100DaysCUDA

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Learning cuda in a 100 days

100DaysCUDA 최신버젼 다운로드

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# Building Deep Learning in CUDA! 🚀

from [Umar Jamil's challenge](https://github.com/hkproj/100-days-of-gpu)

## Goal

Goal is to reproduce some of the results of [The Principles of Deep Learning Theory](https://arxiv.org/abs/2106.10165) using CUDA and benchmark against pytorch on the way.

## Roadmap

| Roadmap | Status |
|---------|--------|
| Vector Addition in CUDA | 🤗 |
| Matrix Addition in CUDA | 🤗 |
| MatMul Level 0: Naive Implementation | 🤗 |
| MatMul Level 1: Memory Coalescing | 🤗 |
| MatMul Level 2: Shared Memory Tiling | 🤗 |
| MatMul Level 3: Loop Unrolling | 🤗  |
| MatMul Level 4: Double Buffering | 🫠 |
| MatMul Level 5: Advanced Block Tiling | 🫠 |
| MatMul Level 6: Warp-level Optimizations | 🫠 |
| MatMul Level 7: Register Tiling & Vectorization | 🫠 |
| MatMul Level 8: Tensor Cores (Optional) | 🫠 |
| MatMul Level 9: Multi-GPU & CUDA Streams | 🫠 |
| MatMul Level 10: Final Optimized Kernel | 🫠 |
| One Layer MLP Forward Pass | 🫠 |
| One Layer MLP Backward Pass | 🫠 |
| Softmax Activation Function | 🫠 |
| SGD Optimizer in CUDA | 🫠 |
| Train on Titanic Dataset | 🫠 |
| VR1 Network Width -> ∞ = Gaussian distribution | 🫠 |

## Side Quests

### CUDA Propr

Propr is a package for differential expression analysis in R. It is based on the log-variance ratio (LRV) method.
The main backend is written in Rcpp, so quite optimized for CPU.

Unfortunately, this is still mega slow, CUDA comes to the rescue as it is an embarrassingly parallel problem.

See Day4 for the implementation and notes for the algorithm.

| Roadmap | Status |
|---------|--------|
| Implement Log Variance Ratio in CUDA | 🤗 |
| Implement Propr in CUDA | 🤗 |
| benchmark both and make sure we get the same results | 🤗 |

## Progress Log

| Day      | Progress & Notes |
|----------|-----------------|
| Day 1    | Implemented vector addition in CUDA. Learned about CUDA thread hierarchy (grids/blocks/threads), memory management (cudaMalloc/cudaMemcpy), and kernel execution. Created a simple vector addition program that adds two arrays of 1024 elements in parallel using 256 threads per block. Focused on proper memory allocation, transfers between host/device, and error handling. |
| Day 2    | Implemented matrix addition in CUDA. Deep dive into memory coalescing, thread synchronization, and grid organization. Learned about memory layout, coalesced access patterns, 2D grid/block organization, CUDA synchronization, and thread execution model. Created a matrix addition program handling 1024x1024 matrices using 16x16 thread blocks. |
| Day 3    | Started matrix multiplication optimization journey with Level 0 (naive implementation). Created comprehensive performance benchmarking infrastructure for measuring kernel execution, memory transfers, GFLOPS, bandwidth and CPU vs GPU comparisons. Created detailed notes on CUDA block size selection, covering hardware constraints, matrix size considerations, memory access patterns and optimization strategies. Implemented naive matrix multiplication with 16x16 thread blocks for 1024x1024 matrices. |
| Day 4    | Implemented Log Variance Ratio in CUDA. Learned about shared memory and about sum reduction. Yields 200x speedup over Rcpp log ratio function !! |
| Day 5    | Implemented MatMul tiling, took a long time to get right, especially vey messy in my dealing with the indices at first, but I feel like I understand the idea now. Benchmarked against naive implementation. |
| Day 6    | Implemented Coalesced access through transposing input matrix at GPU load time. |
| Day 7    | Recap: rewrote naive matmul from scratch (so no llm or internet, just pen&paper). |
| Day 8    | Recap: implemented profiling toolkit. |
| Day 9    | Recap: re-implemented matmul memory coalescing and added debugging tools to understand that memory was indeed coalesced. |
| Day 10   | Recap: re-implemented shared-memory and tiling.|
| Day 11   | Implemented bank conflicts correction but got slower results, so investigating with profiler, see notes for more details. |
| Day 12   | More investigation with profiler, switched 1d blocks to 2d blocks but still saw slower results.  |
| Day 13   | Implemented tensor core matmul for hopper but still getting into compilation errors due to nvcc, see [here](https://github.com/skypjack/entt/issues/1082) |
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100DaysCUDA

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