Multi - Core Architectures and Programming - CS8083, CS6801

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Multi - Core Architectures and Programming

An Introduction to Parallel Programming by Peter S Pacheco

Chapter 1 Why Parallel Computing


=> Why Parallel Computing?
=> Why We Need Ever-Increasing Performance
=> Why We’re Building Parallel Systems
=> Why we Need to Write Parallel Programs
=> How Do We Write Parallel Programs?
=> Concurrent, Parallel, Distributed

Chapter 2 Parallel Hardware and Parallel Software


=> Parallel Hardware and Parallel Software
=> Some Background: von Neumann architecture, Processes, multitasking, and threads
=> Modifications to the Von Neumann Model
=> Parallel Hardware
=> Parallel Software
=> Input and Output
=> Performance of Parallel Programming
=> Parallel Program Design with example
=> Writing and Running Parallel Programs
=> Assumptions - Parallel Programming

Chapter 3 Distributed Memory Programming with MPI


=> Distributed-Memory Programming with MPI
=> The Trapezoidal Rule in MPI
=> Dealing with I/O
=> Collective Communication
=> MPI Derived Datatypes
=> Performance Evaluation of MPI Programs
=> A Parallel Sorting Algorithm

Chapter 4 Shared Memory Programming with Pthreads


=> Shared-Memory Programming with Pthreads
=> Processes, Threads, and Pthreads
=> Pthreads - Hello, World Program
=> Matrix-Vector Multiplication
=> Critical Sections
=> Busy-Waiting
=> Mutexes
=> Producer-Consumer Synchronization and Semaphores
=> Barriers and Condition Variables
=> Read-Write Locks
=> Caches, Cache Coherence, and False Sharing
=> Thread-Safety
=> Shared-Memory Programming with OpenMP
=> The Trapezoidal Rule
=> Scope of Variables
=> The Reduction Clause
=> The parallel For Directive
=> More About Loops in Openmp: Sorting
=> Scheduling Loops
=> Producers and Consumers
=> Caches, Cache Coherence, and False Sharing
=> Thread-Safety
=> Parallel Program Development
=> Two n-Body Solvers
=> Parallelizing the basic solver using OpenMP
=> Parallelizing the reduced solver using OpenMP
=> Evaluating the OpenMP codes
=> Parallelizing the solvers using pthreads
=> Parallelizing the basic solver using MPI
=> Parallelizing the reduced solver using MPI
=> Performance of the MPI solvers
=> Tree Search
=> Recursive depth-first search
=> Nonrecursive depth-first search
=> Data structures for the serial implementations
=> Performance of the serial implementations
=> Parallelizing tree search
=> A static parallelization of tree search using pthreads
=> A dynamic parallelization of tree search using pthreads
=> Evaluating the Pthreads tree-search programs
=> Parallelizing the tree-search programs using OpenMP
=> Performance of the OpenMP implementations
=> Implementation of tree search using MPI and static partitioning
=> Implementation of tree search using MPI and dynamic partitioning
=> Which API?

Multicore Application Programming For Windows Linux and Oracle Solaris by Darryl Gove

Chapter 1 Hardware and Processes and Threads


=> Hardware, Processes, and Threads
=> Examining the Insides of a Computer
=> The Motivation for Multicore Processors
=> Supporting Multiple Threads on a Single Chip
=> Increasing Instruction Issue Rate with Pipelined Processor Cores
=> Using Caches to Hold Recently Used Data
=> Using Virtual Memory to Store Data
=> Translating from Virtual Addresses to Physical Addresses
=> The Characteristics of Multiprocessor Systems
=> How Latency and Bandwidth Impact Performance
=> The Translation of Source Code to Assembly Language
=> The Performance of 32-Bit versus 64-Bit Code
=> Ensuring the Correct Order of Memory Operations
=> The Differences Between Processes and Threads

Chapter 2 Coding for Performance


=> Coding for Performance
=> Defining Performance
=> Understanding Algorithmic Complexity
=> Why Algorithmic Complexity Is Important
=> Using Algorithmic Complexity with Care
=> How Structure Impacts Performance
=> Performance and Convenience Trade-Offs in Source Code and Build Structures
=> Using Libraries to Structure Applications
=> The Impact of Data Structures on Performance
=> The Role of the Compiler
=> The Two Types of Compiler Optimization
=> Selecting Appropriate Compiler Options
=> How Cross-File Optimization Can Be Used to Improve Performance
=> Using Profile Feedback
=> How Potential Pointer Aliasing Can Inhibit Compiler Optimizations
=> Identifying Where Time Is Spent Using Profiling
=> Commonly Available Profiling Tools
=> How Not to Optimize
=> Performance by Design

Chapter 3 Identifying Opportunities for Parallelism


=> Identifying Opportunities for Parallelism
=> Using Multiple Processes to Improve System Productivity
=> Multiple Users Utilizing a Single System
=> Improving Machine Efficiency Through Consolidation
=> Using Containers to Isolate Applications Sharing a Single System
=> Hosting Multiple Operating Systems Using Hypervisors
=> Using Parallelism to Improve the Performance of a Single Task
=> One Approach to Visualizing Parallel Applications
=> How Parallelism Can Change the Choice of Algorithms
=> Amdahl’s Law
=> Determining the Maximum Practical Threads
=> How Synchronization Costs Reduce Scaling
=> Parallelization Patterns
=> Data Parallelism Using SIMD Instructions
=> Parallelization Using Processes or Threads
=> Multiple Independent Tasks
=> Multiple Loosely Coupled Tasks
=> Multiple Copies of the Same Task
=> Single Task Split Over Multiple Threads
=> Using a Pipeline of Tasks to Work on a Single Item
=> Division of Work into a Client and a Server
=> Splitting Responsibility into a Producer and a Consumer
=> Combining Parallelization Strategies
=> How Dependencies Influence the Ability Run Code in Parallel
=> Antidependencies and Output Dependencies
=> Using Speculation to Break Dependencies
=> Critical Paths
=> Identifying Parallelization Opportunities

Chapter 4 Synchronization and Data Sharing


=> Synchronization and Data Sharing
=> Data Races
=> Using Tools to Detect Data Races
=> Avoiding Data Races
=> Synchronization Primitives
=> Mutexes and Critical Regions
=> Spin Locks
=> Semaphores
=> Readers-Writer Locks
=> Barriers
=> Atomic Operations and Lock-Free Code
=> Deadlocks and Livelocks
=> Communication Between Threads and Processes
=> Storing Thread-Private Data

Chapter 5 Using POSIX Threads


=> Using POSIX Threads
=> Creating Threads
=> Compiling Multithreaded Code
=> Process Termination
=> Sharing Data Between Threads
=> Variables and Memory
=> Multiprocess Programming
=> Sockets
=> Reentrant Code and Compiler Flags
=> Windows Threading

Chapter 6 Windows Threading


=> Creating Native Windows Threads
=> Terminating Threads
=> Creating and Resuming Suspended Threads
=> Using Handles to Kernel Resources
=> Methods of Synchronization and Resource Sharing
=> An Example of Requiring Synchronization Between Threads
=> Protecting Access to Code with Critical Sections
=> Protecting Regions of Code with Mutexes
=> Slim Reader/Writer Locks
=> Signaling Event Completion to Other Threads or Processes
=> Wide String Handling in Windows
=> Creating Processes
=> Sharing Memory Between Processes
=> Inheriting Handles in Child Processes
=> Naming Mutexes and Sharing Them Between Processes
=> Communicating with Pipes
=> Communicating Using Sockets
=> Atomic Updates of Variables
=> Allocating Thread-Local Storage
=> Setting Thread Priority

Chapter 7 Using Automatic Parallelization and OpenMP


=> Using Automatic Parallelization and OpenMP
=> Using Automatic Parallelization to Produce a Parallel Application
=> Identifying and Parallelizing Reductions
=> Automatic Parallelization of Codes Containing Calls
=> Assisting Compiler in Automatically Parallelizing Code
=> Using OpenMP to Produce a Parallel Application
=> Using OpenMP to Parallelize Loops
=> Runtime Behavior of an OpenMP Application
=> Variable Scoping Inside OpenMP Parallel Regions
=> Parallelizing Reductions Using OpenMP
=> Accessing Private Data Outside the Parallel Region
=> Improving Work Distribution Using Scheduling
=> Using Parallel Sections to Perform Independent Work
=> Nested Parallelism
=> Using OpenMP for Dynamically Defined Parallel Tasks
=> Keeping Data Private to Threads
=> Controlling the OpenMP Runtime Environment
=> Waiting for Work to Complete
=> Restricting the Threads That Execute a Region of Code
=> Ensuring That Code in a Parallel Region Is Executed in Order
=> Collapsing Loops to Improve Workload Balance
=> Enforcing Memory Consistency
=> An Example of Parallelization

Chapter 8 Hand Coded Synchronization and Sharing


=> Hand-Coded Synchronization and Sharing
=> Atomic Operations
=> Using Compare and Swap Instructions to Form More Complex Atomic Operations
=> Enforcing Memory Ordering to Ensure Correct Operation
=> Compiler Support of Memory-Ordering Directives
=> Reordering of Operations by the Compiler
=> Volatile Variables
=> Operating System–Provided Atomics
=> Lockless Algorithms
=> Dekker’s Algorithm
=> Producer-Consumer with a Circular Buffer
=> Scaling to Multiple Consumers or Producers
=> Scaling the Producer-Consumer to Multiple Threads
=> Modifying the Producer-Consumer Code to Use Atomics
=> The ABA Problem

Chapter 9 Scaling with Multicore Processors


=> Scaling with Multicore Processors
=> Constraints to Application Scaling
=> Hardware Constraints to Scaling
=> Bandwidth Sharing Between Cores
=> False Sharing
=> Cache Conflict and Capacity
=> Pipeline Resource Starvation
=> Operating System Constraints to Scaling
=> Multicore Processors and Scaling

Chapter 10 Other Parallelization Technologies


=> Other Parallelization Technologies
=> GPU-Based Computing
=> Language Extensions
=> Alternative Languages
=> Clustering Technologies
=> Transactional Memory
=> Vectorization

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