12.2 CUDA Thread Hierarchy and Memory Model

CUDA's thread hierarchy and memory model are crucial for efficient GPU programming. Threads, blocks, and grids form a structured approach to parallel computation, allowing developers to map problems to GPU architecture effectively.

Understanding CUDA's memory types is key to optimizing performance. Global, shared, local, constant, and texture memory each serve specific purposes, enabling developers to fine-tune memory access patterns and maximize GPU utilization.

CUDA Thread Hierarchy

Thread Hierarchy Components

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• CUDA's thread hierarchy consists of three levels organized in a hierarchical structure
  • Threads form the smallest unit of execution in CUDA
  • Blocks group threads together
  • Grids collect blocks to form the highest level
• Threads run single instances of kernel functions concurrently
• Blocks allow threads to cooperate and share resources (shared memory)
• Threads within a block can synchronize using barriers
• Grids are created by a single kernel launch
• Thread, block, and grid dimensions specified in up to 3 dimensions (x, y, z)
  • Allows flexible mapping of computational problems to GPU architecture
• CUDA runtime automatically schedules blocks for execution on streaming multiprocessors (SMs)

Hierarchy Relationships and Significance

• Threads within a block can communicate via shared memory and synchronization
• Blocks are independent and can execute in any order
• Grid launches many blocks to solve large computational problems
• Understanding thread hierarchy crucial for:
  • Efficient parallel algorithm design
  • Proper work distribution across GPU
  • Optimizing memory access patterns
• Examples of hierarchy usage:
  • Image processing: each thread processes a pixel, block covers image tile
  • Matrix multiplication: each thread computes one element, block handles submatrix

CUDA Memory Types

Global and Shared Memory

• CUDA provides several memory types with different characteristics and uses
• Global memory
  • Largest and slowest memory type
  • Accessible by all threads across all blocks
  • Persists for entire application lifetime
  • Used for large datasets and communication between blocks
• Shared memory
  • Fast, on-chip memory shared within a block
  • Much lower latency and higher bandwidth than global memory
  • Used for inter-thread communication and data caching
  • Example: storing frequently accessed data for a block's computation

Local, Constant, and Texture Memory

• Local memory
  • Private to each thread
  • Used for automatic variables not fitting in registers
  • Has same performance characteristics as global memory
  • Example: large arrays in thread-specific calculations
• Constant memory
  • Read-only memory, cached and optimized for broadcast access
  • Useful for storing unchanging parameters used by all threads
  • Example: coefficients in a convolution kernel
• Texture memory
  • Optimized for 2D spatial locality
  • Provides hardware filtering for certain data types
  • Beneficial for image processing and graphics applications
  • Example: storing and sampling from image textures

Memory Hierarchy Optimization

Global and Shared Memory Optimization

• Coalesced memory access patterns maximize global memory bandwidth
  • Threads within a warp access contiguous memory locations
  • Example: Accessing adjacent array elements in parallel
• Shared memory serves as software-managed cache
  • Reduces global memory accesses in data-parallel algorithms
  • Example: Tiled matrix multiplication algorithm
• Minimize host-device memory transfers
  • Keep data on GPU as long as possible
  • Use asynchronous memory transfers when appropriate
  • Example: Performing multiple kernel operations on same dataset without transferring back to host

Specialized Memory Optimizations

• Constant memory improves performance for frequently accessed read-only data
  • Example: Lookup tables used by all threads
• Texture memory benefits algorithms with 2D spatial locality
  • Example: Image filtering operations
• Optimize register usage and occupancy to maximize GPU utilization
  • Balance between registers per thread and number of active threads
• Avoid shared memory bank conflicts to prevent access serialization
  • Example: Using padding to avoid conflicts in matrix transposition

CUDA Kernel Implementation

Kernel Definition and Launch

• CUDA kernels defined using global function qualifier
• Launched with specific grid and block configuration using <<<>>> syntax
  • Example:

    myKernel<<<gridSize, blockSize>>>(args);

• Thread indices and dimensions accessed within kernels via built-in variables
  • threadIdx, blockIdx, blockDim, gridDim
  • Example: Calculating global thread ID:

    int tid = blockIdx.x * blockDim.x + threadIdx.x;

Memory Management and Synchronization

• Shared memory declared using shared qualifier
  • Can be statically or dynamically allocated
  • Example:

    __shared__ float sharedData[256];

• Block-level thread synchronization achieved using __syncthreads()
  • Ensures all threads reach a certain point before proceeding
• Memory fence functions (e.g., __threadfence()) enforce memory ordering
  • Used when accessing global memory across multiple threads
• Atomic operations (e.g., atomicAdd()) update shared memory locations concurrently
  • Example: Parallel reduction sum using atomicAdd

Efficient Kernel Design

• Divide problems into independent sub-problems solvable by different blocks
  • Further parallelize within each block using threads
• Balance workload across threads and blocks to maximize GPU utilization
• Minimize divergent execution paths within warps
  • Example: Using shared memory to avoid divergent global memory accesses
• Optimize memory access patterns for coalescing and efficient use of cache
  • Example: Tiling algorithms for matrix operations