Unit 1 - Introduction to Parallel Computing
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Questions and Answers

From 1986 to 2003, the performance of microprocessors increased, on average, more than 50% per year.

True (A)

Since 2003, single processor performance improvement has slowed to the point that in the period from 2015 to 2017, it increased at less than 4% per year.

True (A)

The shift towards parallelism in processor design was primarily driven by the need to improve the performance of existing serial programs.

False (B)

The performance of a serial program run on a system with multiple processors will be significantly faster than its performance on a single processor.

<p>False (B)</p> Signup and view all the answers

What is the key difference between serial and parallel computing?

<p>Serial computing executes instructions sequentially, one after another, while parallel computing allows multiple instructions to be executed simultaneously.</p> Signup and view all the answers

Which of the following are limitations of serial computing? (Select all that apply)

<p>Multitasking (A), Scalability (B), Speed (C), Resource Utilization (D)</p> Signup and view all the answers

Parallel computing aims to leverage multiple computing sources to solve a single problem simultaneously.

<p>True (A)</p> Signup and view all the answers

Concurrency implies that tasks are executed simultaneously, while parallelism means that tasks happen within the same timeframe but might not be truly simultaneous.

<p>False (B)</p> Signup and view all the answers

What is the fundamental concept behind parallel computing?

<p>Parallel computing is the simultaneous use of multiple computing resources to solve a computational problem.</p> Signup and view all the answers

Parallel computing always requires the use of specialized hardware like GPUs to achieve significant performance gains.

<p>False (B)</p> Signup and view all the answers

Parallelism is about doing multiple things at the same time to reduce the time needed for the task.

<p>True (A)</p> Signup and view all the answers

What are some potential computational resources used for parallel computing?

<p>A single computer with multiple processors (A), (Multiple) processors and specialized computer resources like GPUs (B), An arbitrary number of networked computers (C), A combination of any of the above (D)</p> Signup and view all the answers

Parallel computing is only suitable for solving complex scientific problems and not applicable to everyday applications.

<p>False (B)</p> Signup and view all the answers

Problems suitable for parallel computing should be divisible into tasks that can be executed independently.

<p>True (A)</p> Signup and view all the answers

Parallel programming requires a fundamental and complete rewrite of existing serial programs to achieve optimal parallel performance.

<p>False (B)</p> Signup and view all the answers

Task parallelism involves breaking down the task into smaller, independent tasks, while data parallelism distributes the data among the cores, and each core processes its portion.

<p>True (A)</p> Signup and view all the answers

Shared memory systems allow cores to directly access the same shared memory, while distributed memory systems necessitate explicit communication between cores.

<p>True (A)</p> Signup and view all the answers

MIMD (Multiple Instruction Multiple Data) systems allow each core to execute its own instructions on private data, while SIMD (Single Instruction Multiple Data) systems require all cores to execute the same instruction on separate data.

<p>True (A)</p> Signup and view all the answers

MPI is an API ideal for programming shared memory MIMD systems, while Pthreads is suitable for programming distributed memory MIMD systems.

<p>False (B)</p> Signup and view all the answers

OpenMP can be used to program both shared memory MIMD and shared memory SIMD systems, but CUDA is specifically designed for programming Nvidia GPUs.

<p>True (A)</p> Signup and view all the answers

What is the main reason for learning about multiple APIs for parallel programming instead of just one?

<p>Different APIs cater to distinct parallel systems, including shared memory and distributed memory, as well as MIMD and SIMD architectures.</p> Signup and view all the answers

Flashcards

Parallelism

The ability to rapidly increase performance by adding additional processing units (cores) to a system.

Serial Programs

Programs designed to run on a single processor, unaware of the existence of multiple cores.

Performance Saturation

The slowing down of single processor performance improvements since 2003.

Parallel Computing

The use of multiple computational resources to solve a single problem simultaneously.

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Scalability Issue (Serial Computing)

The time required to complete a task in serial computing may become impractical as the task size or complexity increases.

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Speed Limitation (Serial Computing)

The execution of instructions one after another, leading to potentially slow overall execution times.

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Resource Utilization Issue (Serial Computing)

The potential for unused resources in serial computing, such as a CPU not fully utilized while waiting for a single instruction to complete.

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Parallel Computing (Simple Definition)

A type of computing where a problem is broken into discrete parts that can be solved concurrently using multiple CPUs.

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Parallel Computing (Multiple resources)

Multiple processors or computers working together on a common task.

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Parallel Computing Resources

Computational resources used in parallel computing, such as single computers with multiple processors, specialized hardware (GPUs, FPGAs), networked computers, or combinations thereof.

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Multicore Processor

A single computer with multiple CPUs, each capable of performing independent tasks.

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Task-Parallelism

A type of parallel computing where the problem is divided into tasks, and each processor is assigned a different task.

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Data-Parallelism

A type of parallel computing where data used in solving a problem is partitioned among processors, each performing similar operations on their assigned data.

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Coordination in Parallel Computing

The ability for processors in a parallel system to communicate and synchronize their actions.

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Load Balancing

The process of distributing tasks or data among processors in a parallel system to ensure efficient and balanced workload distribution.

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Message-Passing Interface (MPI)

A common approach to program parallel computers using message passing, where processors explicitly communicate with each other.

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POSIX Threads (Pthreads)

A standard API for programming shared memory MIMD systems, allowing processors to share access to the system's memory.

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OpenMP

A directive-based API for programming shared-memory and distributed-memory MIMD systems, as well as SIMD systems.

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CUDA

An API designed for programming Nvidia GPUs, enabling parallel processing on graphics processing units.

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Shared-Memory System

A type of parallel system where processors can access and modify a shared memory space.

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Distributed-Memory System

A type of parallel system where each processor has its own private memory and communication between processors occurs through explicit message passing.

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Multiple-Instruction Multiple-Data (MIMD) System

A parallel system where processors have their own control units and can execute different instructions simultaneously on different data.

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Single-Instruction Multiple-Data (SIMD) System

A parallel system where processors share a single control unit and execute the same instruction on different data.

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Scalability (Parallel Programming)

The ability for a parallel program to execute efficiently on systems with increasing numbers of processors.

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Parallel Program

A parallel program designed to take advantage of the presence of multiple processor cores.

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Program Parallelization

The process of transforming a serial program into a parallel program to utilize multiple processor cores.

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Algorithm Parallelization

The process of designing a new algorithm specifically tailored for parallel execution, potentially resulting in better performance compared to simply parallelizing an existing serial algorithm.

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Synchronization

The process of synchronizing the execution of multiple processors to ensure that they operate in a coordinated manner, avoiding data races or unintended interactions.

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Communication Protocol (Parallel Computing)

A set of guidelines or protocols that dictate how processors in a parallel system communicate and interact with each other.

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Load Balancing (Parallel Programming)

The design of a parallel program that prioritizes balancing the workload among processors to ensure efficient utilization of resources.

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Parallel Efficiency

A measure of the effectiveness of a parallel program in utilizing available computational resources, often related to the speedup achieved compared to running the same program on a single processor.

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Study Notes

Unit 1 - Introduction to Parallel Computing

  • Parallel computing involves the simultaneous use of multiple compute resources to solve a single problem.
  • This is in contrast to serial computation, where instructions are executed one after another.
  • Performance of microprocessors increased significantly (over 50% per year) from 1986 to 2003.
  • However, since 2003, single processor performance improvement has slowed considerably.
  • By 2005, manufacturers had started using parallelism rather than faster monolithic processors.
  • This shift led to multicore processors.

History of Parallel Computing

  • The increasing performance difference between microprocessors in the recent decades led to the development of parallel computing as a way to solve increasingly complex problems.
  • The need for more powerful computers is associated with climate modeling, energy research. and complex data analysis.

Serial Computation

  • Traditionally, software is written for serial execution.
  • In a serial computation, instructions are executed one after another.
  • This approach can be inefficient, leading to slower overall execution times, specifically for complex tasks and large datasets.

Limitations of Serial Computing

  • Speed: Serial computing can be slow for tasks that can be parallelized, especially complex tasks.
  • Resource utilization: Serial computing uses computational resources inefficiently, as only one instruction can be executed at a time, leaving other parts idle.
  • Scalability: Serial computing may not scale well to complete large and complex problems.
  • Limited multitasking: Serial computing can be challenging to perform multiple tasks simultaneously, which can negatively impact the overall efficiency and responsiveness of the system.

Parallel Computing

  • Parallel computing involves the simultaneous use of multiple compute resources (processors or computers) to solve a computational problem.
  • Unlike serial computing, which executes instructions linearly, parallel computation divides a problem into smaller, independent parts that execute simultaneously.
  • This allows for faster and more efficient execution of tasks, especially complex ones.

Parallel vs Concurrent

  • Concurrency means two or more calculations occur in the same time frame but are interdependent (dependent on each other).
  • Parallelism means two or more calculations occur at the same time, independently of each other.

What is Parallel Computing?

  • In simplest terms, parallel computing is the simultaneous use of multiple compute resources to solve a computational problem.
  • A problem is broken down into smaller parts that can execute concurrently on multiple processors or computers.
  • Instructions from the various parts execute concurrently on different CPUs.

Parallelism

  • Parallelism means doing multiple things at the same time.
  • It allows more to be accomplished in less time.

Parallel Computing: Resources

  • The compute resources in parallel computing can be varied.

  • A single computer can have multiple processors.

  • A single computer can be equipped with specialized computer resources like GPU or FPGA.

  • An arbitrary number of networked computers can work together as a parallel system.

  • The combination of these components can be used in a parallel system.

  • Multicore processors - combining multiple complete processor on a single integrated circuit.

Characteristics of Computational Problem

  • Problems can be broken down into smaller pieces of work to be solved simultaneously.
  • Multiple program instructions can be executed at the same time.
  • Parallel computing can solve large problems in less time than with single compute resources.

Why We Need Ever-Increasing Performance

  • As computational power increases, the number of solvable problems also increases.
  • Examples include climate modeling and energy research.
  • Advances in computation power are needed for detailed modeling of complex systems, like the Earth's climate or energy technologies.

Data Analysis

  • The quantity of data increases rapidly and require advanced tools for processing and analysis.
  • Analysis of human DNA, particle colliders data and medical images etc. require parallel computing for efficient analysis.

Application of Parallel Computing

  • Parallel computing is necessary for solving large or complex problems (like weather, climate, chemical/nuclear reaction etc.), handling complex interactions between systems.

Why We're Building Parallel Systems

  • Transistor densities have increased, leading to faster processors.
  • However, further increases in the speed of processors are becoming harder due to power consumption limitations.
  • Parallelism becomes a necessary approach to further increase computing power, allowing multiple simple processors to operate on a single chip.

Why We Need to Write Parallel Programs

  • Most existing programs are not optimized for parallel execution.
  • To harness the potential of multicore systems, programs need to be rewritten or adapted to take advantage of the multiple processors.

Efficient Parallel Implementations

  • The most efficient approach to a parallel computation may involve redesigning the algorithm and not just parallelizing steps in an existing algorithm.

Parallel Computing - Resources continued

  • CPU, GPU, FPGA.
  • Distributed computer networks
  • Multicore processors

Characteristics of Computational Problem continued

  • Problems can be solved more effectively and faster with multiple concurrent processing units.
  • A problem could be divided into smaller pieces of work to be solved simultaneously on various computing cores or processor cores.

Coordination in Parallel Computing

  • Communication: Cores need to communicate and exchange information to coordinate operations in parallel computation.
  • Load Balancing: Cores should be assigned roughly equal amounts of work to ensure efficient use of available computing resources
  • Synchronization: Cores must be synchronized to prevent errors or problems caused by a lack of coordination.

What We'll be Doing in This Course

  • The course will focus on programming for parallel computers using C language and different Application Programming Interfaces (APIs ).
    • MPI, POSIX threads, OpenMP and CUDA.

Memory in Parallel Systems

  • Classification of parallel systems can be made in various ways, with one way being through the method of memory organization.

    • Shared-Memory Systems - Cores share access to the same memory area.
    • Distributed-Memory Systems - Each core has its own separate memory area, and cores must explicitly communicate with each other to exchange data.

  • The systems can be classified based on how the cores access memory (independent instruction streams and independent data streams).

    • Multiple-Instruction, Multiple-Data (MIMD) systems
    • Single-Instruction, Multiple-Data (SIMD) systems

  • Different APIs (like MPI, Pthreads, OpenMP, and CUDA) are intended for executing in these different systems.

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Parallel Computing Unit 1 - Introduction to Parallel Computing PDF

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This quiz covers the fundamentals of parallel computing, including its history, development, and comparison with serial computation. Learn about the evolution of microprocessor performance and the shift towards multicore processors to manage complex problem-solving effectively.

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