Parallel and Heterogeneous Computing with Microsoft PPL and AMP in C++

Computationally intensive work is often best solved by moving to parallel processing, to take advantage of multiple cores. In addition many developers are starting to look towards using different types of hardware to optimize their software further.

Today many C++ programmers are looking to the Microsoft PPL (Parallel Patterns Library) and Microsoft AMP (Accelerated Massive Parallelism) to help them write parallel algorithms to run on both the CPU and GPU. Microsoft PPL is designed to help developers quickly get their code running on multiple cores, while the AMP libraries are intended to remove much of the complexity of heterogeneous computing (particularly around mixing hardware from different manufacturers). This course investigates both PPL and AMP, however it has a larger focus on the more advanced AMP functionality. 

You will come out of “Parallel and Heterogeneous Computing” knowing the following:

  • How to avoid common optimization pitfalls.
  • When to benefit from parallelism.
  • How the underlying hardware contributes to parallelism.
  • How to take advantage of multiple cores with C++ and Microsoft PPL.
  • How to take advantage of the GPU across multiple manufacturers with C++ and Microsoft AMP.
  • How to avoid common parallel and heterogeneous computing pitfalls.
  • How to debug and dig into parallel and heterogeneous programs.

Audience:

  • Experienced C++ Developers.

Prerequisites:

  • Knowledge of multi-threaded programming using threads.

Course outline:

  • Introduction to Parallelism 

    C++11 Refresher

    • Move semantics
    • Deleted/Defaulted Functions
    • Lambdas

     

    Measuring Performance

    • Types of performance
    • Taking good benchmarks
    • Accounting for error

     

    Hardware 

    CPU Internals 

    Instruction level parallelism

    • Understanding Limitations
    • Caching (NUMA)
    • Cost of shared writes
    • Common Pitfalls

     

    Floating Point Numbers

    • 0.1 + 0.2 != 0.3
    • Limitations of floating point
    • Compiler optimizations with floating point
    • Parallel processing and accuracy

     

    Identifying algorithms that are parallelizable

    • CPU bound Vs. IO bound
    • Re-writing for better parallelization.
    • Amdahl’s Law

     

    Parallel patterns and algorithms

    • Map
    • Reduce
    • Scan
    • Pack
    • Command queues
    • Combined examples

     

    Introduction to PPL

    • Why tasks instead of threads?
    • Runtime support
    • Primitives
    • parallel_for
    • non-determinism

     

    Synchronization in PPL

    • critical_section
    • readerwriterlock
    • scopedlock/scopedread
    • event
    • costs of synchronization
    • alternatives

     

    Visual Studio Debug Tools for Concurrency

    • Concurrency Visualizer
    • Parallel Stacks
    • Parallel Tasks
    • Parallel Watch
    • Identifying anti-patterns
    • Debugging PPL Algorithms
    • Event Tracing

     

    Exception Handling

    • Catching task::get/task::wait
    • Exceptions inside parallel_for vs for loops

     

    Similar Technologies to PPL

    • pthread
    • Intel TBB
    • Boost
  • Introduction to Vector programming

    • What is SIMD
    • Masking and execution coherence
    • Memory Layout
    • Structure of Array
    • Array of Structs
    • Auto vectorization

     

    Introduction to the GPU Hardware

    • Hardware
    • Memory Types and Caching
    • Cores, Threads, Tiles and Warps
    • PCIe Bus

     

    Methods of writing code for the GPU

    • OpenCL
    • CUDA
    • DirectCompute
    • Microsoft C++ AMP

     

    Introduction to AMP

    • AMP Syntax and Data Types
    • array, array_view
    • index
    • extent
    • grid
    • restrict

     

    Parallelforeach

    • How to use
    • Optimizing Memory Move/Copy

     

    Synchronizing memory with accelerators

    • Implicit synchronization
    • synchronize*()
    • data()
    • Lost Exceptions

     

    Concurrency::fastmath and precisemath

    • What’s inside
    • Comparison to “standard” math.
    • Precision
    • Accelerator requirements
    • Example

     

    Debugging with Warp

    • Visual Studio Tools
    • GPU Threads
    • Parallel Stacks
    • Parallel Watch

     

    Floating Point Numbers

    • How they are handled
    • Why they are different from CPU
    • Performance of float/double operations
  • Tiling

    • Syntax
    • Determining tile size
    • Memory Coalescence
    • Memory Collisions
    • Tile Synchronization

     

    AMP Atomic Operations

    • atomic_exchange()
    • atomic_fetch*()

     

    Parallel patterns with AMP

    • Map
    • Reduce
    • Scan
    • Pack

     

    AMP Accelerators

    • Accelerator properties
    • Shared memory
    • Using multiple accelerators

     

    Concurrency::graphics

    • Exploiting the texture cache

     

    AMP Error Handling

    • Exceptions
    • Detecting/Recovering from TDR

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