This document provides an overview of programming a heterogeneous computing cluster using the Message Passing Interface (MPI). It begins with background on heterogeneous computing and MPI. It then discusses the MPI programming model and environment management routines. A vector addition example is presented to demonstrate an MPI implementation. Point-to-point and collective communication routines are explained. Finally, it covers groups, communicators, and virtual topologies in MPI programming.
This document discusses software for parallel programming, including parallel programming models, languages, compilers, and environments. It covers shared memory and message passing models, as well as data parallel programming. It also discusses language features that support parallelism, parallel language constructs, optimizing compilers for parallelism, and integrated parallel programming environments. Key topics are parallel debugging, performance monitoring, and program visualization tools. Representative parallel programming environments including Cray, Intel Paragon, and CM-5 software are also summarized.
Heterogeneous computing refers to systems that use more than one type of processor or core. It allows integration of CPUs and GPUs on the same bus, with shared memory and tasks. This is called the Heterogeneous System Architecture (HSA). The HSA aims to reduce latency between devices and make them more compatible for programming. Programming models for HSA include OpenCL, CUDA, and hUMA. Heterogeneous computing is used in platforms like smartphones, laptops, game consoles, and APUs from AMD. It provides benefits like increased performance, lower costs, and better battery life over traditional CPUs, but discrete CPUs and GPUs can provide more power and new software models are needed.
Parallel platforms can be organized in various ways, from an ideal parallel random access machine (PRAM) to more conventional architectures. PRAMs allow concurrent access to shared memory and can be divided into subclasses based on how simultaneous memory accesses are handled. Physical parallel computers use interconnection networks to provide communication between processing elements and memory. These networks include bus-based, crossbar, multistage, and various topologies like meshes and hypercubes. Maintaining cache coherence across multiple processors is important and can be achieved using invalidate protocols, directories, and snooping.
A natural extension of the Random Access Machine (RAM) serial architecture is the Parallel Random Access Machine, or PRAM.
PRAMs consist of p processors and a global memory of unbounded size that is uniformly accessible to all processors.
Processors share a common clock but may execute different instructions in each cycle.
The primary reasons for using parallel computing:
Save time - wall clock time
Solve larger problems
Provide concurrency (do multiple things at the same time)
MPI provides point-to-point and collective communication capabilities. Point-to-point communication includes synchronous and asynchronous send/receive functions. Collective communication functions like broadcast, reduce, scatter, and gather efficiently distribute data among processes. MPI also supports irregular data packaging using packing/unpacking functions and derived datatypes.
This chapter discusses principles of scalable performance for parallel systems. It covers performance measures like speedup factors and parallelism profiles. The key principles discussed include degree of parallelism, average parallelism, asymptotic speedup, efficiency, utilization, and quality of parallelism. Performance models like Amdahl's law and isoefficiency concepts are presented. Standard performance benchmarks and characteristics of parallel applications and algorithms are also summarized.
Memory system, and not processor speed, is often the bottleneck for many applications.
Memory system performance is largely captured by two parameters, latency and bandwidth.
Latency is the time from the issue of a memory request to the time the data is available at the processor.
Bandwidth is the rate at which data can be pumped to the processor by the memory system.
This document discusses software for parallel programming, including parallel programming models, languages, compilers, and environments. It covers shared memory and message passing models, as well as data parallel programming. It also discusses language features that support parallelism, parallel language constructs, optimizing compilers for parallelism, and integrated parallel programming environments. Key topics are parallel debugging, performance monitoring, and program visualization tools. Representative parallel programming environments including Cray, Intel Paragon, and CM-5 software are also summarized.
Heterogeneous computing refers to systems that use more than one type of processor or core. It allows integration of CPUs and GPUs on the same bus, with shared memory and tasks. This is called the Heterogeneous System Architecture (HSA). The HSA aims to reduce latency between devices and make them more compatible for programming. Programming models for HSA include OpenCL, CUDA, and hUMA. Heterogeneous computing is used in platforms like smartphones, laptops, game consoles, and APUs from AMD. It provides benefits like increased performance, lower costs, and better battery life over traditional CPUs, but discrete CPUs and GPUs can provide more power and new software models are needed.
Parallel platforms can be organized in various ways, from an ideal parallel random access machine (PRAM) to more conventional architectures. PRAMs allow concurrent access to shared memory and can be divided into subclasses based on how simultaneous memory accesses are handled. Physical parallel computers use interconnection networks to provide communication between processing elements and memory. These networks include bus-based, crossbar, multistage, and various topologies like meshes and hypercubes. Maintaining cache coherence across multiple processors is important and can be achieved using invalidate protocols, directories, and snooping.
A natural extension of the Random Access Machine (RAM) serial architecture is the Parallel Random Access Machine, or PRAM.
PRAMs consist of p processors and a global memory of unbounded size that is uniformly accessible to all processors.
Processors share a common clock but may execute different instructions in each cycle.
The primary reasons for using parallel computing:
Save time - wall clock time
Solve larger problems
Provide concurrency (do multiple things at the same time)
MPI provides point-to-point and collective communication capabilities. Point-to-point communication includes synchronous and asynchronous send/receive functions. Collective communication functions like broadcast, reduce, scatter, and gather efficiently distribute data among processes. MPI also supports irregular data packaging using packing/unpacking functions and derived datatypes.
This chapter discusses principles of scalable performance for parallel systems. It covers performance measures like speedup factors and parallelism profiles. The key principles discussed include degree of parallelism, average parallelism, asymptotic speedup, efficiency, utilization, and quality of parallelism. Performance models like Amdahl's law and isoefficiency concepts are presented. Standard performance benchmarks and characteristics of parallel applications and algorithms are also summarized.
Memory system, and not processor speed, is often the bottleneck for many applications.
Memory system performance is largely captured by two parameters, latency and bandwidth.
Latency is the time from the issue of a memory request to the time the data is available at the processor.
Bandwidth is the rate at which data can be pumped to the processor by the memory system.
This document provides an overview of performance analysis of parallel programs. It defines key terms like speedup, efficiency, and cost. It describes Amdahl's law, which establishes that the maximum speedup from parallelization is limited by the fraction of the program that must execute sequentially. The document also discusses concepts like superlinear speedup, optimal parallel algorithms, and barriers to higher parallel performance like communication overhead. Overall, the document introduces important metrics and models for predicting and understanding the performance of parallel programs.
This document discusses five parallel programming models: shared-variable, message-passing, data-parallel, object-oriented, and functional/logic models. The shared-variable model uses shared memory for interprocess communication, while the message-passing model uses message passing between separate memory spaces. The data-parallel model explicitly handles parallelism through hardware and focuses on pre-distributed data sets. The object-oriented model discusses concurrency in object-oriented programming using patterns like pipelines and divide-and-conquer. Finally, the functional/logic models emphasize functional programming without state changes and logic programming for databases.
program partitioning and scheduling IN Advanced Computer ArchitecturePankaj Kumar Jain
Advanced Computer Architecture,Program Partitioning and Scheduling,Program Partitioning & Scheduling,Latency,Levels of Parallelism,Loop-level Parallelism,Subprogram-level Parallelism,Job or Program-Level Parallelism,Communication Latency,Grain Packing and Scheduling,Program Graphs and Packing
This document summarizes an introduction to MPI lecture. It outlines the lecture topics which include models of communication for parallel programming, MPI libraries, features of MPI, programming with MPI, using the MPI manual, compilation and running MPI programs, and basic MPI concepts. It provides examples of "Hello World" programs in C, Fortran, and C++. It also discusses what was learned in the lecture which includes processes, communicators, ranks, and the default communicator MPI_COMM_WORLD. The document concludes with noting the general MPI program structure involves initialization, communication/computation, and finalization steps. For homework, it asks to modify the previous "Hello World" program to also print the processor name executing each process using MPI_
The document discusses parallel algorithms and parallel computing. It begins by defining parallelism in computers as performing more than one task at the same time. Examples of parallelism include I/O chips and pipelining of instructions. Common terms for parallelism are defined, including concurrent processing, distributed processing, and parallel processing. Issues in parallel programming such as task decomposition and synchronization are outlined. Performance issues like scalability and load balancing are also discussed. Different types of parallel machines and their classification are described.
This document discusses parallel programming concepts including threads, synchronization, and barriers. It defines parallel programming as carrying out many calculations simultaneously. Advantages include increased computational power and speed up. Key issues in parallel programming are sharing resources between threads, and ensuring synchronization through locks and barriers. Data parallel programming is discussed where the same operation is performed on different data elements simultaneously.
An explicitly parallel program must specify concurrency and interaction between concurrent subtasks.
The former is sometimes also referred to as the control structure and the latter as the communication model.
Parallel computing is computing architecture paradigm ., in which processing required to solve a problem is done in more than one processor parallel way.
Chapter 3 principles of parallel algorithm designDenisAkbar1
The document provides an overview of parallel algorithms and concurrency. It discusses various techniques for decomposing problems into tasks, including recursive decomposition, data decomposition, and intermediate data partitioning. It also covers characteristics of task interactions and dependencies, as well as methods for mapping tasks to processes to optimize load balancing and communication overhead. Examples are provided to illustrate matrix multiplication, database queries, and other problems.
Presentation on Static Network Architecture for multi-programming and multi-processing. Architecture, Ring Architecture, Ring Chordal Architecture, Barrel Shifter Architecture, Fully Connected Architecture.
1. The document discusses symmetric cipher models and elementary number theory. It provides a set of multiple choice questions and answers about topics like brute force attacks, conventional vs asymmetric cipher systems, Caesar cipher, Vigenere cipher, index of coincidence, simplified data encryption standard (SDES) and more.
2. The questions cover topics like encryption algorithms, key sizes, encryption/decryption processes, analyzing ciphertexts produced by different ciphers, and calculating values like round keys and indexes of coincidence.
3. Correct answers are provided along with explanations to help understand the concepts behind symmetric encryption techniques and number theory principles.
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.
Elementary Parallel Algorithm - Sum of n numbers on Hypercube, Shuffle Exchange and Mesh SIMD computers, UMA multiprocessors, Broadcasting and pre-fix sum on multicomputer.
Parallel computing is a type of computation in which many calculations or the execution of processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has been employed for many years, mainly in high-performance computing, but interest in it has grown lately due to the physical constraints preventing frequency scaling. As power consumption (and consequently heat generation) by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.
This document discusses scheduling in distributed systems. It covers:
1) Common scheduling techniques like min-min, max-min, and sufferage for scheduling independent tasks on dedicated systems.
2) Scheduling dependent tasks modeled as directed acyclic graphs (DAGs) using techniques like critical path on a processor (CPOP) and heterogeneous earliest finish time (HEFT).
3) The need for scheduling algorithms to adapt to dynamic grid environments where tasks may have dependencies on shared files and network transfer times vary.
The theory behind parallel computing is covered here. For more theoretical knowledge: https://sites.google.com/view/vajira-thambawita/leaning-materials
IV B.Tech I Sem CSE&IT JNTUK R10 regulation students have Mobile computing paper. This slides especially contains UNIT - 5 total material required for end exams
Research Scope in Parallel Computing And Parallel ProgrammingShitalkumar Sukhdeve
Parallel computing involves performing multiple calculations simultaneously using large problems that can be divided into smaller sub-problems. There are different forms of parallelism at the bit-level, instruction-level, data-level, and task-level. Parallel programs can be challenging to write due to issues with communication and synchronization between subtasks. Software solutions for parallel programming include shared memory languages, distributed memory languages using message passing, and automatic parallelization tools.
Parallel computing is the simultaneous use of multiple compute resources to solve a computational problem faster. It allows for larger problems to be solved and provides cost savings over serial computing. There are different models of parallelism including data parallelism and task parallelism. Flynn's taxonomy categorizes computer architectures as SISD, SIMD, MISD and MIMD based on how instructions and data are handled. Shared memory and distributed memory are two common architectures that differ in scalability and communication handling. Programming models include shared memory, message passing and data parallel approaches. Design considerations for parallel programs include partitioning work, communication between processes, and synchronization.
GoToMeeting is web-hosted remote meeting and desktop sharing software that allows users to meet via the Internet in real-time. It has many capabilities like HD video conferencing, screen sharing, application sharing, chat, audio options, and drawing tools. The document discusses pros and cons of various GoToMeeting features based on a test drive, finding it easy to use but with some limitations around sharing controls and distractions. In conclusion, GoToMeeting provides benefits for computer-mediated learning environments by facilitating remote collaboration and presentations.
The five graphs of telecommunications may 22 2013 webinar finalNeo4j
The document discusses how telecommunications companies can leverage graph databases to derive value from five key "graphs": the network graph, customer graph, call graph, master data graph, and help desk graph. It provides examples of how companies are using graph databases to improve network management, customer analytics, and other tasks. Reasons for adopting graph databases include faster querying of connected data, better matching of the data model to business domains, and improved maintainability. The presentation encourages attendees to connect at upcoming GraphConnect conferences to learn more.
This document provides an overview of performance analysis of parallel programs. It defines key terms like speedup, efficiency, and cost. It describes Amdahl's law, which establishes that the maximum speedup from parallelization is limited by the fraction of the program that must execute sequentially. The document also discusses concepts like superlinear speedup, optimal parallel algorithms, and barriers to higher parallel performance like communication overhead. Overall, the document introduces important metrics and models for predicting and understanding the performance of parallel programs.
This document discusses five parallel programming models: shared-variable, message-passing, data-parallel, object-oriented, and functional/logic models. The shared-variable model uses shared memory for interprocess communication, while the message-passing model uses message passing between separate memory spaces. The data-parallel model explicitly handles parallelism through hardware and focuses on pre-distributed data sets. The object-oriented model discusses concurrency in object-oriented programming using patterns like pipelines and divide-and-conquer. Finally, the functional/logic models emphasize functional programming without state changes and logic programming for databases.
program partitioning and scheduling IN Advanced Computer ArchitecturePankaj Kumar Jain
Advanced Computer Architecture,Program Partitioning and Scheduling,Program Partitioning & Scheduling,Latency,Levels of Parallelism,Loop-level Parallelism,Subprogram-level Parallelism,Job or Program-Level Parallelism,Communication Latency,Grain Packing and Scheduling,Program Graphs and Packing
This document summarizes an introduction to MPI lecture. It outlines the lecture topics which include models of communication for parallel programming, MPI libraries, features of MPI, programming with MPI, using the MPI manual, compilation and running MPI programs, and basic MPI concepts. It provides examples of "Hello World" programs in C, Fortran, and C++. It also discusses what was learned in the lecture which includes processes, communicators, ranks, and the default communicator MPI_COMM_WORLD. The document concludes with noting the general MPI program structure involves initialization, communication/computation, and finalization steps. For homework, it asks to modify the previous "Hello World" program to also print the processor name executing each process using MPI_
The document discusses parallel algorithms and parallel computing. It begins by defining parallelism in computers as performing more than one task at the same time. Examples of parallelism include I/O chips and pipelining of instructions. Common terms for parallelism are defined, including concurrent processing, distributed processing, and parallel processing. Issues in parallel programming such as task decomposition and synchronization are outlined. Performance issues like scalability and load balancing are also discussed. Different types of parallel machines and their classification are described.
This document discusses parallel programming concepts including threads, synchronization, and barriers. It defines parallel programming as carrying out many calculations simultaneously. Advantages include increased computational power and speed up. Key issues in parallel programming are sharing resources between threads, and ensuring synchronization through locks and barriers. Data parallel programming is discussed where the same operation is performed on different data elements simultaneously.
An explicitly parallel program must specify concurrency and interaction between concurrent subtasks.
The former is sometimes also referred to as the control structure and the latter as the communication model.
Parallel computing is computing architecture paradigm ., in which processing required to solve a problem is done in more than one processor parallel way.
Chapter 3 principles of parallel algorithm designDenisAkbar1
The document provides an overview of parallel algorithms and concurrency. It discusses various techniques for decomposing problems into tasks, including recursive decomposition, data decomposition, and intermediate data partitioning. It also covers characteristics of task interactions and dependencies, as well as methods for mapping tasks to processes to optimize load balancing and communication overhead. Examples are provided to illustrate matrix multiplication, database queries, and other problems.
Presentation on Static Network Architecture for multi-programming and multi-processing. Architecture, Ring Architecture, Ring Chordal Architecture, Barrel Shifter Architecture, Fully Connected Architecture.
1. The document discusses symmetric cipher models and elementary number theory. It provides a set of multiple choice questions and answers about topics like brute force attacks, conventional vs asymmetric cipher systems, Caesar cipher, Vigenere cipher, index of coincidence, simplified data encryption standard (SDES) and more.
2. The questions cover topics like encryption algorithms, key sizes, encryption/decryption processes, analyzing ciphertexts produced by different ciphers, and calculating values like round keys and indexes of coincidence.
3. Correct answers are provided along with explanations to help understand the concepts behind symmetric encryption techniques and number theory principles.
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.
Elementary Parallel Algorithm - Sum of n numbers on Hypercube, Shuffle Exchange and Mesh SIMD computers, UMA multiprocessors, Broadcasting and pre-fix sum on multicomputer.
Parallel computing is a type of computation in which many calculations or the execution of processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has been employed for many years, mainly in high-performance computing, but interest in it has grown lately due to the physical constraints preventing frequency scaling. As power consumption (and consequently heat generation) by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.
This document discusses scheduling in distributed systems. It covers:
1) Common scheduling techniques like min-min, max-min, and sufferage for scheduling independent tasks on dedicated systems.
2) Scheduling dependent tasks modeled as directed acyclic graphs (DAGs) using techniques like critical path on a processor (CPOP) and heterogeneous earliest finish time (HEFT).
3) The need for scheduling algorithms to adapt to dynamic grid environments where tasks may have dependencies on shared files and network transfer times vary.
The theory behind parallel computing is covered here. For more theoretical knowledge: https://sites.google.com/view/vajira-thambawita/leaning-materials
IV B.Tech I Sem CSE&IT JNTUK R10 regulation students have Mobile computing paper. This slides especially contains UNIT - 5 total material required for end exams
Research Scope in Parallel Computing And Parallel ProgrammingShitalkumar Sukhdeve
Parallel computing involves performing multiple calculations simultaneously using large problems that can be divided into smaller sub-problems. There are different forms of parallelism at the bit-level, instruction-level, data-level, and task-level. Parallel programs can be challenging to write due to issues with communication and synchronization between subtasks. Software solutions for parallel programming include shared memory languages, distributed memory languages using message passing, and automatic parallelization tools.
Parallel computing is the simultaneous use of multiple compute resources to solve a computational problem faster. It allows for larger problems to be solved and provides cost savings over serial computing. There are different models of parallelism including data parallelism and task parallelism. Flynn's taxonomy categorizes computer architectures as SISD, SIMD, MISD and MIMD based on how instructions and data are handled. Shared memory and distributed memory are two common architectures that differ in scalability and communication handling. Programming models include shared memory, message passing and data parallel approaches. Design considerations for parallel programs include partitioning work, communication between processes, and synchronization.
GoToMeeting is web-hosted remote meeting and desktop sharing software that allows users to meet via the Internet in real-time. It has many capabilities like HD video conferencing, screen sharing, application sharing, chat, audio options, and drawing tools. The document discusses pros and cons of various GoToMeeting features based on a test drive, finding it easy to use but with some limitations around sharing controls and distractions. In conclusion, GoToMeeting provides benefits for computer-mediated learning environments by facilitating remote collaboration and presentations.
The five graphs of telecommunications may 22 2013 webinar finalNeo4j
The document discusses how telecommunications companies can leverage graph databases to derive value from five key "graphs": the network graph, customer graph, call graph, master data graph, and help desk graph. It provides examples of how companies are using graph databases to improve network management, customer analytics, and other tasks. Reasons for adopting graph databases include faster querying of connected data, better matching of the data model to business domains, and improved maintainability. The presentation encourages attendees to connect at upcoming GraphConnect conferences to learn more.
The Indian Mobile Initiative aims to advocate for social innovations and entrepreneurship through mobile technology by educating Indian university students. It consists of a five-week course on entrepreneurship and mobile applications, as well as multiple three-day mobile and entrepreneurship workshops. The workshops will be held at different colleges across India and will teach students Android basics, have them brainstorm ideas to address community problems, and present and discuss their solutions. The overall goals are to inspire students to be social entrepreneurs that address community needs and build a network connecting students, mentors, and investors.
A research report on the establishment of private independent blood banks in ...Rijo Stephen Cletus
This document is a research report on establishing private independent blood banks in India for civilian use. It provides an abstract that outlines issues with India's decentralized blood banking system and the need for qualified professionals and adequate infrastructure. The report then covers the legal framework and guidelines for licensing blood banks and manufacturing blood products in India. It examines requirements for operating blood banks, collecting and processing blood components, and manufacturing blood products to ensure quality, safety and compliance with standards. The document also provides clinical information on blood grouping, cross-matching, transfusion medicine concepts and risks of transfusion-transmitted infections to help blood banks serve patients effectively.
This document provides an overview of personnel cost planning in SAP. It describes key concepts like cost items, cost objects, and wage elements. It also summarizes integration requirements with other SAP modules like organizational management, controlling, and personnel administration. The document gives an overview of the cost planning process and available features for editing scenarios, comparing plans, and integrating with controlling.
Violin Memory DOAG (German Oracle User Group) Nov 2012Jack O'Brien
Flash storage solves the problem of application I/O wait time; Pepperl+Fuchs consolidates applications and databases on a single flash storage platform
This technical paper provides best practices for performing graceful switchover and switchback between a production Oracle database and its corresponding standby database. It describes the prerequisites, steps, and validations needed to safely switch the roles of the databases without invalidating them. The goal is to avoid reopening a database using the resetlogs operation when possible, reduce the vulnerability of the primary site due to loss of the disaster recovery site, and simplify administration without jeopardizing data integrity. Proper testing and understanding of the process is required, as user errors can damage the databases.
This document discusses various performance testing methodologies. It begins by introducing performance testing as a subset of performance engineering aimed at building performance into a system's design. The document then describes different types of performance testing including load testing, stress testing, endurance/soak testing, spike testing, and configuration testing. It emphasizes that performance testing validates attributes like scalability and resource usage under various loads.
This certificate is presented to Mr./Ms./Mrs. for successfully completing the Hadoop 2.x Foundation Course with a grade of A. The course was completed on 03/07/2016. The certificate is signed by Santosh Das, Founder & CEO of BlueCamphor Technologies(p)Ltd, and is intended for the serious learner.
Mobile App development has taken the industries by storm as everyone wants to get the best solutions in the fastest and most affordable way. As suggested by Adobe, the segment of business leaders who finds enterprise mobility solutions beneficial for the business stands strong at 77%. Also, with this strong inclination towards tapping the mobile power around 66% of the business owners are looking to increase their investments for mobile app development. It is the time that we look into the specific goals of business owners as well as the needs of the main consumers to anticipate the most valuable trends in mobile space in 2017.
Pragadeeswaran Rajendran is a human resources professional with over 6 years of experience in HR roles. He is currently a Senior Associate in HR at IMedX Information Services Private Limited, where he handles all HR functions including recruitment, payroll, employee engagement and statutory compliance. Previously he has worked as an HR executive at M*Modal Global Services and Classic Knits India, where he was responsible for recruitment, payroll processing, and maintaining statutory records and compliance. He holds an MBA in HR and a bachelor's degree in mathematics.
There are typically many students per classroom section which can lead to overcrowding issues. Overcrowded classrooms along with lack of motivation, insufficient teaching methods, irregular attendance, and troubled home lives can cause students to become reluctant learners. Reluctant learners struggle due to deficiencies in background knowledge, drilling/practice, and potential learning disabilities.
This document summarizes and compares different types of cooperatives, including mutual societies, building societies, credit unions, consumer cooperatives, and producer cooperatives. It discusses their purposes of serving member needs by overcoming exploitation and improving quality of life. Case studies are presented on the Trustee Savings Bank, credit unions, building societies, and the Co-operative Retail Services consumer cooperative to analyze causes of failures and reasons for success in cooperative management and control.
The document discusses mobile phone history and popular apps in different categories. It mentions the Alcatel One Touch Easy from 1999 and the Ipod from 2007. It then lists the top 3 useful apps as a barcode scanner, running app Endomondo, and camera app Camera 360. The top 3 English learning apps are listed as tongue twisters, Busuu, and SpeakingPal. A tongue twister is also provided. Finally, the top 3 time waster apps are listed as iLightr, Steamy Window, and SoundHound.
The document describes a proposed General Ledger Information System that would provide several benefits:
It would create a centralized system to define tasks, collect information from multiple locations, and consolidate it into a single repository. This would streamline reporting and eliminate duplication. It would introduce validations and automated workflows to improve accuracy while reducing errors. Managers could track progress and exceptions in real-time through customizable reports and dashboards. The system would be developed in phases, starting with key tasks like capturing financial information, then expanding to other areas like taxes and risk reporting.
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.
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 provides an overview of message passing programming and the Message Passing Interface (MPI). It discusses the principles of message passing including processes with exclusive address spaces that communicate via messages. It describes the basic send and receive operations in MPI and how they can be blocking or non-blocking. It also covers topics like collectives, topologies, and overlapping communication with computation.
- McMPI is a managed-code (C#) MPI library that was implemented to remove overhead of inter-language function calls compared to using existing C-based MPI libraries in other languages like C++.
- It uses object-oriented design patterns like abstract factory and bridge to isolate concerns and enable extensibility.
- Performance results show its shared-memory latency and bandwidth is comparable to other MPI implementations, while its distributed-memory latency is higher but bandwidth is comparable.
Adding Real-time Features to PHP ApplicationsRonny López
It's possible to introduce real-time features to PHP applications without deep modifications of the current codebase.
Using WAMP you can build distributed systems out of application components which are loosely coupled and communicate in (soft) real-time.
There is no need to learn a whole new language, with the implications it has.
It also opens the door to write reactive, event-based, distributed architectures and to achieve easier scalability by distributing messages to multiple systems.
Foundational Design Patterns for Multi-Purpose ApplicationsChing-Hwa Yu
This document discusses foundational design patterns for multi-purpose applications. It covers topics like coupling, cohesion, and moving beyond using a single while loop. Functional global variables and queued message handlers are introduced as design patterns to enable communication between independent processes. The document provides examples of how these patterns can be used to decompose an application into independent processes to improve scalability, testability, and maintainability.
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.
Message Passing Interface (MPI)-A means of machine communicationHimanshi Kathuria
MPI (Message Passing Interface) is a standard for writing message passing programs between parallel processes. It was developed in the late 1980s and early 1990s due to increasing computational needs. An MPI program typically initializes and finalizes the MPI environment, declares variables, includes MPI header files, contains parallel code using MPI calls, and terminates the environment before ending. Key MPI calls initialize and finalize the environment, determine the process rank and number of processes, and get the processor name.
This document discusses Process Management Interface for Exascale (PMIx). It provides an overview and objectives of PMIx, which aims to establish an independent and open community effort to develop scalable client/server libraries for job launch and management. The document discusses performance status showing improvements over PMI2, integration status in Open MPI and SLURM, and roadmap for continued development including supporting evolving application needs through flexible resource allocation and fault tolerance. It also discusses different types of malleable and adaptive jobs that PMIx aims to support.
Inter-Process Communication in distributed systemsAya Mahmoud
Inter-Process Communication is at the heart of all distributed systems, so we need to know the ways that processes can exchange information.
Communication in distributed systems is based on Low-level message passing as offered by the underlying network.
This document discusses technologies used for distributed systems and microservices including Golang, Protocol Buffers (Protobuf), gRPC, HTTP/2, Docker, and Kubernetes. It provides overviews of each technology, their uses, benefits, and how they enable building distributed systems through containerization and orchestration of microservices. When building distributed systems, these technologies help address challenges through a microservices architecture, horizontal scaling, language independence, and focusing on code deployment over servers.
Compilers can have a huge effect on software efficiency and performance by changing what user experiences are possible and reducing CPU and resource usage. They work by parsing code, generating machine-friendly representations, and emitting optimized machine code. As web programming grew in complexity, developers started building more efficient compilers for dynamic languages to preserve rapid development workflows while improving performance. There are various approaches to building compilers like interpreters, transpilers, using backends like LLVM, and fully custom solutions. The best approach depends on goals, constraints, and tradeoffs around control, performance, and development effort. Optimization focuses should include memory usage, caching, and runtime layout. Future areas may include database query compilation for real-time analytics on large datasets.
The Overview of Microservices ArchitectureParia Heidari
This document discusses monolithic architecture and microservices architecture. It begins by defining monolithic architecture as having a single code base with multiple components/modules. It then lists advantages like being simple to develop, test, deploy and scale, as well as drawbacks like flexibility, maintenance, reliability, and scaling challenges.
Microservices architecture is presented as a solution to problems with monolithic architecture. Each microservice has a specific focus and functionality. Benefits include improved testability, loose coupling, and ability to develop, deploy and scale services independently. Challenges include increased complexity of developing, testing and operating distributed systems.
The document provides examples and discusses strategies for migrating a monolithic system to microservices, technologies
MQSeries is a middleware product that implements a messaging and queuing framework to allow programs to communicate asynchronously by sending messages to queues. It provides assured delivery of messages across platforms and languages. The core components of MQSeries include queue managers, queues, message channels, and a messaging programming interface. MQSeries uses message logging and recovery to ensure reliable and persistent message delivery.
This document provides a high-level overview of key concepts in distributed systems, including programming models, remote procedure calls, messaging, distributed data, peer-to-peer networks, distributed hash tables, cloud computing, and security. It discusses implicit and explicit programming models, synchronous and asynchronous communication, and distributed computing frameworks like MapReduce.
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Similar to Presentation - Programming a Heterogeneous Computing Cluster (20)
2. We’ll discuss the following today
• Background of Heterogeneous Computing
• Message Passing Interface(MPI)
• Vector Addition Example(MPI Implementation)
• More implementation details of MPI
3. Background
• Heterogeneous Computing System(HCS)
• High Performance Computing & its uses
• Supercomputer vs. HCS
• Why use Heterogeneous Computers in HCS?
• MPI is the predominant message passing system forClusters
4. Introduction to MPI
• MPI stands for Message Passing Interface
• Predominant API
• Runs on virtually any hardware platform
• Programming Model – Distributed Memory Model
• Supports Explicit Parallelism
• Multiple Languages supported
5. Reasons for using MPI
• Standardization
• Portability
• Performance Opportunities
• Functionality
• Availability
6. MPI Model
• Flat view of the cluster to
programmer
• SPMD Programming Model
• No Global Memory
• Inter-processCommunication is
possible & required
• Process Synchronization
Primitives
9. Format of MPI
Calls
• Format of MPI Calls
• Case Sensitivity
• C –Yes
• Fortran – No
• Name Restrictions
• MPI_ *
• PMPI_* ( Profiling interface)
• Error Handling
• Handled via return parameter
10. Groups &
Communicators
Groups – Ordered set of processes
Communicators – Handle to a
group of processes
Most MPI Routines require a
communicator as argument
MPI_COMM_WORLD – Predefined
Communicator that includes all
processes
Rank – Unique ID
25. Point-to-Point Operations
• Typically involve two, and only two, different MPI threads
• Different types of send and receive routines
• Synchronous send
• Blocking send / blocking receive
• Non-blocking send / non-blocking receive
• Buffered send
• Combined send/receive
• "Ready" send
• Send/Receive Routines not tightly coupled
26. Buffering
• Why is buffering required?
• It is Implementation Dependent
• Opaque to the programmer and
managed by the MPI library
• Advantages
• Can exist on the sending side, the
receiving side, or both
• Improves program performance
• Disadvantages
• A finite resource that can be easy to
exhaust
• Often mysterious and not well
documented
27. Blocking vs. Non-blocking
Blocking Non Blocking
Send will only return after it’s safe to modify
application buffer
Send/Receive return almost immediately
Receive returns after the data has arrived and
ready for use by the application
Unsafe to modify our variables till we know
send operation has been completed
Synchronous Communication is possible OnlyAsynchronous Communication possible
Asynchronous Communication is also possible Primarily used to overlap computation with
communication to get performance gain
28. Order and Fairness
• Order
• MPI guarantees that messages will not overtake each other
• Order rules do not apply if there are multiple threads participating in the
communication operations
• Fairness
• MPI does not guarantee fairness - it's up to the programmer to prevent
"operation starvation"
30. Collective Communication Routines(contd.)
• Scope
• Must involve all processes within the scope of a communicator
• Unexpected behavior, including program failure, can occur if even one task in the
communicator doesn't participate
• Programmer's responsibility to ensure that all processes within a communicator
participate in any collective operations.
• Collective communication functions are highly optimized
31. Groups & Communicators(additional details)
• Group
• Represented within system memory as an object
• Only accessible as a handle
• Always associated with a communicator object
• Communicator
• Represented within system memory as an object.
• In the simplest sense, the communicator is an extra "tag" that must be included with
MPI calls
• Inter-group and Intra-group communicators available
• From the programmer's perspective, a group and a communicator are one
32. Primary Purposes of Group and
Communicator Objects
1. Allows you to organize tasks, based upon function, into task
groups.
2. Enable Collective Communications operations across a subset of
related tasks.
3. Provide basis for implementing user defined virtual topologies
4. Provide for safe communications
33. Programming Considerations and
Restrictions
• Groups/communicators are dynamic
• Processes may be in more than one group/communicator
• MPI provides over 40 routines related to groups, communicators, and virtual topologies.
• Typical usage:
• Extract handle of global group from MPI_COMM_WORLD using MPI_Comm_group
• Form new group as a subset of global group using MPI_Group_incl
• Create new communicator for new group using MPI_Comm_create
• Determine new rank in new communicator using MPI_Comm_rank
• Conduct communications using any MPI message passing routine
• When finished, free up new communicator and group (optional) using MPI_Comm_free and
MPI_Group_free
34. Virtual Topologies
• Mapping/ordering of MPI processes into a geometric "shape“
• Similar to CUDA Grid / Block 2D/3D structure
• They are onlyVirtual
• Two MainTypes
• Cartesian(grid)
• Graph
• Virtual topologies are built upon MPI communicators and groups.
• Must be "programmed" by the application developer.
35. Why use Virtual Topologies?
• Convenience
• Useful for applications with specific communication patterns
• Communication Efficiency
• Penalty avoided on some hardware architectures for communication between
distant nodes
• Process Mapping may be optimized based on physical characteristics of the
machine
• MPI Implementation decides ifVT is ignored or not
HCS
Systems that use more than one kind of processor
So far we have discussed programming on a system with one host-one device
HPC & uses
Using more than one computer as a part of a cluster to get things done faster.
A computer cluster is just a bunch of computers connected to a local network or LAN
Uses
Stock Prediction & Trading
Rendering an very very high resolution picture(400,000,000 pixels)
Evolutionary Algorithms
SC vs. HCS
SC
Good only for specialized problems
Requires vast sums of money and specialized expertise to use
HCS
Can be managed without a lot of expense or expertise
Why use Heterogeneous Computers in HCS?
For better energy efficiency
Usage of GPU’s in clusters started in 2009, so it’s relatively new
Effectiveness of this approach – Lot of clusters in the Green 500 List
Green 500 – List of most energy-efficient/ greenest supercomputers in the world
Message Passing Interface
Originally designed for distributed memory architectures(1980’s to early 90’s)
Predominant API
Wiped out other API’s from before it
Runs on virtually any hardware platform
Distributed Memory
Shared Memory
Hybrid
Programming Model
Regardless of the underlying physical architecture of the machine
Explicit Parallelism
Programmer is responsible for identification and implementation of parallelism using algorithms and MPI Constructs
Languages
C, C++ and Fortran
Standardization
It’s supported on all HPC platforms like
MVAPICH – Linux Cluster
Open MPI – Linux Cluster
IBM MPI – BG/Q Cluster – part of their blue jean series
Portability
No source code modifications when porting between platforms - platform supports MPI Std.
Performance Opportunities
Vendors can tune it further based on native h/w
Functionality
Over 430 routines in MPI 3
Most use less than a dozen routines
Availability
Variety of implementations in vendor n public domain
SPMD Programming Model
Each process computes part of the output
Flat view of the cluster
Instead of having a node concept, MPI just has threads.
All threads given a flat index like global index in OpenCL
Programming is similar to CUDA & OpenCL
No Global Memory
No such thing
No shared memory between nodes
Inter-process Communication is possible
Since no GM, any data transfer has to be done via IPC using MPI constructs
Process Synchronization Primitives
We use MPI Collectives to provide Synchronization
Header File
Header file is mandatory
USE mpi_f08 module is preferred over the include
The highlighted portions are where we will use the MPI Constructs
0
Only one thread will execute
1
Process may be multi-threaded
However only main thread will make MPI Calls(funneled through main)
2
Process may be multi-threaded
Multiple threads may make MPI calls but only 1 at a time.
Concurrent calls are serialized.
3
Multiple threads may call MPI with no restrictions
Format of MPI Calls
Case Sensitivity
C – yes
Fortran - No
Name Restrictions
Prefixes starting with MPI_ * & PMPI_* ( profiling Interface)
Error Handling
Default behavior of an MPI Call is to abort if there is an error
Good news – Probably never see anything other than success
Bad new – Pain to debug
Default Handler can be over ridden
Errors displayed to user is implementation dependent
After 2nd point
MPI uses objects called communicators and groups to define which collection of processes may communicate with each other.
Rank
Unique identifier assigned by system to a process when process initializes
Sometimes called a task ID
They are contiguous and begin at 0
MPI_Init (&argc,&argv)
Initialized the MPI Execution Environment
Must be called in every MPI Program
Should be called only once and before any other MPI function
May be used to pass the command like arguments to all processes
Not required by std. & implementation dependent
MPI_Comm_size (comm,&size)
Returns total no. of MPI Processes in specified communicator
Size has the value
Required as the no. of allocated processes might not be the same as the no. of the requested processes
MPI_Comm_rank (comm,&rank)
Task id of the process
Will be an integer between 0 & n-1 withing MPI_COMM_WORLD communicator
If process is associated with another communicator, it will have unique rank within each of these communicators also
MPI_Abort (comm,errorcode)
Terminates all MPI Processes associated with a communicator
Communicator is ignored in most implementations and all processes are terminated
MPI_Get_processor_name (&name,&resultlength)
Returns processor name and it’s name length
May not be same as host name, it is implementation dependent
MPI_Get_version (&version,&subversion)
Returns version n subversion of MPI Std. implemented by the library
MPI_Initialized (&flag)
Indicates whether MPI_Init has been called.
MPI_Wtime ()
Returns elapsed wall clock time in seconds(double precision)
MPI_Wtick ()
Returns the resolution of MPI_Wtime in seconds
For example, if the clock is implemented by the hardware as a counter that is incremented every millisecond, the value returned by MPI_WTICK should be 10^-3
MPI_Finalize ()
Terminates MPI Execution environment
Should be the last MPI Routine called
No other MPI Routines may be called after it
Explain every line
Main will be executed by all the processes
np = no. of processes == griddim.x * blockdim.x
Pid == blockidx.x * blockdim.x + threadix.x
We request n no. of processes when we begin the program execution, we use MPI_Comm_rank to verify if we have got the requested no. of processes or not.
If the system does not have enough resources then it means that we don’t get enough processes for our program. We check to see if we have enough and abort if we don’t have atlease 3
We are printing the error message only from one process
We are aborting all the processes liked with the communicator
If the no. of processes are sufficient then we get into the real execution of the program
Control flow is used to specialize one of the processes
np-1 process acts as the server == host
n-2 act like the compute nodes == device
If you are compute node, u only receive a section of the input for computation
Once all the processes are complete, we clean up DS and release all resources to call MPI_Finalize.
This use used by one process to send data to another process
Very easy to use…As a beginner, you don’t need to know too much about the implementation to actually use it.
*buf – Starting address of the sending buffer i.e the location from which data has to be copied
Count
No. of elements in the buffer
Note : Elements not bytes
If we have a buffer of type double then it’s size is going to be more than the size of a buffer of type int even though the count is same
Datatype
Datatype of the elements in the buffer
Dest – process id of the target process
Tag
Message tag(integer)
Has to be a non negative
Comm
Communicator
Similar to the send data interface
Status
Output parameter
Status of the received message
This is 2 step process where the send has to be called by one process and receive has to called by the next
In CUDA, it’s one step with 2 directions
Host to Device
Device to Host
This is the server code
Only the (np-1)th process will be executing this.
Server is going to do the I/O and distribute the data to computer nodes
Eventually it will collect the output from all the compute nodes and do I/O again
Q = why is MPI_Comm_Size called here again?
A - Little cleaner code as no. of parameters are reduced
We are going to allocate memory for the entire input and output.
Program will abort if there isn’t enough memory available.
In a real program, we would be reading from the Input / disk to populate the data
Here we just fill the input vectors with random data
We initialize the pointers to these input vectors
We then go into the for loop, where each iteration will send a chuck from vector A and a section from vector B to the compute process
We start from 0 up to the no. of nodes == np-2 (bcoz last one is used for server process)
Once we send a section to a compute process, we increment the pointer in the input vectors by the section’s size so that we can send the next section to the subsequent thread.
For extremely large input sizes, we may have to further parallelize this server process
Perhaps by having more than 1 server process
Once data is distributed to all the compute processes,
the server process is going to wait till all the compute process are done with their processing
Once every one finish their work, everyone will be release from their barrier
Now the server process will collect the data from all the processes using MPI_Recv
Blocks caller until all group members have called it
It returns only after all group members have entered the call
As the name suggests this is called barrier synchronization which is similar to
syncthreads() in CUDA.
Once they finish copying the data from the compute processes, I/O is performed by the Server Process.
After the I/0 and before the program begins, the memory allocated on the heap is released.
Here we show the code for the compute process
Total np-1 no. of processes executing the compute code
By program design, we identify (np-1)th process as server hence we call MPI_Comm_size
Now we allocate memory for a section of data(not the whole)
Immediately go into MPI_Recv to receive the data from server
We then compute the output
Similar to how we do it CUDA, we should expect barrier synchronization
And we see the barrier synchronization as expected
Now once all the compute processes are done with the computation, they send the data back to the server process
They do free the local memory allocations
Finally as shown in main program before, before the main exits, it uses the MPI_Finalize() call to clear up all MPI DS and returns successfully
Typically involve two, and only tow, different MPI threads
One is performing send and the other is doing the matching receive operation
Different types of send and receive routines
6 types of send routines and 3 types of receive routines
Send/Receive Routines not tightly coupled
Any type of send can be used with any type of receive routine
Blocking
1
Safe means modification will not affect the data to be sent
It does not mean that data was actually received, it may still be in the system buffer
3
Handshake occurs with receive task to confirm safe send
4
If a system buffer is used
Order – pt 1
If a sender sends two messages (Message 1 and Message 2) in succession to the same destination, and both match the same receive, the receive operation will receive Message 1 before Message 2.
If a receiver posts two receives (Receive 1 and Receive 2), in succession, and both are looking for the same message, Receive 1 will receive the message before Receive 2.
Fairness
Task 0 sends a message to task 2. However, task 1 sends a competing message that matches task 2's receive. Only one of the sends will complete.(if no buffer)
Synchronization - processes wait until all members of the group have reached the synchronization point.
Data Movement - broadcast, scatter/gather, all to all.
Collective Computation(reductions) - one member of the group collects data from the other members and performs an operation (min, max, add, multiply, etc.) on that data.
Collective communication functions are highly optimized
Using them usually leads to better performance as well as readability and productivity
From the programmer's perspective, a group and a communicator are one
The group routines are primarily used to specify which processes should be used to construct a communicator.
Groups/communicators are dynamic
created and destroyed during program execution
Processes may be in more than one group/communicator
They will have a unique rank within each group/communicator.
They are only Virtual
No relation between physical structure of machine and process topology
Useful for applications with specific communication patterns
Cartesian topology might prove convenient for an application that requires 4-way nearest neighbor communications for grid based data.
Tell them to see example