This document provides an overview of OpenMP, including its basic architecture, programming model, syntax, directives, clauses, and examples. OpenMP is an application programming interface used to write multi-threaded programs for shared memory multiprocessing. It utilizes compiler directives, library routines and environment variables to define parallel regions of code, distribute loops, and manage data in memory. The document discusses OpenMP's parallel execution model, work sharing constructs, data environment, synchronization methods, and best practices for utilizing its features to efficiently parallelize code.
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.
This document provides an introduction to GPU programming using OpenMP. It discusses what OpenMP is, how the OpenMP programming model works, common OpenMP directives and constructs for parallelization including parallel, simd, work-sharing, tasks, and synchronization. It also covers how to write, compile and run an OpenMP program. Finally, it describes how OpenMP can be used for GPU programming using target directives to offload work to the device and data mapping clauses to manage data transfer between host and device.
Traditionally, computer software has been written for serial computation. To solve a problem, an algorithm is constructed and implemented as a serial stream of instructions. These instructions are executed on a central processing unit on one computer. Only one instruction may execute at a time—after that instruction is finished, the next is executed.
This document provides an introduction to OpenMP, which is an application programming interface that allows for parallel programming on shared memory architectures. OpenMP is a specification for parallel programming and is supported by C, C++, and Fortran. It uses compiler directives to specify parallel regions, shared/private variables, thread synchronization, and work distribution among threads. OpenMP uses a shared memory model and fork-join execution model, where the main thread forks additional threads to perform work and then joins them back together.
Concurrent Programming OpenMP @ Distributed System DiscussionCherryBerry2
This powerpoint presentation discusses OpenMP, a programming interface that allows for parallel programming on shared memory architectures. It covers the basic architecture of OpenMP, its core elements like directives and runtime routines, advantages like portability, and disadvantages like potential synchronization bugs. Examples are provided of using OpenMP directives to parallelize a simple "Hello World" program across multiple threads. Fine-grained and coarse-grained parallelism are also defined.
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.
This document provides an introduction to GPU programming using OpenMP. It discusses what OpenMP is, how the OpenMP programming model works, common OpenMP directives and constructs for parallelization including parallel, simd, work-sharing, tasks, and synchronization. It also covers how to write, compile and run an OpenMP program. Finally, it describes how OpenMP can be used for GPU programming using target directives to offload work to the device and data mapping clauses to manage data transfer between host and device.
Traditionally, computer software has been written for serial computation. To solve a problem, an algorithm is constructed and implemented as a serial stream of instructions. These instructions are executed on a central processing unit on one computer. Only one instruction may execute at a time—after that instruction is finished, the next is executed.
This document provides an introduction to OpenMP, which is an application programming interface that allows for parallel programming on shared memory architectures. OpenMP is a specification for parallel programming and is supported by C, C++, and Fortran. It uses compiler directives to specify parallel regions, shared/private variables, thread synchronization, and work distribution among threads. OpenMP uses a shared memory model and fork-join execution model, where the main thread forks additional threads to perform work and then joins them back together.
Concurrent Programming OpenMP @ Distributed System DiscussionCherryBerry2
This powerpoint presentation discusses OpenMP, a programming interface that allows for parallel programming on shared memory architectures. It covers the basic architecture of OpenMP, its core elements like directives and runtime routines, advantages like portability, and disadvantages like potential synchronization bugs. Examples are provided of using OpenMP directives to parallelize a simple "Hello World" program across multiple threads. Fine-grained and coarse-grained parallelism are also defined.
The document summarizes a lecture on task parallelism in OpenMP. It discusses using tasks to parallelize a linked list traversal, which is difficult to express with parallel for. Tasks allow expressing the parallel work without having to explicitly count iterations. The document also reviews other OpenMP constructs like locks, conditional execution with parallel, sections for multi-section parallel code, and the single and master directives.
Presentació a càrrec de Cristian
Gomollon, tècnic d'Aplicacions al CSUC, duta a terme a la "4a Jornada de formació sobre l'ús del servei de càlcul" celebrada el 17 de març de 2021 en format virtual.
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.
Presentació a càrrec de Cristian Gomollon, tècnic d'Aplicacions al CSUC, duta a terme a la "5a Jornada de formació sobre l'ús del servei de càlcul" celebrada el 16 de març de 2021 en format virtual.
How a BEAM runner executes a pipeline. Apache BEAM Summit London 2018javier ramirez
In this talk I will present the architecture that allows runners to execute a Beam pipeline. I will explain what needs to happen in order for a compatible runner to know which transforms to run, how to pass data from one step to the next, and how beam allows runners to be SDK agnostic when running pipelines.
The document provides an overview of techniques for faster computation in MATLAB, including:
- Closing other programs and setting the CPU to high performance to improve performance.
- Preallocating variables to avoid recalculating memory requirements in loops.
- Using suppression with semicolons to avoid displaying variable outputs.
- Converting variables to more efficient data types like single precision.
- Implementing parallel processing on CPUs and GPUs for large datasets using techniques like vectorization and avoiding dependencies between iterations.
OpenMP directives are used to parallelize sequential programs. The key directives discussed include:
1. Parallel and parallel for to execute loops or code blocks across multiple threads.
2. Sections and parallel sections to execute different code blocks simultaneously in parallel across threads.
3. Critical to ensure a code block is only executed by one thread at a time for mutual exclusion.
4. Single to restrict a code block to only be executed by one thread.
OpenMP makes it possible to easily convert sequential programs to leverage multiple threads and processors through directives like these.
This talk was given at GTC16 by James Beyer and Jeff Larkin, both members of the OpenACC and OpenMP committees. It's intended to be an unbiased discussion of the differences between the two languages and the tradeoffs to each approach.
Understand and Harness the Capabilities of Intel® Xeon Phi™ ProcessorsIntel® Software
The second-generation Intel® Xeon Phi™ processor offers new and enhanced features that provide significant performance gains in modernized code. For this lab, we pair these features with Intel® Software Development Products and methodologies to enable developers to gain insights on application behavior and to find opportunities to optimize parallelism, memory, and vectorization features.
GDB can debug programs by running them under its control. It allows inspecting and modifying program state through breakpoints, watchpoints, and examining variables and memory. GDB supports debugging optimized code, multi-threaded programs, and performing tasks like stepping, continuing, and backtracing through the call stack. It can also automate debugging through commands, scripts, and breakpoint actions.
Migration To Multi Core - Parallel Programming ModelsZvi Avraham
The document discusses multi-core and many-core processors and parallel programming models. It provides an overview of hardware trends including increasing numbers of cores in CPUs and GPUs. It also covers parallel programming approaches like shared memory, message passing, data parallelism and task parallelism. Specific APIs discussed include Win32 threads, OpenMP, and Intel TBB.
This document discusses utilizing multicore processors with OpenMP. It provides an overview of OpenMP, including that it is an industry standard for parallel programming in C/C++ that supports parallelizing loops and tasks. Examples are given of using OpenMP to parallelize particle system position calculation and collision detection across multiple threads. Performance tests on dual-core and triple-core systems show speedups of 2-5x from using OpenMP. Some limitations of OpenMP are also outlined.
The document discusses different OpenMP constructs for parallelizing work across multiple threads. It describes how a parallel for loop can distribute loop iterations across threads to execute work concurrently. A parallel sections construct allows assigning different tasks to different threads without dependencies between sections. The nowait clause with parallel for or sections avoids implicit barriers between statements.
This document provides an overview of MPI (Message Passing Interface) and OpenMP (Open Multi-Processing) for parallel programming. MPI is a standard for message passing between processes across distributed memory systems. It uses send and receive operations as basic building blocks. OpenMP is an API that supports shared memory multiprocessing programming using directives like parallel and threads. It uses a fork-join model where the master thread creates worker threads for parallel regions. Both approaches have pros and cons for different types of parallel architectures and problems.
This document provides an introduction to OpenMP, a standard for parallel programming using shared memory. OpenMP uses compiler directives like #pragma omp parallel to create threads that can access shared data. It uses a fork-join model where the master thread creates worker threads to execute blocks of code in parallel. OpenMP supports work sharing constructs like parallel for loops and sections to distribute work among threads, and synchronization constructs like barriers to coordinate thread execution. Variables can be declared as private to each thread or shared among all threads.
Building a Perl5 smoketest environment in Docker using CPAN::Reporter::Smoker. Includes an overview of "smoke testing", shell commands to contstruct a hybrid environment with underlying O/S image and data volumes for /opt, /var/lib/CPAN. This allows maintaining the Perly smoke environemnt without having to rebuild it.
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.
OpenMP is an API used for multi-threaded parallel programming on shared memory machines. It uses compiler directives, runtime libraries and environment variables. OpenMP supports C/C++ and Fortran. The programming model uses a fork-join execution model with explicit parallelism defined by the programmer. Compiler directives like #pragma omp parallel are used to define parallel regions. Work is shared between threads using constructs like for, sections and tasks. Synchronization is implemented using barriers, critical sections and locks.
HC-4021, Efficient scheduling of OpenMP and OpenCL™ workloads on Accelerated ...AMD Developer Central
Presentation HC-4021, Efficient scheduling of OpenMP and OpenCL™ workloads on Accelerated Processing Units, by Robert Engel at the AMD Developer Summit (APU13) Nov. 11-13, 2013.
- OpenMP provides compiler directives and library calls to incrementally parallelize applications for shared memory multiprocessor systems. It works by allowing the master thread to spawn worker threads to perform work concurrently using directives like parallel and parallel do.
- Variables in OpenMP can be shared, private, or reduction. Shared variables are accessible by all threads while private variables have a separate copy for each thread. Reduction variables are used to combine values across threads.
- Synchronization is needed to coordinate thread access and ensure correct results. The barrier directive synchronizes threads at the end of parallel regions.
Blood finder application project report (1).pdfKamal Acharya
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
The document summarizes a lecture on task parallelism in OpenMP. It discusses using tasks to parallelize a linked list traversal, which is difficult to express with parallel for. Tasks allow expressing the parallel work without having to explicitly count iterations. The document also reviews other OpenMP constructs like locks, conditional execution with parallel, sections for multi-section parallel code, and the single and master directives.
Presentació a càrrec de Cristian
Gomollon, tècnic d'Aplicacions al CSUC, duta a terme a la "4a Jornada de formació sobre l'ús del servei de càlcul" celebrada el 17 de març de 2021 en format virtual.
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.
Presentació a càrrec de Cristian Gomollon, tècnic d'Aplicacions al CSUC, duta a terme a la "5a Jornada de formació sobre l'ús del servei de càlcul" celebrada el 16 de març de 2021 en format virtual.
How a BEAM runner executes a pipeline. Apache BEAM Summit London 2018javier ramirez
In this talk I will present the architecture that allows runners to execute a Beam pipeline. I will explain what needs to happen in order for a compatible runner to know which transforms to run, how to pass data from one step to the next, and how beam allows runners to be SDK agnostic when running pipelines.
The document provides an overview of techniques for faster computation in MATLAB, including:
- Closing other programs and setting the CPU to high performance to improve performance.
- Preallocating variables to avoid recalculating memory requirements in loops.
- Using suppression with semicolons to avoid displaying variable outputs.
- Converting variables to more efficient data types like single precision.
- Implementing parallel processing on CPUs and GPUs for large datasets using techniques like vectorization and avoiding dependencies between iterations.
OpenMP directives are used to parallelize sequential programs. The key directives discussed include:
1. Parallel and parallel for to execute loops or code blocks across multiple threads.
2. Sections and parallel sections to execute different code blocks simultaneously in parallel across threads.
3. Critical to ensure a code block is only executed by one thread at a time for mutual exclusion.
4. Single to restrict a code block to only be executed by one thread.
OpenMP makes it possible to easily convert sequential programs to leverage multiple threads and processors through directives like these.
This talk was given at GTC16 by James Beyer and Jeff Larkin, both members of the OpenACC and OpenMP committees. It's intended to be an unbiased discussion of the differences between the two languages and the tradeoffs to each approach.
Understand and Harness the Capabilities of Intel® Xeon Phi™ ProcessorsIntel® Software
The second-generation Intel® Xeon Phi™ processor offers new and enhanced features that provide significant performance gains in modernized code. For this lab, we pair these features with Intel® Software Development Products and methodologies to enable developers to gain insights on application behavior and to find opportunities to optimize parallelism, memory, and vectorization features.
GDB can debug programs by running them under its control. It allows inspecting and modifying program state through breakpoints, watchpoints, and examining variables and memory. GDB supports debugging optimized code, multi-threaded programs, and performing tasks like stepping, continuing, and backtracing through the call stack. It can also automate debugging through commands, scripts, and breakpoint actions.
Migration To Multi Core - Parallel Programming ModelsZvi Avraham
The document discusses multi-core and many-core processors and parallel programming models. It provides an overview of hardware trends including increasing numbers of cores in CPUs and GPUs. It also covers parallel programming approaches like shared memory, message passing, data parallelism and task parallelism. Specific APIs discussed include Win32 threads, OpenMP, and Intel TBB.
This document discusses utilizing multicore processors with OpenMP. It provides an overview of OpenMP, including that it is an industry standard for parallel programming in C/C++ that supports parallelizing loops and tasks. Examples are given of using OpenMP to parallelize particle system position calculation and collision detection across multiple threads. Performance tests on dual-core and triple-core systems show speedups of 2-5x from using OpenMP. Some limitations of OpenMP are also outlined.
The document discusses different OpenMP constructs for parallelizing work across multiple threads. It describes how a parallel for loop can distribute loop iterations across threads to execute work concurrently. A parallel sections construct allows assigning different tasks to different threads without dependencies between sections. The nowait clause with parallel for or sections avoids implicit barriers between statements.
This document provides an overview of MPI (Message Passing Interface) and OpenMP (Open Multi-Processing) for parallel programming. MPI is a standard for message passing between processes across distributed memory systems. It uses send and receive operations as basic building blocks. OpenMP is an API that supports shared memory multiprocessing programming using directives like parallel and threads. It uses a fork-join model where the master thread creates worker threads for parallel regions. Both approaches have pros and cons for different types of parallel architectures and problems.
This document provides an introduction to OpenMP, a standard for parallel programming using shared memory. OpenMP uses compiler directives like #pragma omp parallel to create threads that can access shared data. It uses a fork-join model where the master thread creates worker threads to execute blocks of code in parallel. OpenMP supports work sharing constructs like parallel for loops and sections to distribute work among threads, and synchronization constructs like barriers to coordinate thread execution. Variables can be declared as private to each thread or shared among all threads.
Building a Perl5 smoketest environment in Docker using CPAN::Reporter::Smoker. Includes an overview of "smoke testing", shell commands to contstruct a hybrid environment with underlying O/S image and data volumes for /opt, /var/lib/CPAN. This allows maintaining the Perly smoke environemnt without having to rebuild it.
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.
OpenMP is an API used for multi-threaded parallel programming on shared memory machines. It uses compiler directives, runtime libraries and environment variables. OpenMP supports C/C++ and Fortran. The programming model uses a fork-join execution model with explicit parallelism defined by the programmer. Compiler directives like #pragma omp parallel are used to define parallel regions. Work is shared between threads using constructs like for, sections and tasks. Synchronization is implemented using barriers, critical sections and locks.
HC-4021, Efficient scheduling of OpenMP and OpenCL™ workloads on Accelerated ...AMD Developer Central
Presentation HC-4021, Efficient scheduling of OpenMP and OpenCL™ workloads on Accelerated Processing Units, by Robert Engel at the AMD Developer Summit (APU13) Nov. 11-13, 2013.
- OpenMP provides compiler directives and library calls to incrementally parallelize applications for shared memory multiprocessor systems. It works by allowing the master thread to spawn worker threads to perform work concurrently using directives like parallel and parallel do.
- Variables in OpenMP can be shared, private, or reduction. Shared variables are accessible by all threads while private variables have a separate copy for each thread. Reduction variables are used to combine values across threads.
- Synchronization is needed to coordinate thread access and ensure correct results. The barrier directive synchronizes threads at the end of parallel regions.
Blood finder application project report (1).pdfKamal Acharya
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
ELS: 2.4.1 POWER ELECTRONICS Course objectives: This course will enable stude...Kuvempu University
Introduction - Applications of Power Electronics, Power Semiconductor Devices, Control Characteristics of Power Devices, types of Power Electronic Circuits. Power Transistors: Power BJTs: Steady state characteristics. Power MOSFETs: device operation, switching characteristics, IGBTs: device operation, output and transfer characteristics.
Thyristors - Introduction, Principle of Operation of SCR, Static Anode- Cathode Characteristics of SCR, Two transistor model of SCR, Gate Characteristics of SCR, Turn-ON Methods, Turn-OFF Mechanism, Turn-OFF Methods: Natural and Forced Commutation – Class A and Class B types, Gate Trigger Circuit: Resistance Firing Circuit, Resistance capacitance firing circuit.
Properties of Fluids, Fluid Statics, Pressure MeasurementIndrajeet sahu
Properties of Fluids: Density, viscosity, surface tension, compressibility, and specific gravity define fluid behavior.
Fluid Statics: Studies pressure, hydrostatic pressure, buoyancy, and fluid forces on surfaces.
Pressure at a Point: In a static fluid, the pressure at any point is the same in all directions. This is known as Pascal's principle. The pressure increases with depth due to the weight of the fluid above.
Hydrostatic Pressure: The pressure exerted by a fluid at rest due to the force of gravity. It can be calculated using the formula P=ρghP=ρgh, where PP is the pressure, ρρ is the fluid density, gg is the acceleration due to gravity, and hh is the height of the fluid column above the point in question.
Buoyancy: The upward force exerted by a fluid on a submerged or partially submerged object. This force is equal to the weight of the fluid displaced by the object, as described by Archimedes' principle. Buoyancy explains why objects float or sink in fluids.
Fluid Pressure on Surfaces: The analysis of pressure forces on surfaces submerged in fluids. This includes calculating the total force and the center of pressure, which is the point where the resultant pressure force acts.
Pressure Measurement: Manometers, barometers, pressure gauges, and differential pressure transducers measure fluid pressure.
3rd International Conference on Artificial Intelligence Advances (AIAD 2024)GiselleginaGloria
3rd International Conference on Artificial Intelligence Advances (AIAD 2024) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the area advanced Artificial Intelligence. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the research area. Core areas of AI and advanced multi-disciplinary and its applications will be covered during the conferences.
A high-Speed Communication System is based on the Design of a Bi-NoC Router, ...DharmaBanothu
The Network on Chip (NoC) has emerged as an effective
solution for intercommunication infrastructure within System on
Chip (SoC) designs, overcoming the limitations of traditional
methods that face significant bottlenecks. However, the complexity
of NoC design presents numerous challenges related to
performance metrics such as scalability, latency, power
consumption, and signal integrity. This project addresses the
issues within the router's memory unit and proposes an enhanced
memory structure. To achieve efficient data transfer, FIFO buffers
are implemented in distributed RAM and virtual channels for
FPGA-based NoC. The project introduces advanced FIFO-based
memory units within the NoC router, assessing their performance
in a Bi-directional NoC (Bi-NoC) configuration. The primary
objective is to reduce the router's workload while enhancing the
FIFO internal structure. To further improve data transfer speed,
a Bi-NoC with a self-configurable intercommunication channel is
suggested. Simulation and synthesis results demonstrate
guaranteed throughput, predictable latency, and equitable
network access, showing significant improvement over previous
designs
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...Transcat
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
Will has worked in the renewable energy industry since 2005, first as an installer for a small east coast solar integrator before adding sales, design, and project management to his skillset. In 2022, Will joined Fluke as a solar application specialist, where he supports their renewable energy testing equipment like IV-curve tracers, electrical meters, and thermal imaging cameras. Experienced in wind power, solar thermal, energy storage, and all scales of PV, Will has primarily focused on residential and small commercial systems. He is passionate about implementing high-quality, code-compliant installation techniques.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
Particle Swarm Optimization–Long Short-Term Memory based Channel Estimation w...IJCNCJournal
Paper Title
Particle Swarm Optimization–Long Short-Term Memory based Channel Estimation with Hybrid Beam Forming Power Transfer in WSN-IoT Applications
Authors
Reginald Jude Sixtus J and Tamilarasi Muthu, Puducherry Technological University, India
Abstract
Non-Orthogonal Multiple Access (NOMA) helps to overcome various difficulties in future technology wireless communications. NOMA, when utilized with millimeter wave multiple-input multiple-output (MIMO) systems, channel estimation becomes extremely difficult. For reaping the benefits of the NOMA and mm-Wave combination, effective channel estimation is required. In this paper, we propose an enhanced particle swarm optimization based long short-term memory estimator network (PSOLSTMEstNet), which is a neural network model that can be employed to forecast the bandwidth required in the mm-Wave MIMO network. The prime advantage of the LSTM is that it has the capability of dynamically adapting to the functioning pattern of fluctuating channel state. The LSTM stage with adaptive coding and modulation enhances the BER.PSO algorithm is employed to optimize input weights of LSTM network. The modified algorithm splits the power by channel condition of every single user. Participants will be first sorted into distinct groups depending upon respective channel conditions, using a hybrid beamforming approach. The network characteristics are fine-estimated using PSO-LSTMEstNet after a rough approximation of channels parameters derived from the received data.
Keywords
Signal to Noise Ratio (SNR), Bit Error Rate (BER), mm-Wave, MIMO, NOMA, deep learning, optimization.
Volume URL: https://airccse.org/journal/ijc2022.html
Abstract URL:https://aircconline.com/abstract/ijcnc/v14n5/14522cnc05.html
Pdf URL: https://aircconline.com/ijcnc/V14N5/14522cnc05.pdf
#scopuspublication #scopusindexed #callforpapers #researchpapers #cfp #researchers #phdstudent #researchScholar #journalpaper #submission #journalsubmission #WBAN #requirements #tailoredtreatment #MACstrategy #enhancedefficiency #protrcal #computing #analysis #wirelessbodyareanetworks #wirelessnetworks
#adhocnetwork #VANETs #OLSRrouting #routing #MPR #nderesidualenergy #korea #cognitiveradionetworks #radionetworks #rendezvoussequence
Here's where you can reach us : ijcnc@airccse.org or ijcnc@aircconline.com
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
2. Centre for Development of Advanced Computing
Contents
➢ Basic Architecture
➢ Sequential Program Execution
➢ Introduction to OpenMP
➢ OpenMP Programming Model
➢ OpenMP Syntax and Execution
➢ OpenMP Directives and Clauses
➢ Race Condition
➢ Environment variables
➢ OpenMP examples
➢ Good things and Limitation of OpenMP
➢ References
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Parallel programing Architectures/Model ..
❏ Shared-memory Model ❏ Distributed-memory Model
❖ openMP
❖ Pthreads ...
❖ MPI - Message Passing
Interface
❏ Hybrid model
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Sequential Program Execution
When you run sequential
program
➢ Instructions executed in serial
➢ Other cores are idle
Waste of available resource...
We want all cores to be used
to execute program.
HOW ?
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OpenMP Introduction
➢ Open Specification for Multi Processing.
➢ It is an specification for
➢ OpenMP is an Application Program Interface
(API) for writing multi-threaded, shared
memory parallelism.
➢ Easy to create multi-threaded programs in
C,C++ and Fortran.
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OpenMP Versions
➢ Fortran 1.0 was released in October 1997
○ C/C++ 1.0 was approved in November 1998
○ version 2.5 joint specification in May 2005
○ OpenMP 3.0 API released May 2008
○ Version 4.0 released in July 2013
○ Version 4.5 released in Nov 2015
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Why Choose OpenMP ?
➢ Portable
○ standardized for shared memory architectures
➢ Simple and Quick
○ Relatively easy to do parallelization for small parts of
an application at a time
○ Incremental parallelization
○ Supports both fine grained and coarse grained
parallelism
➢ Compact API
○ Simple and limited set of directives
○ Not automatic parallelization
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Execution Model
➢ OpenMP program starts single threaded
➢ To create additional threads, user starts a parallel region
○ additional threads are launched to create a team
○ original (master) thread is part of the team
○ threads “go away” at the end of the parallel region:
usually sleep or spin
➢ Repeat parallel regions as necessary
Fork-join model
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OpenMP Programming Model
#pragma omp parallel
{
……
…...
}
main (..)
{
#pragma omp parallel
{
……
…...
}
}
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Communication Between Threads
➢ Shared Memory Model
○ threads read and write shared variable no need for explicit
message passing
○ use synchronization to protect against race conditions
○ change storage attributes for minimizing synchronization and
improving cache reuse
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When to use openMP ?
➢ Target platform is multi core or
multiprocessor.
➢ Application is cross-platform
➢ Parallelizable loop
➢ Last-minute optimization
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OpenMP Basic Syntax
➢ Header file
#include <omp.h>
➢ Parallel region:
#pragma omp construct_name [clause, clause...]
{
// .. Do some work here
}
// end of parallel region/block
Environment variables and functions
➢ In Fortran:
!$OMP construct [clause [clause] .. ]
C$OMP construct [clause [clause] ..]
*$OMP construct [clause [clause] ..]
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Executing OpenMP Program
Compilation
gcc –fopenmp <program name> –o <execcutable>
gfortran –fopenmp <program name> –o <execcutable>
ifort <program name> -qopenmp –o <execcutable>
icc <program name> -qopenmp –o <execcutable>
Execution:
./ <executable-name>
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Compiler support for OpenMP
Compiler Flag
GNU (gcc/g++) -fopenmp
Intel (icc) -qopenmp
Sun -xopenmp
Many more….
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OpenMP Constructs
➢ Parallel Regions
#pragma omp parallel
➢ Worksharing
#pragma omp for, #pragma omp sections
➢ Data Environment
#pragma omp parallel shared/private (...)
➢ Synchronization
#pragma omp barrier, #pragma omp critical
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Parallel Region
➢ Fork a team of N threads {0.... N-1}
➢ Without it, all codes are sequential
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Loop Constructs: Parallel do/for
In C:
#pragma omp parallel for
for(i=0; i<n; i++)
a[i] = b[i] + c[i]
In Fortran:
!$omp parallel do
Do i=1, n
a(i) = b(i) +c(i)
enddo
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Clauses for Parallel constructs
➢ shared
➢ private
➢ firstprivate
➢ lastprivate
➢ nowait
#pragma omp parallel [clause, clause, ...]
➢ if
➢ reduction
➢ Schedule
➢ default
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private Clause
➢ The values of private data are undefined upon
entry to and exit from the specific construct.
➢ Loop iteration variable is private by default.
Example:
#prgma omp parallel for private(a)
for(i=0; i<n; i++)
{
a=i+1;
printf(“Thread %d has value of a=%d for i=%dn”,
omp_get_thread_num(),a,i)
}
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firstprivate Clause
➢ The clause combines behavior of private
clause with automatic initialization of the
variables in its list.
Example:
b=50,n=1858;
printf(“Before parallel loop: b=%d ,n=%dn”,b,n)
#pragma omp parallel for private(i), firstprivate(b), lastprivate(a)
for(i=0; i<n; i++)
{
a=i+b;
}
c=a+b;
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lastprivate Clause
➢ Performs finalization of private variables.
➢ Each thread has its own copy.
Example:
b=50,n=1858;
printf(“Before parallel loop: b=%d ,n=%dn”,b,n)
#pragma omp parallel for private(i), firstprivate(b), lastprivate(a)
for(i=0; i<n; i++)
{
a=i+b;
}
c=a+b;
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shared Clause
➢ Shared among team of threads.
➢ Each thread can modify shared variables.
➢ Data corruption is possible when multiple threads
attempt to update the same memory location.
➢ Data correctness is user’s responsibility.
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default Clause
➢ Defines the default data scope within parallel
region.
➢ default(private | shared | none).
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nowait Clause
➢ Allow threads that finish earlier to proceed
without waiting.
➢ If specified, then threads do not synchronize
at the end of parallel loop.
#pragma omp parallel nowait
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reduction Clause
➢ Performs a reduction on variables subject to
given operator.
#pragma omp parallel for reduction(+ : result)
for (unsigned i = 0; i < v.size(); i++)
{
result += v[i];
...
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schedule Clause
➢ Specifies how loop iteration are divided
among team of threads.
➢ Supported Scheduling Classes:
○ Static
○ Dynamic
○ Guided
○ runtime
#pragma omp parallel for schedule (type,[chunk size])
{
// ...some stuff
}
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schedule Clause.. cont
schedule(static, [n])
➢ Each thread is assigned chunks in “round robin” fashion, known as
block cyclic scheduling.
➢ If n has not been specified, it will contain
CEILING(number_of_iterations / number_of_threads) iterations.
○ Example: loop of length 16, 3 threads, chunk size 2:
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schedule Clause.. cont
schedule(dynamic, [n])
➢ Iteration of loop are divided into chunks containing n iterations
each.
➢ Default chunk size is 1.
!$omp do schedule (dynamic)
do i=1,8
… (loop body)
end do
!$omp end do
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schedule(guided, [n])
➢ If you specify n, that is the minimum chunk size that each thread
should posses.
➢ Size of each successive chunks is exponentially decreasing.
➢ initial chunk size
max((num_of_iterations/num_of_threads),n)
Subsequent chunks consist of
max(remaining_iterations/number_of_threads,n) iterations.
Schedule(runtime): export OMP_SCHEDULE “STATIC, 4”
➢ Determine the scheduling type at run time by the
OMP_SCHEDULE environment variable.
schedule Clause.. cont
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Work sharing : Section Directive
➢ Each section is executed exactly once and one thread executes
one section
#pragma omp parallel
#pragma omp sections
{
#pragma omp section
x_calculation();
#pragma omp section
y_calculation();
#pragma omp section
z_calculation();
}
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Designated section is executed by single thread only.
#pragma omp single
{
a =10;
}
#pragma omp for
{
for (i=0;i<N;i++)
b[i] = a;
}
Work sharing : Single Directive
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➢ Similar to single, but code block will be executed by the master
thread only.
#pragma omp master
{
a =10;
}
#pragma omp for
{
for (i=0;i<N;i++)
b[i] = a;
}
Work sharing : Master
C/C++:
#pragma omp master
----- block of code--
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Race condition
Finding the largest
element in a list of
numbers.
Max = 10
!$omp parallel do
do i=1,n
if(a(i) .gt. Max) then
Max = a(i)
endif
enddo
Thread 0
Read a(i) value = 12
Read Max value = 10
If (a(i) > Max) (12 > 10)
Max = a(i) (i.e. 12)
Thread 1
Read a(i) value = 11
Read Max value = 10
If (a(i) > Max) (11 > 10)
Max = a(i) (i.e. 11
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Synchronization: Critical Section
Directive restrict access to the enclosed code to only one thread at a
time. cnt = 0, f=7;
#pragma omp parallel
{
#pragma omp for
for(i=0;i<20;i++)
{
if(b[i] == 0)
{
#pragma omp
critical
cnt++;
}
a[i] = b[i] + f*(i+1);
}
}
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Synchronization:Barrier Directive
Synchronizes all the threads in a team.
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int x=2;
#pragma omp parallel shared(x)
{
int tid = omp_get_thread_num();
if(tid == 0)
x=5;
else
printf(“[1] thread %2d:
x=%dn”,tid,x);
#pragma omp barrier
printf(“[2] thread %2d:
x=%dn”,tid,x);
}
Synchronization:Barrier Directive
Some threads may still have
x=2 here
Cache flush + thread
synchronization
All threads have x=5 here
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➢ Mini Critical section.
➢ Specific memory location must be updated atomically.
Synchronization:Atomic Directive
C:
#pragma omp atomic
----- Single line code--
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Environment Variable
➢ To set number of threads during execution
export OMP_NUM_THREADS=4
➢ To allow run time system to determine the number of
threads
export OMP_DYNAMIC=TRUE
➢ To allow nesting of parallel region
export OMP_NESTED=TRUE
➢ Get thread ID
omp_get_thread_num()
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Control the Number of Threads
➢ Parallel region
#pragma omp parallel num_threads(integer)
➢ Run-time function/ICV
omp_set_num_threads(integer)
➢ Environment Variable
OMP_NUM_THREADS
Higher Priority
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Good Things about OpenMP
➢ Incrementally Parallelization of sequential code.
➢ Leave thread management to compiler.
➢ Directly supported by compiler.
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Limitations
➢ Internal details are hidden
➢ Run efficiently in shared-memory multiprocessor
platforms only but not on distributed memory
➢ Limited performance by memory architecture
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References
https://computing.llnl.gov/tutorials/openMP/
http://wiki.scinethpc.ca/wiki/images/9/9b/D s-openmp.pdf
www.openmp.org/
http://openmp.org/sc13/OpenMP4.0_Intro_Y onghongYan_SC13.pdf
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Task Construct
int fib(int n)
{
int i, j;
if (n<2) return n;
else
{
#pragma omp task shared(i) firstprivate(n)
i=fib(n-1);
#pragma omp task shared(j) firstprivate(n)
j=fib(n-2);
#pragma omp taskwait
return i+j;
}
}
● Tasks in OpenMP are code blocks that the compiler wraps up and
makes available to be executed in parallel.
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Taskloop Construct
#pragma omp taskgroup
{
for (int tmp = 0; tmp < 32;
tmp++)
#pragma omp task
for (long l = tmp * 32; l
< tmp * 32 + 32; l++)
do_something (l);
}
#pragma omp taskloop
num_tasks (32)
for (long l = 0; l <
1024; l++)
do_something (l);
OpenMP 4.0 OpenMP 4.5
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OpenMP 4.5 Features
• Explicit map clause
• Target Construct
• Target data Construct
• Target Update Construct
• Declare target
• Teams construct
• Device run time routines
• Target Memory and device pointer routines
• Thread Affinity
• Linear clause in loop construct
• Asynchronous target execution with nowait clause
• Doacross loop construct
• SIMD construct
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Environment Variable
➢ To set number of threads during execution
export OMP_NUM_THREADS=4
➢ To allow run time system to determine the number of
threads
export OMP_DYNAMIC=TRUE
➢ To allow nesting of parallel region
export OMP_NESTED=TRUE
➢ Get thread ID
omp_get_thread_num()
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Control the Number of Threads
➢ Parallel region
#pragma omp parallel num_threads(integer)
➢ Run-time function/ICV
omp_set_num_threads(integer)
➢ Environment Variable
OMP_NUM_THREADS
Higher Priority
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OPENMP 3.1 TO 4+ FEATURES
Openmp 3.0 Openmp 4.0 to 4.0.2 Openmp 4.0.2 to 4.5
Parallel construct
Worksharing
construct
Synronization
construct Data
sharing clauses Task
Construct
Proc_bind clause
Task group construct
Task dependences
Target construct
Target update
construct
Declare target
construct Teams
construct Device
runtime routines
Distribute construct
Task Priority
Task loop construct
Device memory
routines and device
pointers Do across
loop construct Linear
clause in loop
construct
Cancellation
Construct
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Good Things about OpenMP
➢ Incrementally Parallelization of sequential code.
➢ Leave thread management to compiler.
➢ Directly supported by compiler.
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Limitations
➢ Internal details are hidden
➢ Run efficiently in shared-memory multiprocessor
platforms only but not on distributed memory
➢ Limited performance by memory architecture
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References
https://computing.llnl.gov/tutorials/openMP/
http://wiki.scinethpc.ca/wiki/images/9/9b/D s-openmp.pdf
www.openmp.org/
http://openmp.org/sc13/OpenMP4.0_Intro_Y onghongYan_SC13.pdf
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References
https://www.openmp.org/#
https://www.openmp.org/resources/openmp-benchmarks/
https://www.openmp.org/resources/openmp-presentations/
https://www.openmp.org/resources/openmp-presentations/sc-19-
booth-talks/
https://www.openmp.org/events/sc21/