11.5 GPU computing and CUDA programming

GPU computing and CUDA programming revolutionize high-performance computing by harnessing the parallel processing power of graphics cards. This approach enables massive speedups for tasks like scientific simulations, machine learning, and data analysis by executing thousands of threads simultaneously.

CUDA, NVIDIA's parallel computing platform, provides tools and APIs for developers to tap into GPU capabilities. It allows writing specialized kernels that run on the GPU, managing memory transfers, and optimizing performance through techniques like shared memory usage and coalesced memory access.

GPU Architecture and Programming Model

GPU Structure and Parallel Processing

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• GPU architecture comprises numerous small, specialized cores optimized for parallel processing
  • Contrasts with CPUs having fewer, more versatile cores
  • Enables simultaneous execution of multiple tasks
• SIMD (Single Instruction, Multiple Data) paradigm forms the foundation of GPU computing
  • Allows identical operations on multiple data elements concurrently
  • Enhances efficiency for tasks with high data parallelism (matrix operations, image processing)
• GPU memory hierarchy includes global memory, shared memory, and registers
  • Global memory: largest capacity, slowest access (DRAM)
  • Shared memory: faster access, limited size, shared within thread blocks
  • Registers: fastest access, very limited, private to each thread

Thread Organization and Execution Model

• Thread hierarchy in GPUs involves grids, blocks, and threads
  • Grids: highest level, contain multiple blocks
  • Blocks: intermediate level, contain multiple threads
  • Threads: lowest level, individual units of parallel execution
• Warp execution crucial in GPU computing
  • Warps consist of 32 threads in NVIDIA GPUs
  • Execute instructions in lockstep for efficiency
  • Divergence within a warp can impact performance
• GPU programming models (CUDA for NVIDIA) provide abstractions for parallel execution
  • Manage thread organization and memory access patterns
  • Simplify development of parallel algorithms
• Host (CPU) and device (GPU) code execution differ significantly
  • Host code manages overall program flow and GPU resource allocation
  • Device code runs on GPU, focuses on parallel computations

CUDA Programming for Parallel Algorithms

CUDA C/C++ Fundamentals

• CUDA C/C++ extends standard C/C++ with GPU-specific elements
  • Keywords (e.g.,

    __global__

    ,

    __device__

    )
  • Functions for GPU operations (e.g.,

    cudaMalloc()

    ,

    cudaFree()

    )
• Kernel functions in CUDA defined using special syntax
  • Executed in parallel on GPU
  • Example:

    __global__ void kernelFunction(int* data) { ... }

• Thread indexing utilizes built-in variables

  • blockIdx

    : identifies the current block

  • threadIdx

    : identifies the thread within a block
  • Enables unique identification of each thread in the grid
• Memory allocation and data transfer managed via CUDA API

  • cudaMalloc()

    : allocates memory on GPU

  • cudaMemcpy()

    : transfers data between host and device
  • Example:

    cudaMemcpy(d_data, h_data, size, cudaMemcpyHostToDevice)

Advanced CUDA Programming Techniques

• Shared memory in CUDA kernels enables efficient data sharing
  • Declared using

    __shared__

    keyword
  • Faster than global memory, accessible by all threads in a block
  • Example:

    __shared__ float sharedData[256]

• Synchronization primitives coordinate thread execution

  • __syncthreads()

    : synchronizes all threads within a block
  • Ensures data consistency in shared memory operations
• CUDA streams allow concurrent execution of multiple kernels
  • Enable asynchronous operations for improved performance
  • Example:

    cudaStream_t stream; cudaStreamCreate(&stream)

• Asynchronous memory transfers overlap computation and data movement
  • Utilizes

    cudaMemcpyAsync()

    for non-blocking transfers
  • Improves overall program efficiency

Optimizing GPU Program Performance

Memory Access and Resource Utilization

• Coalesced memory access patterns maximize global memory bandwidth
  • Adjacent threads access adjacent memory locations
  • Improves memory throughput significantly
• Shared memory usage reduces global memory accesses
  • Caches frequently accessed data within thread blocks
  • Example: Tiled matrix multiplication algorithm
• Occupancy optimization balances resource usage
  • Adjusts thread block size and register usage
  • Aims to maximize GPU utilization
  • Tools like CUDA Occupancy Calculator assist in optimization
• Warp divergence minimization avoids performance penalties
  • Reduces branching within warps
  • Techniques include loop splitting and branch elimination

Advanced Optimization Strategies

• Loop unrolling and instruction-level parallelism improve efficiency
  • Reduces loop overhead and increases instruction throughput
  • Example: Manually unrolling small, fixed-size loops in kernels
• Asynchronous operations overlap computation and data movement
  • Utilize CUDA streams for concurrent kernel execution
  • Implement double buffering for continuous data processing
• CUDA events and profiling tools identify performance bottlenecks

  • cudaEvent_t

    for timing specific operations
  • NVIDIA Visual Profiler for comprehensive performance analysis
• Algorithmic optimizations tailored for GPU architecture
  • Redesign algorithms to exploit massive parallelism
  • Example: Parallel reduction for sum or max operations

Performance and Scalability of GPU-Accelerated Applications

Performance Analysis and Metrics

• GPU performance metrics include execution time, throughput, and hardware counters
  • Execution time: overall kernel runtime
  • Throughput: operations per second (FLOPS, IOPS)
  • Hardware counters: cache hits, memory transactions, warp efficiency
• Amdahl's Law implications for parallel speedup
  • Defines theoretical speedup limits based on parallelizable portion
  • Formula: $S(n) = \frac{1}{(1-p) + \frac{p}{n}}$, where $p$ is parallel portion and $n$ is number of processors
• Profiling tools provide detailed performance data
  • NVIDIA Visual Profiler: comprehensive GPU performance analysis
  • Nsight Systems: system-wide performance optimization
  • Reveal bottlenecks, memory transfer inefficiencies, kernel launch overheads

Scalability and Performance Optimization

• Scalability analysis examines performance across problem sizes and GPU configurations
  • Strong scaling: fixed problem size, increasing resources
  • Weak scaling: increasing problem size proportional to resources
• Memory bandwidth and computational intensity influence performance
  • Roofline model: visualizes performance limits based on memory and compute bounds
  • Helps identify whether an application is compute-bound or memory-bound
• Load balancing critical for optimal performance in heterogeneous systems
  • Between CPU and GPU: distribute workload based on device capabilities
  • Among multiple GPUs: ensure even distribution of tasks
  • Dynamic load balancing algorithms adapt to runtime conditions
• Performance modeling predicts application behavior
  • Analytical models based on algorithm complexity and hardware specifications
  • Machine learning approaches for complex, data-dependent performance patterns
  • Guides optimization efforts and hardware selection for specific applications