13.3 GPU Computing and CUDA Programming

GPUs revolutionize scientific computing with their parallel processing power. Their architecture, featuring Streaming Multiprocessors and a complex memory hierarchy, enables efficient handling of massive datasets and computations. The CUDA programming model harnesses this power, allowing scientists to accelerate their work.

Optimizing GPU kernels is crucial for peak performance. Techniques like memory access optimization, thread divergence reduction, and occupancy optimization squeeze out every bit of computational power. Performance analysis tools help identify bottlenecks, guiding further improvements in GPU-accelerated scientific applications.

GPU Architecture and Programming Model

Architecture of GPUs for scientific computing

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• GPU architecture comprises Streaming Multiprocessors (SMs) containing numerous CUDA cores for parallel processing
• Memory hierarchy includes global memory (large, high-latency), shared memory (fast, limited size), registers (fastest, per-thread), constant memory (read-only, cached), and texture memory (optimized for 2D/3D data)
• SIMT (Single Instruction, Multiple Thread) execution model enables efficient parallel processing of data
• Thread hierarchy organizes computations into threads (smallest unit), warps (32 threads), blocks (grouped threads), and grids (multiple blocks)
• Memory coalescing optimizes global memory access by combining multiple memory requests into a single transaction
• Warp divergence occurs when threads within a warp take different execution paths, reducing performance

CUDA programming for GPU systems

• CUDA programming model separates host (CPU) and device (GPU) code
• Kernel functions define parallel computations executed on the GPU
• Memory management involves cudaMalloc() for allocation, cudaMemcpy() for data transfer, and cudaFree() for deallocation
• Thread indexing uses blockIdx, threadIdx, and blockDim to identify individual threads
• Synchronization with __syncthreads() ensures all threads in a block reach the same point before proceeding
• Error handling employs cudaGetLastError() to retrieve errors and cudaGetErrorString() for error descriptions

Optimization of GPU kernels

• Memory access optimization focuses on coalesced global memory access, efficient shared memory usage, and minimizing bank conflicts
• Thread divergence reduction involves minimizing conditional statements and applying loop unrolling techniques
• Occupancy optimization balances block size, register usage, and shared memory allocation for maximum GPU utilization
• Asynchronous operations use streams to overlap computation and data transfer
• Atomic operations ensure correct results when multiple threads access shared data simultaneously
• Warp-level primitives enhance performance for specific parallel patterns

Performance analysis of GPU programs

• Profiling tools (NVIDIA Visual Profiler, nvprof) provide detailed performance insights
• Performance metrics include execution time, throughput, and memory bandwidth
• Amdahl's Law quantifies potential speedup from GPU acceleration
• Strong scaling assesses performance with fixed problem size and varying resources, weak scaling with fixed problem size per resource
• Roofline model visualizes performance limits based on compute and memory bandwidth
• Performance bottlenecks identified as compute-bound or memory-bound kernels
• Load balancing techniques distribute work evenly across GPU resources
• Multi-GPU programming involves data partitioning and efficient inter-GPU communication