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Farhat Rams
Farhat Rams

Learn the Basics of Multithreaded, Parallel and Distributed Programming with MPD



Heading Subheading Content --- --- --- H1: Foundations of Multithreaded, Parallel and Distributed Programming Introduction: What are multithreaded, parallel and distributed programming and why are they important? H2: Multithreaded Programming Definition: A program that uses multiple threads of execution to perform concurrent tasks within a single process. Benefits: Improved performance, responsiveness, modularity and scalability. Challenges: Synchronization, deadlock, race conditions and memory consistency. H2: Parallel Programming Definition: A program that uses multiple processors or cores to execute multiple tasks simultaneously. Benefits: Increased speedup, throughput and efficiency. Challenges: Load balancing, communication, coordination and scalability. H2: Distributed Programming Definition: A program that uses multiple networked computers to execute multiple tasks across different locations. Benefits: Enhanced availability, reliability, fault tolerance and scalability. Challenges: Latency, bandwidth, security and consistency. H1: Core Concepts and Techniques for Multithreaded, Parallel and Distributed Programming Overview: What are the common concepts and techniques that apply to all three types of programming? H2: Concurrency Models Definition: A way of describing how multiple tasks interact and coordinate with each other. Examples: Shared memory, message passing, remote invocation, event-driven, actor-based and functional. H2: Synchronization Mechanisms Definition: A way of ensuring that multiple tasks access shared resources in a consistent and orderly manner. Examples: Locks, semaphores, monitors, barriers, atomic operations and transactions. H2: Performance Metrics and Analysis Definition: A way of measuring and evaluating the performance of multithreaded, parallel and distributed programs. Examples: Speedup, efficiency, scalability, overhead, bottleneck and workload. H1: Case Studies of Multithreaded, Parallel and Distributed Programming Languages, Libraries and Tools Overview: What are some of the popular and widely used languages, libraries and tools for multithreaded, parallel and distributed programming? H2: Java Threads and Sockets Description: Java is an object-oriented language that supports multithreading and networking through its built-in classes and interfaces. Features: Thread creation and management, synchronization using monitors and locks, socket programming using streams and datagrams, remote method invocation using stubs and skeletons. H2: Pthreads, MPI and OpenMP Libraries for C/C++ Description: Pthreads is a library that provides a standard interface for creating and manipulating threads in C/C++. MPI is a library that provides a standard interface for message passing between processes in C/C++. OpenMP is a library that provides a standard interface for parallel loops and regions in C/C++. Features: Thread creation and management using attributes and routines, synchronization using mutexes and condition variables, message passing using communicators and collective operations, parallel loops and regions using directives and clauses. Heading Subheading Content --- --- --- each other. A concurrency model defines the basic elements, rules and guarantees of concurrency. A concurrency model can be either explicit or implicit, depending on whether the programmer has to specify the details of concurrency or not.




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  • Examples: Some examples of concurrency models are:Shared memory: Shared memory is a concurrency model where multiple tasks share the same address space and access the same variables and data structures. Shared memory is an explicit concurrency model, as the programmer has to specify how to create and manage tasks, how to synchronize and coordinate tasks, and how to ensure memory consistency. Shared memory is commonly used for multithreaded and parallel programming, as it allows fast and direct communication between tasks.

  • Message passing: Message passing is a concurrency model where multiple tasks have their own address space and communicate by sending and receiving messages. Message passing is an explicit concurrency model, as the programmer has to specify how to create and manage tasks, how to send and receive messages, and how to handle errors and failures. Message passing is commonly used for parallel and distributed programming, as it allows flexible and scalable communication between tasks.

  • Remote invocation: Remote invocation is a concurrency model where multiple tasks invoke methods or functions on remote objects or services. Remote invocation is an implicit concurrency model, as the programmer does not have to specify the details of concurrency, such as task creation and management, message passing and synchronization. Remote invocation is commonly used for distributed programming, as it allows transparent and high-level communication between tasks.

  • Event-driven: Event-driven is a concurrency model where multiple tasks react to events that occur in the system or environment. Event-driven is an implicit concurrency model, as the programmer does not have to specify the details of concurrency, such as task creation and management, synchronization and coordination. Event-driven is commonly used for interactive and reactive programming, as it allows responsive and adaptive behavior of tasks.

  • Actor-based: Actor-based is a concurrency model where multiple tasks are actors that have their own state and behavior and communicate by sending messages. Actor-based is an implicit concurrency model, as the programmer does not have to specify the details of concurrency, such as task creation and management, synchronization and coordination. Actor-based is commonly used for concurrent and distributed programming, as it allows modular and scalable design of tasks.

  • Functional: Functional is a concurrency model where multiple tasks are functions that have no side effects and communicate by passing values. Functional is an implicit concurrency model, as the programmer does not have to specify the details of concurrency, such as task creation and management, synchronization and coordination. Functional is commonly used for parallel and distributed programming, as it allows simple and elegant expression of tasks.

  • H2: Synchronization Mechanisms Definition: A synchronization mechanism is a way of ensuring that multiple tasks access shared resources in a consistent and orderly manner. A synchronization mechanism defines the basic operations, rules and guarantees of synchronization. A synchronization mechanism can be either blocking or non-blocking, depending on whether the tasks have to wait for each other or not. Examples: Some examples of synchronization mechanisms are:Locks: Locks are a synchronization mechanism where a shared resource can be accessed by only one task at a time. Locks are a blocking synchronization mechanism, as a task has to wait until the lock is available before accessing the resource. Locks provide mutual exclusion, which means that only one task can enter a critical section at a time. Locks are commonly used for shared memory programming, as they prevent data inconsistency.

  • Semaphores: Semaphores are a synchronization mechanism where a shared resource can be accessed by a limited number of tasks at a time. Semaphores are a blocking synchronization mechanism, as a task has to wait until the semaphore has enough permits before accessing the resource. Semaphores provide counting, which means that they keep track of how many tasks can enter a critical section at a time. Semaphores are commonly used for shared memory programming, as they control access to limited resources.

  • Monitors: Monitors are a synchronization mechanism where a shared resource can be accessed by only one task at a time within a class or an object. Monitors are a blocking synchronization mechanism, as a task has to wait until the monitor is available before accessing the resource. Monitors provide encapsulation, which means that they hide the details of synchronization within a class or an object. Monitors are commonly used for object-oriented programming, as they ensure data integrity.

  • Barriers: Barriers are a synchronization mechanism where multiple tasks have to wait for each other before proceeding to the next step. Barriers are a blocking synchronization mechanism, as a task has to wait until all the other tasks reach the barrier before proceeding. Barriers provide synchronization, which means that they align the execution of tasks at certain points. Barriers are commonly used for parallel programming, as they coordinate the phases of computation.

  • Atomic operations: Atomic operations are a synchronization mechanism where multiple tasks can access shared resources without interference. Atomic operations are a non-blocking synchronization mechanism, as a task does not have to wait for other tasks before accessing the resource. Atomic operations provide atomicity, which means that they appear to be executed in one indivisible step. Atomic operations are commonly used for concurrent programming, as they avoid locking and blocking.

  • Transactions: Transactions are a synchronization mechanism where multiple tasks can access shared resources in an isolated and consistent manner. Transactions are a non-blocking synchronization mechanism, as a task does not have to wait for other tasks before accessing the resource. Transactions provide isolation and consistency, which means that they prevent interference and ensure correctness of data. Transactions are commonly used for distributed programming, as they cope with failures and concurrency.

  • H2: Performance Metrics and Analysis Definition: A performance metric is a way of measuring and evaluating the performance of multithreaded, parallel and distributed programs. A performance metric defines the basic units, formulas and values of performance. A performance metric can be either absolute or relative, depending on whether it compares the performance of a program to itself or to another program. Examples: Some examples of performance metrics are:Speedup: Speedup is a performance metric that compares the execution time of a sequential program to the execution time of a parallel program. Speedup is a relative performance metric, as it measures how much faster a parallel program is than a sequential program. Speedup is calculated by dividing the execution time of the sequential program by the execution time of the parallel program. Speedup can be either ideal or actual, depending on whether it assumes perfect or realistic conditions.

  • Efficiency: Efficiency is a performance metric that compares the speedup of a parallel program to the number of processors or cores. Efficiency is a relative performance metric, as it measures how well a parallel program utilizes the available resources. Efficiency is calculated by dividing the speedup by the number of processors or cores. Efficiency can be either ideal or actual, depending on whether it assumes perfect or realistic conditions.

  • Scalability: Scalability is a performance metric that compares the speedup of a parallel program to the increase in the number of processors or cores. Scalability is a relative performance metric, as it measures how well a parallel program adapts to different workloads and environments. Scalability is calculated by dividing the speedup by the increase in the number of processors or cores. Scalability can be either ideal or actual, depending on whether it assumes perfect or realistic conditions.

  • Overhead: Overhead is a performance metric that measures the extra work or time that is required by a parallel program compared to a sequential program. Overhead is an absolute performance metric, as it measures how much slower or more complex a parallel program is than a sequential program. Overhead can be either positive or negative, depending on whether it increases or decreases the execution time or complexity of a parallel program.

  • Bottleneck: Bottleneck is a performance metric that measures the limiting factor that prevents a parallel program from achieving higher speedup or efficiency. Bottleneck is an absolute performance metric, as it measures how much slower or more difficult a parallel program is than its potential. Bottleneck can be either internal or external, depending on whether it originates from within or outside the parallel program.

  • Workload: Workload is a performance metric that measures the amount and type of work that is performed by a parallel program. Workload is an absolute performance metric, as it measures how much work or what kind of work a parallel program does. Workload can be either static or dynamic, depending on whether it remains constant or changes during the execution of a parallel program.

  • H1: Case Studies of Multithreaded, Parallel and Distributed Programming Languages, Libraries and Tools Overview: In this section, we will look at some case studies of real-world applications that use multithreaded, parallel and distributed programming languages, libraries and tools. We will describe what these languages, libraries and tools are, what features they provide, and how they are used in practice. Heading Subheading Content --- --- --- classes and interfaces. Java is a widely used language for developing cross-platform applications that can run on multiple devices and platforms. Features: Some features of Java threads and sockets are:Thread creation and management: Java provides the Thread class and the Runnable interface to create and manage threads. A thread can be created by either extending the Thread class or implementing the Runnable interface and passing it to a Thread object. A thread can be started by calling the start method and stopped by calling the interrupt method. A thread can also be joined, suspended, resumed or prioritized by using other methods of the Thread class.

  • Synchronization using monitors and locks: Java provides the synchronized keyword and the Lock interface to synchronize threads that access shared resources. The synchronized keyword can be used to create a monitor, which is a block of code that can be executed by only one thread at a time. The synchronized keyword can be applied to either a method or a statement. The Lock interface can be used to create a lock, which is an object that can be acquired and released by threads. The Lock interface provides more flexibility and functionality than the synchronized keyword, such as try-locking, timed-locking and condition variables.

  • Socket programming using streams and datagrams: Java provides the Socket class and the DatagramSocket class to communicate over networks using streams and datagrams. A stream is a sequence of bytes that can be sent or received over a reliable and connection-oriented protocol, such as TCP. A datagram is a packet of bytes that can be sent or received over an unreliable and connectionless protocol, such as UDP. A socket is an endpoint of communication that can be bound to a port and an address. A socket can be either a server socket or a client socket, depending on whether it listens for or initiates connections.

  • Remote method invocation using stubs and skeletons: Java provides the Remote interface and the RMI registry to invoke methods on remote objects or services. A remote object or service is an object or a class that implements the Remote interface and resides on a different computer. A remote method invocation is a call to a method of a remote object or service that is transparent to the caller. A stub is an object that acts as a proxy for a remote object or service on the client side. A skeleton is an object that acts as a dispatcher for a remote object or service on the server side. A RMI registry is a service that maintains a mapping between names and remote objects or services.

  • H2: Pthreads, MPI and OpenMP Libraries for C/C++ Description: Pthreads, MPI and OpenMP are libraries that provide standard interfaces for multithreaded, parallel and distributed programming in C/C++. C/C++ are low-level languages that offer high performance and control over hardware resources. Features: Some features of Pthreads, MPI and OpenMP are:Thread creation and management using attributes and routines: Pthreads (POSIX Threads) is a library that provides a standard interface for creating and manipulating threads in C/C++. A thread can be created by calling the pthread_create routine and passing it a thread attribute object, a function pointer and an argument. A thread can be terminated by calling the pthread_exit routine or returning from the function. A thread can also be joined, detached, canceled or signaled by using other routines of the Pthreads library.

  • Message passing using communicators and collective operations: MPI (Message Passing Interface) is a library that provides a standard interface for message passing between processes in C/C++. A process is an instance of a program that runs on one or more processors or cores. A message is a unit of data that can be sent or received by processes. A communicator is an object that defines a group of processes that can communicate with each other. A collective operation is an operation that involves all processes in a communicator, such as broadcast, reduce or scatter.

  • Parallel loops and regions using directives and clauses: OpenMP (Open Multi-Processing) is a library that provides a standard interface for parallel loops and regions in C/C++. A parallel loop is a loop that can be executed by multiple threads in parallel. A parallel region is a block of code that can be executed by multiple threads in parallel. A directive is a pragma that instructs the compiler how to parallelize a loop or a region. A clause is an option that modifies the behavior of a directive, such as specifying the number of threads, the distribution of work or the sharing of variables.

  • H2: MPD Programming Language for Multithreading and Distributed Computing Description: MPD (Multi-Paradigm Distributed) is a programming language that enables students to write programs using a syntax that is similar to C/C++, but with features that support multithreading and distributed computing. MPD is essentially an alternative syntax for SR (Synchronizing Resources), which is a language that supports concurrency and communication through the concept of resources. MPD is designed to be simple and expressive, and to illustrate the core concepts and techniques of multithreaded, parallel and distributed programming. Features: Some features of MPD are:Resource creation and management: A resource is an entity that has a name, a state and a behavior. A resource can be either local or remote, depending on whether it resides on the same or a different computer. A resource can be created by using the resource keyword and specifying its name, state and behavior. A resource can be terminated by using the terminate keyword or reaching the end of its behavior.

Communication and synchronization using operations and capabilities: An operation is an action that can be performed on a resource, such as sending or receiving data, invoking or returning from a method, or creating or terminating a subresource. A capability is an object that represents the right to perform an


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