Getting Started with the CUDA SDK: A Beginner’s Guide

CUDA SDK Tutorials: Building High-Performance GPU Applications

What this tutorial series covers

• Basics: CUDA architecture, GPU vs CPU, memory types (global, shared, constant), threads, warps, blocks, and grids.
• Development setup: Installing CUDA Toolkit, setting up nvcc, Visual Studio/Make/CMake integration, and using Nsight tools.
• Core programming: Writing kernels, launching kernels, memory allocation and transfers, synchronization, and error checking.
• Performance topics: Memory coalescing, shared memory tiling, avoiding bank conflicts, occupancy tuning, loop unrolling, and instruction-level optimization.
• Advanced techniques: Streams and concurrency, Unified Memory, CUDA Graphs, Tensor Cores, cuBLAS/cuDNN/cuFFT libraries, and multi-GPU programming with NCCL.
• Debugging & profiling: Using cuda-memcheck, Nsight Compute, Nsight Systems, and interpreting profiler reports to find bottlenecks.
• Real-world examples: Matrix multiply (GEMM), convolution for CNNs, reduce/scan primitives, particle simulation, and ray tracing kernels.
• Porting guides: Strategies for migrating CPU code to CUDA, minimizing data transfer, and hybrid CPU–GPU workflows.

Recommended learning path (4 steps)

1. Setup and “Hello, CUDA” kernel — verify environment and run a simple vector add.
2. Memory and execution model — implement and optimize tiled matrix multiply using shared memory.
3. Profiling-driven optimization — profile matrix multiply, fix memory/compute imbalances, retest.
4. Advanced projects — add streams, implement a cuBLAS-backed neural net layer, and scale to multiple GPUs.

Typical code snippet (vector add)

// nvcc compile: nvcc vec_add.cu -o vec_addglobal void vecAdd(const floata, const float *b, float *c, int n) { int i = blockIdx.x * blockDim.x + threadIdx.x; if (i < n) c[i] = a[i] + b[i];}

Tools & libraries to learn

• CUDA Toolkit (nvcc, cuBLAS, cuDNN, cuFFT)
• Nsight Compute / Nsight Systems / cuda-memcheck
• Thrust (C++ parallel algorithms)
• NCCL for multi-GPU communication

Expected outcomes

• Be able to write correct CUDA kernels, optimize memory access patterns, use profiler outputs to drive improvements, and integrate GPU-accelerated libraries into real applications.

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