Introduction to Heterogeneous Parallel Computing
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Questions and Answers

What is a primary goal of programming heterogeneous parallel computing systems?

  • Simplifying programming for sequential tasks
  • Increasing single-thread performance
  • Achieving high performance and energy-efficiency (correct)
  • Eliminating the need for multiple processors

    • Which of the following best describes the concept of scalability in heterogeneous parallel computing?

  • Ability to run programs on a single core without modification
  • Requirement for programs to be vendor-specific
  • Capability to maintain performance as hardware resources increase (correct)
  • Limitation to only small-scale applications

    • Which parallel programming model is known for its ability to support a wide range of hardware architectures while allowing for high-level programming?

  • CUDA C
  • MPI
  • OpenCL (correct)
  • OpenACC

    • What is one of the key principles for understanding CUDA memory models?

    <p>Efficient memory usage minimizes data transfers</p> Signup and view all the answers

    Which aspect is NOT a focus when learning about parallel algorithms in this course?

    <p>Enhancing single-thread performance</p> Signup and view all the answers

    What does Unified Memory in CUDA primarily aim to achieve?

    <p>Simplifying data management between host and device</p> Signup and view all the answers

    Which aspect is NOT typically associated with OpenCL?

    <p>Single-threaded execution only</p> Signup and view all the answers

    In the context of parallel programming, what is the significance of Dynamic Parallelism?

    <p>It allows devices to execute processes without host intervention</p> Signup and view all the answers

    What is a primary function of CUDA streams in achieving task parallelism?

    <p>Facilitating simultaneous data transfers and computations</p> Signup and view all the answers

    How does OpenACC primarily optimize parallel computing?

    <p>By providing directive-based programming for parallelism</p> Signup and view all the answers

    Which feature is NOT part of the CUDA memory model?

    <p>Automatic variable cleanup post kernel execution</p> Signup and view all the answers

    The implementation of MPI in joint MPI-CUDA programming primarily facilitates what?

    <p>Communicating between multiple processes over networked devices</p> Signup and view all the answers

    Which application case study demonstrates the use of Electrostatic Potential Calculation?

    <p>Magnetic Resonance Imaging (MRI)</p> Signup and view all the answers

    What is a significant benefit of using parallel scan algorithms in CUDA?

    <p>They facilitate per-thread output variable allocation.</p> Signup and view all the answers

    In the context of the advanced CUDA memory model, what is the function of texture memory?

    <p>It allows for efficient spatial memory locality for large datasets.</p> Signup and view all the answers

    Which of the following describes a key distinction between OpenCL and OpenACC?

    <p>OpenACC emphasizes compiler directives, while OpenCL provides more control over parallelism.</p> Signup and view all the answers

    What is the primary goal of memory coalescing in CUDA?

    <p>To optimize memory access patterns and reduce latency.</p> Signup and view all the answers

    What is the main characteristic of the work-efficient parallel scan kernel?

    <p>It processes data in a single pass without needing multiple iterations.</p> Signup and view all the answers

    What kind of performance consideration is essential when using a tiled convolution approach?

    <p>Maximizing data reuse within tiles to minimize global memory accesses.</p> Signup and view all the answers

    In the context of atomic operations within CUDA, what is a common issue that atomicity can help resolve?

    <p>Race conditions occurring among multiple threads.</p> Signup and view all the answers

    What is a common performance consideration when implementing a basic reduction kernel?

    <p>Avoiding thread divergence in the reduction process.</p> Signup and view all the answers

    What does the term 'data movement API' typically refer to in the context of GPU architecture?

    <p>Protocols for transferring data between CPU and GPU.</p> Signup and view all the answers

    Which of the following best describes the purpose of the histogram example using atomics?

    <p>To illustrate counting occurrences while ensuring data integrity.</p> Signup and view all the answers

    Study Notes

    Course Introduction and Overview

    • The course aims to teach students how to program heterogeneous parallel computing systems. The main focus is on high performance, energy-efficiency, functionality, maintainability, scalability and portability across vendor devices.
    • The course covers parallel programming APIs tools and techniques, principles and patterns of parallel algorithms, processor architecture features and constraints.

    People

    • Professors and Instructors include: Wen-mei Hwu, David Kirk, Joe Bungo, Mark Ebersole, Abdul Dakkak, Izzat El Hajj, Andy Schuh, John Stratton, Issac Gelado, John Stone, Javier Cabezas and Michael Garland.

    Course Content

    • The course covers the following Modules:
      • Module 1: Introduction to Heterogeneous Parallel Computing, CUDA C vs.CUDA Libs vs.Unified Memory, Pinned Host Memory
      • Module 2: Memory Allocation and Data Movement API Functions, Introduction to CUDA C, Kernel-Based SPMD Parallel Programming
      • Module 3: Multidimensional Kernel Configuration, CUDA Parallelism Model, CUDA Memories, Tiled Matrix Multiplication
      • Module 4: Handling Boundary Conditions in Tiling, Tiled Kernel for Arbitrary Matrix Dimensions, Histogram (Sort) Example
      • Module 5: Basic Matrix-Matrix Multiplication Example, Thread Scheduling, Control Divergence
      • Module 6: DRAM Bandwidth, Memory Coalescing in CUDA
      • Module 7: Atomic Operations
      • Module 8: Convolution, Tiled Convolution, 2D Tiled Convolution Kernel
      • Module 9: Tiled Convolution Analysis, Data Reuse in Tiled Convolution
      • Module 10: Reduction, Basic Reduction Kernel, Improved Reduction Kernel, Scan (Parallel Prefix Sum)
      • Module 11: Work-Inefficient Parallel Scan Kernel, Work-Efficient Parallel Scan Kernel, More on Parallel Scan
      • Module 12: Scan Applications: Per-thread Output Variable Allocation, Scan Applications: Radix Sort, Performance Considerations (Histogram (Atomics) Example), Performance Considerations (Histogram (Scan) Example), Advanced CUDA Memory Model
      • Module 13: Constant Memory, Texture Memory
      • Module 14: Floating Point Precision Considerations, Numerical Stability
      • Module 15: GPU as part of the PC Architecture
      • Module 16: Data Movement API vs.GPU Teaching Kit, Accelerated Computing
      • Module 17: Application Case Study: Advanced MRI Reconstruction
      • Module 18: Application Case Study: Electrostatic Potential Calculation (part 1), Electrostatic Potential Calculation (part 2)
      • Module 19: Computational Thinking for Parallel Programming, Joint MPI-CUDA Programming
      • Module 20: Joint MPI-CUDA Programming (Vector Addition - Main Function), Joint MPI-CUDA Programming (Message Passing and Barrier), Joint MPI-CUDA Programming (Data Server and Compute Processes), Joint MPI-CUDA Programming (Adding CUDA), Joint MPI-CUDA Programming (Halo Data Exchange)
      • Module 21: CUDA Python Using Numba
      • Module 22: OpenCL Data Parallelism Model, OpenCL Device Architecture, OpenCL Host Code (Part 1), OpenCL Host Code (Part 2)
      • Module 23: Introduction to OpenACC, OpenACC Subtleties
      • Module 24: OpenGLand CUDA Interoperability
      • Module 25: Effective use of Dynamic Parallelism, Advanced Architectural Features: Hyper-Q
      • Module 26: Multi-GPU
      • Module 27: Example Applications Using Libraries: CUBLAS, CUFFT, CUSOLVER
      • Module 28: Advanced Thrust
      • Module 29: Other GPU Development Platforms: QwickLABS, Where to Find Support

    GPU Teaching Kit

    • The GPU Teaching Kit is licensed by NVIDIA and the University of Illinois under the Creative Commons Attribution-NonCommercial 4.0 International License.

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    Description

    This quiz covers the foundational concepts of heterogeneous parallel computing. Students will explore programming APIs, parallel algorithms, and the architectural features relevant to high-performance and energy-efficient computing systems. Module-specific principles and tools will also be examined.

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