12.3 CUDA Kernel Optimization Techniques

CUDA kernel optimization techniques are crucial for maximizing GPU performance. These methods focus on efficient thread management, memory access patterns, and data transfer strategies. By applying these techniques, developers can significantly boost the speed and efficiency of their CUDA programs.

Understanding and implementing these optimization techniques is essential for harnessing the full power of GPU computing. From thread coarsening to shared memory usage, these strategies enable programmers to fine-tune their kernels for optimal performance across various GPU architectures.

Optimizing CUDA Kernels

Thread and Memory Optimization

Top images from around the web for Thread and Memory Optimization

• 

  CUDA - Wikipedia, the free encyclopedia View original

  Is this image relevant?

• 

  [MPC/CUDA] CUDA Kernel View original

  Is this image relevant?

• 

  GPU计算 -- GPU体系结构及CUDA编程模型 View original

  Is this image relevant?

• 

  CUDA - Wikipedia, the free encyclopedia View original

  Is this image relevant?

• 

  [MPC/CUDA] CUDA Kernel View original

  Is this image relevant?

1 of 3

Top images from around the web for Thread and Memory Optimization

• 

  CUDA - Wikipedia, the free encyclopedia View original

  Is this image relevant?

• 

  [MPC/CUDA] CUDA Kernel View original

  Is this image relevant?

• 

  GPU计算 -- GPU体系结构及CUDA编程模型 View original

  Is this image relevant?

• 

  CUDA - Wikipedia, the free encyclopedia View original

  Is this image relevant?

• 

  [MPC/CUDA] CUDA Kernel View original

  Is this image relevant?

1 of 3

• Thread coarsening combines work of multiple threads into a single thread reducing overhead and increasing arithmetic intensity
• Loop unrolling reduces branch penalties and increases instruction-level parallelism by executing multiple iterations simultaneously
• Memory coalescing organizes global memory accesses maximizing bandwidth utilization by ensuring adjacent threads access contiguous memory locations
• Occupancy optimization balances number of active threads with available resources maximizing GPU utilization
  • Adjust thread block size and register usage to achieve optimal occupancy
  • Use shared memory judiciously to avoid limiting occupancy
• Instruction-level optimizations significantly improve kernel performance
  • Use intrinsic functions for faster math operations (sin, cos, exp)
  • Avoid thread divergence within warps by minimizing conditional statements
  • Utilize fast math options when precision requirements allow

Profiling and Performance Analysis

• Profiling tools identify performance bottlenecks and guide optimization efforts
  • NVIDIA Visual Profiler provides detailed kernel analysis and optimization suggestions
  • NVIDIA Nsight Compute offers advanced profiling capabilities for modern GPU architectures
• Analyze kernel execution time, memory bandwidth utilization, and compute utilization
• Identify and address memory access patterns causing poor performance
• Experiment with different optimization techniques and measure their impact
• Iteratively refine kernels based on profiling results to achieve optimal performance

Minimizing CPU-GPU Communication

Efficient Data Transfer Strategies

• Data transfer between CPU and GPU bottlenecks CUDA applications due to limited PCIe bandwidth
• Asynchronous data transfers using CUDA streams overlap computation with data movement hiding transfer latency
  • Implement multiple streams to concurrently transfer data and execute kernels
  • Use

    [cudaMemcpyAsync()](https://www.fiveableKeyTerm:cudamemcpyasync())

    for asynchronous memory transfers
• Pinned (page-locked) memory allocation improves transfer speeds preventing memory from being swapped out to disk
  • Allocate pinned memory using

    cudaMallocHost()

    or

    cudaHostAlloc()

  • Be cautious of overuse as it reduces available system memory
• Compression techniques reduce amount of data transferred between CPU and GPU trading computation for bandwidth
  • Implement data compression algorithms (RLE, Huffman coding) before transfer
  • Decompress data on GPU after transfer

Advanced Memory Management

• Unified Memory provides single memory space accessible by both CPU and GPU simplifying memory management and potentially reducing explicit transfers
  • Allocate Unified Memory using

    cudaMallocManaged()

  • Let CUDA runtime automatically migrate data between CPU and GPU
• Kernel fusion combines multiple small kernels into single larger kernel reducing number of separate GPU invocations and associated data transfers
  • Identify kernels with data dependencies and merge them when possible
  • Balance kernel fusion with occupancy and resource utilization
• Zero-copy memory allows GPU to directly access host memory beneficial for certain access patterns and small data sizes
  • Allocate zero-copy memory using

    cudaHostAlloc()

    with

    cudaHostAllocMapped

    flag
  • Use sparingly as it can lead to lower performance due to PCIe transfer overhead

Shared Memory for Data Reuse

Shared Memory Fundamentals

• Shared memory fast on-chip memory accessed by all threads within thread block
• Proper use of shared memory significantly reduces global memory bandwidth requirements and improves kernel performance
• Tiling techniques involve loading data into shared memory in tiles allowing threads to cooperatively load and process data
  • Implement 2D tiling for matrix operations (matrix multiplication)
  • Use 1D tiling for vector operations (convolution)
• Size of shared memory per block limited and balanced against number of thread blocks scheduled concurrently
  • Typical shared memory size ranges from 48KB to 96KB per SM depending on GPU architecture
  • Adjust shared memory usage to maintain high occupancy

Advanced Shared Memory Techniques

• Bank conflicts in shared memory access limit performance requiring careful consideration of access patterns and padding techniques
  • Avoid bank conflicts by ensuring threads access different banks or same location within a bank
  • Use padding to resolve bank conflicts in multi-dimensional arrays
• Shared memory used as software-managed cache allowing programmers to explicitly control data movement and reuse
  • Implement sliding window algorithms using shared memory for data reuse
  • Utilize shared memory for fast inter-thread communication within a block
• Dynamic shared memory allocation allows size of shared memory to be set at kernel launch time providing flexibility in memory usage
  • Declare dynamic shared memory using

    extern __shared__ type[] array;

  • Set shared memory size at kernel launch using

    <<<gridSize, blockSize, sharedMemSize>>>

Efficient Parallel Reduction and Scan Operations

Parallel Reduction Techniques

• Parallel reduction fundamental operation for computing single result from large array of data (sum, maximum value)
• Efficient CUDA reductions use techniques minimizing divergence and maximizing parallelism
  • Sequential addressing reduces bank conflicts and improves memory coalescing
  • Loop unrolling reduces number of iterations and increases instruction-level parallelism
  • Warp-level primitives (

    __shfl_down_sync()

    ) implement highly efficient reductions within warp without using shared memory
• Hierarchical approaches to reduction handle large datasets exceeding capacity of single thread block
  • Implement two-level reduction: block-level reduction followed by global reduction
  • Use atomic operations for final reduction step across blocks

Parallel Scan Algorithms

• Parallel scan (prefix sum) operation computes cumulative sums fundamental to many parallel algorithms
• Work-efficient scan algorithms achieve $O(n)$ work complexity and $O(\log n)$ step complexity
  • Implement Blelloch's algorithm for efficient parallel scan
  • Use up-sweep and down-sweep phases to compute prefix sum
• Advanced techniques offer trade-offs between work efficiency and step efficiency in parallel scan operations
  • Kogge-Stone algorithm provides better step efficiency at cost of increased work
  • Brent-Kung algorithm offers good balance between work and step efficiency
• Implement hierarchical scan for large datasets
  • Divide data into blocks and perform local scans
  • Compute block-wide prefix sum
  • Combine local and block-wide results for final scan