UX, Linux, MacOS, and Windows.
OpenMP uses a portable, scalable model that gives programmers a simple and flexible interface for developing parallel applications for platforms ranging from the standard desktop computer to the supercomputer.
This document provides an overview of MPI (Message Passing Interface), which is a standard for parallel programming using message passing. The key points covered include:
- MPI allows programs to run across multiple computers in a distributed memory environment. It has functions for point-to-point and collective communication.
- Common MPI functions introduced are MPI_Send, MPI_Recv for point-to-point communication, and MPI_Bcast, MPI_Gather for collective operations.
- More advanced topics like derived data types and examples of Poisson equation and FFT solvers are also briefly discussed.
The Message Passing Interface (MPI) allows parallel applications to communicate between processes using message passing. MPI programs initialize and finalize a communication environment, and most communication occurs through point-to-point send and receive operations between processes. Collective communication routines like broadcast, scatter, and gather allow all processes to participate in the communication.
The document appears to be a block of random letters with no discernible meaning or purpose. It consists of a series of letters without any punctuation, formatting, or other signs of structure that would indicate it is meant to convey any information. The document does not provide any essential information that could be summarized.
The document provides an introduction to Message Passing Interface (MPI), which is a standard for message passing parallel programming. It discusses key MPI concepts like communicators, data types, point-to-point and collective communication routines. It also presents examples of common parallel programming patterns like broadcast, scatter-gather, and parallel sorting and matrix multiplication. Programming hints are provided, along with references for further reading.
Hybrid parallel programming uses both message passing (e.g. MPI) and shared memory parallelism (e.g. OpenMP). MPI is used to distribute work across multiple computers while OpenMP parallelizes work within each computer across multiple cores. This approach can improve performance over MPI-only for problems where communication between computers is expensive compared to synchronization within a computer. However, for matrix multiplication experiments, a hybrid MPI-OpenMP approach did not show better performance than MPI-only. Larger problem sizes or different algorithms may be needed to realize benefits of the hybrid approach.
High Performance Computing Workshop for IHPC, Techkriti'13
Supercomputing Blog contains the codes -
http://ankitmahato.blogspot.in/search/label/Supercomputing
Credits:
https://computing.llnl.gov/
http://www.mcs.anl.gov/research/projects/mpi/
This document provides an introduction and overview of MPI (Message Passing Interface). It discusses:
- MPI is a standard for message passing parallel programming that allows processes to communicate in distributed memory systems.
- MPI programs use function calls to perform all operations. Basic definitions are included in mpi.h header file.
- The basic model in MPI includes communicators, groups, and ranks to identify processes. MPI_COMM_WORLD identifies all processes.
- Sample MPI programs are provided to demonstrate point-to-point communication, collective communication, and matrix multiplication using multiple processes.
- Classification of common MPI functions like initialization, communication, and information queries are discussed.
Message Passing Interface (MPI) is a language-independent communications protocol used to program parallel computers. Both point-to-point and collective communication are supported.
MPI "is a message-passing application programmer interface, together with protocol and semantic specifications for how its features must behave in any implementation." So, MPI is a specification, not an implementation.
MPI's goals are high performance, scalability, and portability.
OpenMP (Open Multi-Processing) is an application programming interface (API) that supports multi-platform shared memory multiprocessing programming in C, C++, and Fortran, on most platforms, processor architectures and operating systems, including Solaris, AIX, HP-UX, Linux, MacOS, and Windows.
OpenMP uses a portable, scalable model that gives programmers a simple and flexible interface for developing parallel applications for platforms ranging from the standard desktop computer to the supercomputer.
MPI4Py provides an interface to MPI (Message Passing Interface) that allows Python programs to perform parallel and distributed computing. It supports key MPI concepts like point-to-point and collective communication, communicators, and spawning new processes. The documentation discusses how MPI4Py can communicate Python objects and NumPy arrays between processes, supports common MPI routines, and enables features like one-sided communication and MPI I/O. Examples demonstrate using MPI4Py for tasks like broadcasting data, scattering/gathering arrays, and spawning new Python processes to calculate Pi in parallel.
The document discusses the basics of MPI (Message Passing Interface), which is a standard for message passing parallel programming. It explains the basic model of MPI including communicators, groups, and ranks. It then covers point-to-point communication functions like blocking and non-blocking send/receive. Finally, it briefly introduces collective communication functions that involve groups of processes like broadcast and barrier.
The document provides an overview of Message Passing Interface (MPI), a standard for message passing parallel programming. It explains the basic MPI model including communicators, groups, ranks, and point-to-point communication functions like MPI_Send and MPI_Recv. Blocking and non-blocking send/receive operations are discussed along with how data is described and processes identified in MPI point-to-point communication.
This document discusses implementing a parallel merge sort algorithm using MPI (Message Passing Interface). It describes the background of MPI and how it can be used for communication between processes. It provides details on the dataset used, MPI functions for initialization, communication between processes, and summarizes the results which show a decrease in runtime when increasing the number of processors.
The document discusses parallel programming and message passing as a parallel programming model. It provides examples of using MPI (Message Passing Interface) and MapReduce frameworks for parallel programming. Some key applications discussed are financial risk assessment, molecular dynamics simulations, rendering animation, and web indexing. Challenges with parallel programming include potential slowdown due to overhead and limitations of parallel speedup based on sequential fractions of programs.
The document discusses parallel programming using MPI (Message Passing Interface). It introduces MPI as a standard for message passing between processes. It describes how to set up a basic parallel computing environment using a cluster of networked computers. It provides examples of using MPI functions to implement parallel algorithms, including point-to-point and collective communication like broadcast, gather, and scatter.
This document provides an overview of Message Passing Interface (MPI) including advantages of the message passing programming model, background on MPI, key concepts, and examples of basic MPI communications. The 6 basic MPI calls in C and Fortran are described which include MPI_Init, MPI_Comm_rank, MPI_Comm_Size, MPI_Send, MPI_Recv, and MPI_Finalize. A simple example program demonstrates a basic send and receive of an integer between two processors.
Move Message Passing Interface Applications to the Next LevelIntel® Software
Explore techniques to reduce and remove message passing interface (MPI) parallelization costs. Get practical examples and examples of performance improvements.
This document provides an overview of message passing computing and the Message Passing Interface (MPI) library. It discusses message passing concepts, the Single Program Multiple Data (SPMD) model, point-to-point communication using send and receive routines, message tags, communicators, debugging tools, and evaluating performance through timing. Key points covered include how MPI defines a standard for message passing between processes, common routines like MPI_Send and MPI_Recv, and how to compile and execute MPI programs on multiple computers.
This document provides an introduction to MPI (Message Passing Interface) and parallel programming. It discusses the message passing model and types of parallel computer models that MPI supports. It also describes basic MPI concepts like processes, communicators, datatypes, tags, and blocking/non-blocking send and receive routines. Collective operations like broadcast and reduce are introduced. Finally, it discusses sources of deadlock and solutions using non-blocking routines.
This document provides an overview of parallel programming with OpenMP. It discusses how OpenMP allows users to incrementally parallelize serial C/C++ and Fortran programs by adding compiler directives and library functions. OpenMP is based on the fork-join model where all programs start as a single thread and additional threads are created for parallel regions. Core OpenMP elements include parallel regions, work-sharing constructs like #pragma omp for to parallelize loops, and clauses to control data scoping. The document provides examples of using OpenMP for tasks like matrix-vector multiplication and numerical integration. It also covers scheduling, handling race conditions, and other runtime functions.
"This deck is from the opening session of the "Introduction to Programming Pascal (P100) with CUDA 8" workshop at CSCS in Lugano, Switzerland. The three-day course is intended to offer an introduction to Pascal computing using CUDA 8."
Watch the video: http://wp.me/p3RLHQ-gsQ
Learn more: http://www.cscs.ch/events/event_detail/index.html?tx_seminars_pi1%5BshowUid%5D=155
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Parallel computing uses multiple processors simultaneously to solve computational problems faster. It allows solving larger problems or more problems in less time. Shared memory parallel programming with tools like OpenMP and pthreads is used for multicore processors that share memory. Distributed memory parallel programming with MPI is used for large clusters with separate processor memories. GPU programming with CUDA is also widely used to leverage graphics hardware for parallel tasks like SIMD. The key challenges in parallel programming are load balancing, communication overhead, and synchronization between processors.
This document provides an overview of parallel computing. It discusses why parallel computation is needed due to limitations in increasing processor speed. It then covers various parallel platforms including shared and distributed memory systems. It describes different parallel programming models and paradigms including MPI, OpenMP, Pthreads, CUDA and more. It also discusses key concepts like load balancing, domain decomposition, and synchronization which are important for parallel programming.
The document provides an overview of parallel programming using MPI and OpenMP. It discusses key concepts of MPI including message passing, blocking and non-blocking communication, and collective communication operations. It also covers OpenMP parallel programming model including shared memory model, fork/join parallelism, parallel for loops, and shared/private variables. The document is intended as lecture material for an introduction to high performance computing using MPI and OpenMP.
The document provides an introduction to OpenMP, which is an application programming interface for explicit, portable, shared-memory parallel programming in C/C++ and Fortran. OpenMP consists of compiler directives, runtime calls, and environment variables that are supported by major compilers. It is designed for multi-processor and multi-core shared memory machines, where parallelism is accomplished through threads. Programmers have full control over parallelization through compiler directives that control how the program works, including forking threads, work sharing, synchronization, and data environment.
MPI4Py provides an interface to MPI (Message Passing Interface) that allows Python programs to perform parallel and distributed computing. It supports key MPI concepts like point-to-point and collective communication, communicators, and spawning new processes. The documentation discusses how MPI4Py can communicate Python objects and NumPy arrays between processes, supports common MPI routines, and enables features like one-sided communication and MPI I/O. Examples demonstrate using MPI4Py for tasks like broadcasting data, scattering/gathering arrays, and spawning new Python processes to calculate Pi in parallel.
The document discusses the basics of MPI (Message Passing Interface), which is a standard for message passing parallel programming. It explains the basic model of MPI including communicators, groups, and ranks. It then covers point-to-point communication functions like blocking and non-blocking send/receive. Finally, it briefly introduces collective communication functions that involve groups of processes like broadcast and barrier.
The document provides an overview of Message Passing Interface (MPI), a standard for message passing parallel programming. It explains the basic MPI model including communicators, groups, ranks, and point-to-point communication functions like MPI_Send and MPI_Recv. Blocking and non-blocking send/receive operations are discussed along with how data is described and processes identified in MPI point-to-point communication.
This document discusses implementing a parallel merge sort algorithm using MPI (Message Passing Interface). It describes the background of MPI and how it can be used for communication between processes. It provides details on the dataset used, MPI functions for initialization, communication between processes, and summarizes the results which show a decrease in runtime when increasing the number of processors.
The document discusses parallel programming and message passing as a parallel programming model. It provides examples of using MPI (Message Passing Interface) and MapReduce frameworks for parallel programming. Some key applications discussed are financial risk assessment, molecular dynamics simulations, rendering animation, and web indexing. Challenges with parallel programming include potential slowdown due to overhead and limitations of parallel speedup based on sequential fractions of programs.
The document discusses parallel programming using MPI (Message Passing Interface). It introduces MPI as a standard for message passing between processes. It describes how to set up a basic parallel computing environment using a cluster of networked computers. It provides examples of using MPI functions to implement parallel algorithms, including point-to-point and collective communication like broadcast, gather, and scatter.
This document provides an overview of Message Passing Interface (MPI) including advantages of the message passing programming model, background on MPI, key concepts, and examples of basic MPI communications. The 6 basic MPI calls in C and Fortran are described which include MPI_Init, MPI_Comm_rank, MPI_Comm_Size, MPI_Send, MPI_Recv, and MPI_Finalize. A simple example program demonstrates a basic send and receive of an integer between two processors.
Move Message Passing Interface Applications to the Next LevelIntel® Software
Explore techniques to reduce and remove message passing interface (MPI) parallelization costs. Get practical examples and examples of performance improvements.
This document provides an overview of message passing computing and the Message Passing Interface (MPI) library. It discusses message passing concepts, the Single Program Multiple Data (SPMD) model, point-to-point communication using send and receive routines, message tags, communicators, debugging tools, and evaluating performance through timing. Key points covered include how MPI defines a standard for message passing between processes, common routines like MPI_Send and MPI_Recv, and how to compile and execute MPI programs on multiple computers.
This document provides an introduction to MPI (Message Passing Interface) and parallel programming. It discusses the message passing model and types of parallel computer models that MPI supports. It also describes basic MPI concepts like processes, communicators, datatypes, tags, and blocking/non-blocking send and receive routines. Collective operations like broadcast and reduce are introduced. Finally, it discusses sources of deadlock and solutions using non-blocking routines.
This document provides an overview of parallel programming with OpenMP. It discusses how OpenMP allows users to incrementally parallelize serial C/C++ and Fortran programs by adding compiler directives and library functions. OpenMP is based on the fork-join model where all programs start as a single thread and additional threads are created for parallel regions. Core OpenMP elements include parallel regions, work-sharing constructs like #pragma omp for to parallelize loops, and clauses to control data scoping. The document provides examples of using OpenMP for tasks like matrix-vector multiplication and numerical integration. It also covers scheduling, handling race conditions, and other runtime functions.
"This deck is from the opening session of the "Introduction to Programming Pascal (P100) with CUDA 8" workshop at CSCS in Lugano, Switzerland. The three-day course is intended to offer an introduction to Pascal computing using CUDA 8."
Watch the video: http://wp.me/p3RLHQ-gsQ
Learn more: http://www.cscs.ch/events/event_detail/index.html?tx_seminars_pi1%5BshowUid%5D=155
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Parallel computing uses multiple processors simultaneously to solve computational problems faster. It allows solving larger problems or more problems in less time. Shared memory parallel programming with tools like OpenMP and pthreads is used for multicore processors that share memory. Distributed memory parallel programming with MPI is used for large clusters with separate processor memories. GPU programming with CUDA is also widely used to leverage graphics hardware for parallel tasks like SIMD. The key challenges in parallel programming are load balancing, communication overhead, and synchronization between processors.
This document provides an overview of parallel computing. It discusses why parallel computation is needed due to limitations in increasing processor speed. It then covers various parallel platforms including shared and distributed memory systems. It describes different parallel programming models and paradigms including MPI, OpenMP, Pthreads, CUDA and more. It also discusses key concepts like load balancing, domain decomposition, and synchronization which are important for parallel programming.
The document provides an overview of parallel programming using MPI and OpenMP. It discusses key concepts of MPI including message passing, blocking and non-blocking communication, and collective communication operations. It also covers OpenMP parallel programming model including shared memory model, fork/join parallelism, parallel for loops, and shared/private variables. The document is intended as lecture material for an introduction to high performance computing using MPI and OpenMP.
The document provides an introduction to OpenMP, which is an application programming interface for explicit, portable, shared-memory parallel programming in C/C++ and Fortran. OpenMP consists of compiler directives, runtime calls, and environment variables that are supported by major compilers. It is designed for multi-processor and multi-core shared memory machines, where parallelism is accomplished through threads. Programmers have full control over parallelization through compiler directives that control how the program works, including forking threads, work sharing, synchronization, and data environment.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
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Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
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The CBC machine is a common diagnostic tool used by doctors to measure a patient's red blood cell count, white blood cell count and platelet count. The machine uses a small sample of the patient's blood, which is then placed into special tubes and analyzed. The results of the analysis are then displayed on a screen for the doctor to review. The CBC machine is an important tool for diagnosing various conditions, such as anemia, infection and leukemia. It can also help to monitor a patient's response to treatment.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
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KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
2. Message passing vs. Shared memory
Message passing: exchange data
explicitly via IPC
Application developers define
protocol and exchanging format,
number of participants, and each
exchange
Shared memory: all multiple processes
to share data via memory
Applications must locate and and map
shared memory regions to exchange
data
Client
send(msg)
MSG
Client
recv(msg)
MSG
MSG IPC
Client
send(msg)
Client
recv(msg)
Shared
Memory
4. MPI
MPI - Message Passing Interface
• Library standard defined by a committee of vendors, implementers, and
parallel programmers
• Used to create parallel programs based on message passing
Portable: one standard, many implementations
• Available on almost all parallel machines in C and Fortran
• De facto standard platform for the HPC community
5. Groups, Communicators, Contexts
Group: a fixed ordered set of k
processes, i.e., 0, 1, .., k-1
Communicator: specify scope of
communication
• Between processes in a group
• Between two disjoint groups
Context: partition communication space
• A message sent in one context cannot
be received in another context
This image is captured from:
“Writing Message Passing Parallel
Programs with MPI”, Course Notes,
Edinburgh Parallel Computing Centre
The University of Edinburgh
6. Synchronous vs. Asynchronous Message Passing
A synchronous communication is not complete until the
message has been received
An asynchronous communication completes before the
message is received
7. Communication Modes
Synchronous: completes once ack is received by sender
Asynchronous: 3 modes
• Standard send: completes once the message has been sent, which may
or may not imply that the message has arrived at its destination
• Buffered send: completes immediately, if receiver not ready, MPI buffers
the message locally
• Ready send: completes immediately, if the receiver is ready for the
message it will get it, otherwise the message is dropped silently
8. Blocking vs. Non-Blocking
Blocking, means the program will not continue until the
communication is completed
• Synchronous communication
• Barriers: wait for every process in the group to reach a point in
execution
Non-Blocking, means the program will continue, without waiting
for the communication to be completed
10. MPI Basic
Many parallel programs can be written using just these six
functions, only two of which are non-trivial;
– MPI_INIT
– MPI_FINALIZE
– MPI_COMM_SIZE
– MPI_COMM_RANK
– MPI_SEND
– MPI_RECV
11. Skeleton MPI Program (C)
#include <mpi.h>
main(int argc, char** argv)
{
MPI_Init(&argc, &argv);
/* main part of the program */
/* Use MPI function call depend on your data
* partitioning and the parallelization architecture
*/
MPI_Finalize();
}
12. A minimal MPI program (C)
#include “mpi.h”
#include <stdio.h>
int main(int argc, char *argv[])
{
MPI_Init(&argc, &argv);
printf(“Hello, world!n”);
MPI_Finalize();
return 0;
}
13. A minimal MPI program (C)
#include “mpi.h” provides basic MPI definitions and types.
MPI_Init starts MPI
MPI_Finalize exits MPI
Notes:
• Non-MPI routines are local; this “printf” run on each process
• MPI functions return error codes or MPI_SUCCESS
14. Error handling
By default, an error causes all processes to abort
The user can have his/her own error handling routines
Some custom error handlers are available for downloading
from the net
15. Improved Hello (C)
#include <mpi.h>
#include <stdio.h>
int main(int argc, char *argv[])
{
int rank, size;
MPI_Init(&argc, &argv);
/* rank of this process in the communicator */
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
/* get the size of the group associates to the communicator */
MPI_Comm_size(MPI_COMM_WORLD, &size);
printf("I am %d of %dn", rank, size);
MPI_Finalize();
return 0;
}
16. Improved Hello (C)
/* Find out rank, size */
int world_rank, size;
MPI_Comm_rank(MPI_COMM_WORLD, &world_rank);
MPI_Comm_size(MPI_COMM_WORLD, &world_size);
int number;
if (world_rank == 0) {
number = -1;
MPI_Send(&number, 1, MPI_INT, 1, 0, MPI_COMM_WORLD);
} else if (world_rank == 1) {
MPI_Recv(&number, 1, MPI_INT, 0, 0, MPI_COMM_WORLD, MPI_STATUS_IGNORE);
printf("Process 1 received number %d from process 0n", number);
}
Rank of
destination Default
communicator
Tag to identify
message
Number of
elements
Rank of
source
Status
17. Many other functions…
MPI_Bcast: send same piece of
data to all processes in the group
MPI_Scatter: send different
pieces of an array to different
processes (i.e., partition an array
across processes)
From: http://mpitutorial.com/tutorials/mpi-scatter-gather-and-allgather/
18. Many other functions…
MPI_Gather: take elements from
many processes and gathers them
to one single process
• E.g., parallel sorting, searching
From: http://mpitutorial.com/tutorials/mpi-scatter-gather-and-allgather/
19. Many other functions…
MPI_Reduce: takes an array of
input elements on each process
and returns an array of output
elements to the root process
given a specified operation
MPI_Allreduce: Like
MPI_Reduce but distribute
results to all processes
From: http://mpitutorial.com/tutorials/mpi-scatter-gather-and-allgather/
20. MPI Discussion
Gives full control to programmer
• Exposes number of processes
• Communication is explicit, driven by the program
Assume
• Long running processes
• Homogeneous (same performance) processors
Little support for failures, no straggler mitigation
Summary: achieve high performance by hand-optimizing jobs
but requires experts to do so, and little support for fault
tolerance
21. OpenMP
Based on the “Introduction to OpenMP” presentation:
(webcourse.cs.technion.ac.il/236370/Winter2009.../OpenMPLecture.ppt)
22. Motivation
Multicore CPUs are everywhere:
• Servers with over 100 cores today
• Even smartphone CPUs have 8 cores
Multithreading, natural programming model
• All processors share the same memory
• Threads in a process see same address space
• Many shared-memory algorithms developed
23. But…
Multithreading is hard
• Lots of expertise necessary
• Deadlocks and race conditions
• Non-deterministic behavior makes it hard to debug
24. Example
Parallelize the following code using threads:
for (i=0; i<n; i++) {
sum = sum + sqrt(sin(data[i]));
}
Why hard?
• Need mutex to protect the accesses to sum
• Different code for serial and parallel version
• No built-in tuning (# of processors?)
25. OpenMP
A language extension with constructs for parallel programming:
• Critical sections, atomic access, private variables, barriers
Parallelization is orthogonal to functionality
• If the compiler does not recognize OpenMP directives, the code
remains functional (albeit single-threaded)
Industry standard: supported by Intel, Microsoft, IBM, HP
26. OpenMP execution model
Fork and Join: Master thread spawns a team of threads as
needed
Master thread
Master thread
Worker
Thread
FORK
JOIN
FORK
JOIN
Parallel
regions
27. OpenMP memory model
Shared memory model
• Threads communicate by accessing shared variables
The sharing is defined syntactically
• Any variable that is seen by two or more threads is shared
• Any variable that is seen by one thread only is private
Race conditions possible
• Use synchronization to protect from conflicts
• Change how data is stored to minimize the synchronization
28. OpenMP: Work sharing example
answer1 = long_computation_1();
answer2 = long_computation_2();
if (answer1 != answer2) { … }
How to parallelize?
29. OpenMP: Work sharing example
answer1 = long_computation_1();
answer2 = long_computation_2();
if (answer1 != answer2) { … }
How to parallelize?
#pragma omp sections
{
#pragma omp section
answer1 = long_computation_1();
#pragma omp section
answer2 = long_computation_2();
}
if (answer1 != answer2) { … }
30. OpenMP: Work sharing example
Sequential code for (int i=0; i<N; i++) { a[i]=b[i]+c[i]; }
31. OpenMP: Work sharing example
Sequential code
(Semi) manual
parallelization
for (int i=0; i<N; i++) { a[i]=b[i]+c[i]; }
#pragma omp parallel
{
int id = omp_get_thread_num();
int nt = omp_get_num_threads();
int i_start = id*N/nt, i_end = (id+1)*N/nt;
for (int i=istart; i<iend; i++) { a[i]=b[i]+c[i]; }
}
32. OpenMP: Work sharing example
Sequential code
(Semi) manual
parallelization
for (int i=0; i<N; i++) { a[i]=b[i]+c[i]; }
#pragma omp parallel
{
int id = omp_get_thread_num();
int nt = omp_get_num_threads();
int i_start = id*N/nt, i_end = (id+1)*N/nt;
for (int i=istart; i<iend; i++) { a[i]=b[i]+c[i]; }
}
• Launch nt threads
• Each thread uses id
and nt variables to
operate on a different
segment of the arrays
33. OpenMP: Work sharing example
Sequential code
(Semi) manual
parallelization
Automatic
parallelization of
the for loop
using
#parallel for
for (int i=0; i<N; i++) { a[i]=b[i]+c[i]; }
#pragma omp parallel
{
int id = omp_get_thread_num();
int nt = omp_get_num_threads();
int i_start = id*N/nt, i_end = (id+1)*N/nt;
for (int i=istart; i<iend; i++) { a[i]=b[i]+c[i]; }
}
#pragma omp parallel
#pragma omp for schedule(static)
{
for (int i=0; i<N; i++) { a[i]=b[i]+c[i]; }
}
One signed
variable in
the loop
Initialization
:
var = init
Comparison:
var op last,
where
op: <, >, <=, >=
Increment:
var++, var--,
var += incr, var -=
incr
34. Challenges of #parallel for
Load balancing
• If all iterations execute at the same speed, the processors are used optimally
• If some iterations are faster, some processors may get idle, reducing the speedup
• We don’t always know distribution of work, may need to re-distribute dynamically
Granularity
• Thread creation and synchronization takes time
• Assigning work to threads on per-iteration resolution may take more time than the
execution itself
• Need to coalesce the work to coarse chunks to overcome the threading overhead
Trade-off between load balancing and granularity
35. Schedule: controlling work distribution
schedule(static [, chunksize])
• Default: chunks of approximately equivalent size, one to each thread
• If more chunks than threads: assigned in round-robin to the threads
• Why might want to use chunks of different size?
schedule(dynamic [, chunksize])
• Threads receive chunk assignments dynamically
• Default chunk size = 1
schedule(guided [, chunksize])
• Start with large chunks
• Threads receive chunks dynamically. Chunk size reduces
exponentially, down to chunksize
36. OpenMP: Data Environment
Shared Memory programming model
• Most variables (including locals) are shared by threads
{
int sum = 0;
#pragma omp parallel for
for (int i=0; i<N; i++) sum += i;
}
• Global variables are shared
Some variables can be private
• Variables inside the statement block
• Variables in the called functions
• Variables can be explicitly declared as private
37. Overriding storage attributes
private:
• A copy of the variable is created for
each thread
• There is no connection between
original variable and private copies
• Can achieve same using variables
inside { }
firstprivate:
• Same, but the initial value of the
variable is copied from the main
copy
lastprivate:
• Same, but the last value of the
variable is copied to the main copy
int i;
#pragma omp parallel for private(i)
for (i=0; i<n; i++) { … }
int idx=1;
int x = 10;
#pragma omp parallel for
firsprivate(x) lastprivate(idx)
for (i=0; i<n; i++) {
if (data[i] == x)
idx = i;
}
38. Reduction
for (j=0; j<N; j++) {
sum = sum + a[j]*b[j];
}
How to parallelize this code?
• sum is not private, but accessing it atomically is too expensive
• Have a private copy of sum in each thread, then add them up
Use the reduction clause
#pragma omp parallel for reduction(+: sum)
• Any associative operator could be used: +, -, ||, |, *, etc
• The private value is initialized automatically (to 0, 1, ~0 …)
39. #pragma omp reduction
float dot_prod(float* a, float* b, int N)
{
float sum = 0.0;
#pragma omp parallel for reduction(+:sum)
for(int i = 0; i < N; i++) {
sum += a[i] * b[i];
}
return sum;
}
40. Conclusions
OpenMP: A framework for code parallelization
• Available for C++ and FORTRAN
• Based on a standard
• Implementations from a wide selection of vendors
Relatively easy to use
• Write (and debug!) code first, parallelize later
• Parallelization can be incremental
• Parallelization can be turned off at runtime or compile time
• Code is still correct for a serial machine