CUDA 100 Days Challenge

Challenge Progress

Days Completed: 75/100

Started on January 1st, 2024 with a commitment to learn GPU programming fundamentals and advance to complex optimizations. Each day includes hands-on coding, kernel optimization, and performance analysis.

Challenge Overview

The CUDA 100 Days Challenge is an intensive learning journey focused on mastering GPU programming through daily practice and progressively complex projects. This challenge covers everything from basic CUDA kernel development to advanced optimization techniques used in high-performance computing.

Each day includes practical coding exercises, performance benchmarking, and deep dives into GPU architecture. The challenge progresses from simple parallel algorithms to complex applications in machine learning, scientific computing, and graphics processing.

Learning Topics Covered

Days 1-20: Fundamentals

CUDA basics, kernel functions, thread organization, memory types, and simple parallel algorithms.

Days 21-40: Memory Management

Global, shared, constant memory optimization, memory coalescing, and bandwidth optimization.

Days 41-60: Advanced Patterns

Reduction operations, scan algorithms, dynamic parallelism, and warp-level primitives.

Days 61-80: Optimization

Performance profiling, occupancy optimization, instruction throughput, and memory hierarchy.

Days 81-100: Applications

Machine learning kernels, image processing, numerical simulations, and real-world projects.

Sample CUDA Code

Here's an example from Day 45 - Optimized Matrix Multiplication:

__global__ void matrixMulShared(float* A, float* B, float* C, 
                                   int width) {
    __shared__ float sharedA[TILE_SIZE][TILE_SIZE];
    __shared__ float sharedB[TILE_SIZE][TILE_SIZE];
    
    int bx = blockIdx.x, by = blockIdx.y;
    int tx = threadIdx.x, ty = threadIdx.y;
    
    int row = by * TILE_SIZE + ty;
    int col = bx * TILE_SIZE + tx;
    
    float sum = 0.0f;
    
    for (int tile = 0; tile < (width + TILE_SIZE - 1) / TILE_SIZE; ++tile) {
        // Load tiles into shared memory
        if (row < width && tile * TILE_SIZE + tx < width)
            sharedA[ty][tx] = A[row * width + tile * TILE_SIZE + tx];
        else
            sharedA[ty][tx] = 0.0f;
            
        if (col < width && tile * TILE_SIZE + ty < width)
            sharedB[ty][tx] = B[(tile * TILE_SIZE + ty) * width + col];
        else
            sharedB[ty][tx] = 0.0f;
            
        __syncthreads();
        
        // Compute partial results
        for (int k = 0; k < TILE_SIZE; ++k) {
            sum += sharedA[ty][k] * sharedB[k][tx];
        }
        
        __syncthreads();
    }
    
    if (row < width && col < width) {
        C[row * width + col] = sum;
    }
}

Performance Achievements

Matrix Multiplication

50x speedup over CPU implementation using shared memory and tiling.

Image Convolution

30x speedup for 2D convolution operations with optimized memory access.

Vector Operations

100x speedup for large vector additions and dot products.

Neural Network Training

25x speedup for forward and backward propagation in deep networks.

Daily Learning Structure

Each Day Includes:

Theory (30 min): Study GPU architecture and CUDA concepts
Coding (60 min): Implement kernels and optimization techniques
Benchmarking (15 min): Profile performance and analyze results
Documentation (15 min): Record learnings and code insights

// Daily practice example - Day 32: Reduction Operations
__global__ void reduceSum(float* input, float* output, int n) {
    extern __shared__ float sdata[];
    
    unsigned int tid = threadIdx.x;
    unsigned int i = blockIdx.x * blockDim.x + threadIdx.x;
    
    // Load data into shared memory
    sdata[tid] = (i < n) ? input[i] : 0;
    __syncthreads();
    
    // Perform reduction in shared memory
    for (unsigned int s = blockDim.x / 2; s > 0; s >>= 1) {
        if (tid < s) {
            sdata[tid] += sdata[tid + s];
        }
        __syncthreads();
    }
    
    // Write result for this block to global memory
    if (tid == 0) output[blockIdx.x] = sdata[0];
}

Development Environment

Hardware & Software Stack:

GPU: NVIDIA RTX 3080 (8704 CUDA cores)
CUDA Toolkit: Version 12.0 with latest drivers
Compiler: nvcc with C++17 support
Profiling: Nsight Compute and nvprof
IDE: Visual Studio Code with CUDA extensions
Version Control: Git with daily commits

Key Learnings & Insights

Memory Coalescing: Proper memory access patterns can improve performance by 5-10x
Occupancy Optimization: Balancing threads per block with register usage is crucial
Shared Memory: Effective use of shared memory can eliminate global memory bottlenecks
Warp Divergence: Understanding execution models prevents performance degradation
Algorithmic Thinking: GPU algorithms require different approaches than CPU algorithms
Profiling First: Always profile before optimizing to identify real bottlenecks

Learning Resources

Recommended Resources:

NVIDIA CUDA Documentation: Official programming guide and best practices
"Professional CUDA C Programming": Comprehensive book on CUDA development
GPU Computing Gems: Advanced optimization techniques and case studies
NVIDIA Developer Blog: Latest updates and optimization tips
Stack Overflow CUDA Tag: Community support and problem solving