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Parallel Computing with CUDA

Course Summary

This introductory course on CUDA shows how to get started with using the CUDA platform and leverage the power of modern NVIDIA GPUs. It covers the basics of CUDA C, explains the architecture of the GPU and presents solutions to some of the common computat


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    Course Syllabus

    ● GPU Architecture Overview
        ◦ Course Outline
        ◦ History of GPU Computation
        ◦ GPGPU Frameworks
        ◦ Graphics Processor Architecture
        ◦ Compute Capability
        ◦ Choosing a Graphics Card
    ● Tools of The Trade
        ◦ Tools Overview
        ◦ Using NSight
        ◦ Running CUDA Apps
        ◦ Debugging
        ◦ Profiling
    ● Introduction to CUDA C
        ◦ Overview
        ◦ Compilation Process
        ◦ Hello, CUDA
        ◦ Location Qualifiers
        ◦ Execution Model
        ◦ Grid and Block Dimensions
        ◦ Error Handling
        ◦ Device Introspection
    ● Parallel Programming Patterns
        ◦ Overview
        ◦ Element Addressing
        ◦ Map
        ◦ Gather
        ◦ Scatter
        ◦ Reduce
        ◦ Scan
    ● The Many Types of Memory
        ◦ Overview
        ◦ Global Memory
        ◦ Constant & Texture Memory
        ◦ Shared Memory
        ◦ Register & Local Memory
        ◦ Summary
    ● Thread Cooperation and Synchronization
        ◦ Overview
        ◦ Barrier Synchronization
        ◦ Thread Synchronization Demo
        ◦ Warp Divergence
        ◦ Summary
    ● Atomic Operations
        ◦ Overview
        ◦ Why Atomics?
        ◦ Atomic Functions
        ◦ Atomic Sum
        ◦ Monte Carlo Pi
        ◦ Summary
    ● Events and Streams
        ◦ Overview
        ◦ Events
        ◦ Event API
        ◦ Event example
        ◦ Pinned memory
        ◦ Streams
        ◦ Stream API
        ◦ Example (single stream)
        ◦ Example (multiple streams)
        ◦ Summary
    ● CUDA in Advanced Scenarios
        ◦ Overview
        ◦ Inline PTX
        ◦ Device API
        ◦ Pinned Memory
        ◦ Multi-GPU Programming
        ◦ Thrust
        ◦ Summary

     


Course Fee:
USD 29

Course Type:

Self-Study

Course Status:

Active

Workload:

1 - 4 hours / week

This course is listed under Development & Implementations and IT Security & Architecture Community

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