Podcast
Questions and Answers
From 1986 to 2003, the performance of microprocessors increased, on average, more than 50% per year.
From 1986 to 2003, the performance of microprocessors increased, on average, more than 50% per year.
True
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.
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
The shift towards parallelism in processor design was primarily driven by the need to improve the performance of existing serial programs.
The shift towards parallelism in processor design was primarily driven by the need to improve the performance of existing serial programs.
False
The performance of a serial program run on a system with multiple processors will be significantly faster than its performance on a single processor.
The performance of a serial program run on a system with multiple processors will be significantly faster than its performance on a single processor.
Signup and view all the answers
What is the key difference between serial and parallel computing?
What is the key difference between serial and parallel computing?
Signup and view all the answers
Which of the following are limitations of serial computing? (Select all that apply)
Which of the following are limitations of serial computing? (Select all that apply)
Signup and view all the answers
Parallel computing aims to leverage multiple computing sources to solve a single problem simultaneously.
Parallel computing aims to leverage multiple computing sources to solve a single problem simultaneously.
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.
Concurrency implies that tasks are executed simultaneously, while parallelism means that tasks happen within the same timeframe but might not be truly simultaneous.
Signup and view all the answers
What is the fundamental concept behind parallel computing?
What is the fundamental concept behind parallel computing?
Signup and view all the answers
Parallel computing always requires the use of specialized hardware like GPUs to achieve significant performance gains.
Parallel computing always requires the use of specialized hardware like GPUs to achieve significant performance gains.
Signup and view all the answers
Parallelism is about doing multiple things at the same time to reduce the time needed for the task.
Parallelism is about doing multiple things at the same time to reduce the time needed for the task.
Signup and view all the answers
What are some potential computational resources used for parallel computing?
What are some potential computational resources used for parallel computing?
Signup and view all the answers
Parallel computing is only suitable for solving complex scientific problems and not applicable to everyday applications.
Parallel computing is only suitable for solving complex scientific problems and not applicable to everyday applications.
Signup and view all the answers
Problems suitable for parallel computing should be divisible into tasks that can be executed independently.
Problems suitable for parallel computing should be divisible into tasks that can be executed independently.
Signup and view all the answers
Parallel programming requires a fundamental and complete rewrite of existing serial programs to achieve optimal parallel performance.
Parallel programming requires a fundamental and complete rewrite of existing serial programs to achieve optimal parallel performance.
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.
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.
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.
Shared memory systems allow cores to directly access the same shared memory, while distributed memory systems necessitate explicit communication between cores.
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.
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.
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.
MPI is an API ideal for programming shared memory MIMD systems, while Pthreads is suitable for programming distributed memory MIMD systems.
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.
OpenMP can be used to program both shared memory MIMD and shared memory SIMD systems, but CUDA is specifically designed for programming Nvidia GPUs.
Signup and view all the answers
What is the main reason for learning about multiple APIs for parallel programming instead of just one?
What is the main reason for learning about multiple APIs for parallel programming instead of just one?
Signup and view all the answers
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.
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
Related Documents
Description
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.
