https://github.com/flosmume/cpp-cuda-deepvision-rtx-starter
CUDA C++ practice project for RTX 4070 SUPER — explore GPU concurrency, pinned memory, and Nsight profiling. Includes SAXPY and 2D blur kernels to train optimization, stream overlap, and timing analysis for NVIDIA Developer Technology Engineering skillset.
https://github.com/flosmume/cpp-cuda-deepvision-rtx-starter
cpp cuda cuda-kernels cuda-streams deep-learning-inference gpu gpu-optimization gpu-profiling high-performance-computing nsight nvidia parrallel-computing pinned-memory
Last synced: 6 days ago
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CUDA C++ practice project for RTX 4070 SUPER — explore GPU concurrency, pinned memory, and Nsight profiling. Includes SAXPY and 2D blur kernels to train optimization, stream overlap, and timing analysis for NVIDIA Developer Technology Engineering skillset.
- Host: GitHub
- URL: https://github.com/flosmume/cpp-cuda-deepvision-rtx-starter
- Owner: FlosMume
- Created: 2025-10-29T03:43:40.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-10-29T08:05:50.000Z (7 months ago)
- Last Synced: 2025-10-29T10:12:29.852Z (7 months ago)
- Topics: cpp, cuda, cuda-kernels, cuda-streams, deep-learning-inference, gpu, gpu-optimization, gpu-profiling, high-performance-computing, nsight, nvidia, parrallel-computing, pinned-memory
- Language: C++
- Homepage:
- Size: 3.91 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# DeepVision-RTX Starter
This is an enhanced README combining:
- GitHub-friendly formatting
- Technical deep dive
- ASCII diagrams
- Rendered PNG diagrams
- Profiling workflow
- Architecture explanations
- GPU hardware reasoning
## Architecture Diagram (SVG)
## Streams Overlap Diagram (SVG)
## ASCII Architecture Diagram
```
+-------------------+ PCIe / DMA +---------------------+
| CPU Host | -----------------> | GPU Global |
| (Pinned Memory) | <----------------- | Memory |
+-------------------+ +---------------------+
| |
| Launch Kernels |
v v
+--------------+ +------------------+
| CUDA Driver | | SMs (Ada 8.9) |
| Runtime API | | Warps / Threads |
+--------------+ +------------------+
```
## ASCII Streams Overlap Diagram
```
Time →
----------------------------------------------------------------------
H2D Stream 0: [========== copy =========]
H2D Stream 1: [========== copy =========]
Compute Stream 2: [==== kernel ====]
D2H Stream 0: [==== copy ====]
----------------------------------------------------------------------
```
## Build Instructions
```bash
cmake -S . -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build -j
./build/deepvision_rtx
```
## Profiling Instructions (Nsight Systems & Nsight Compute)
```bash
nsys profile -o nsys_report ./build/deepvision_rtx
ncu --set full ./build/deepvision_rtx
```
## Project Structure
```
src/
main.cpp # Entry point
main.cu # Separate experimental demo
conv_kernels.cu # SAXPY + blur3x3 kernels
conv_kernels.cuh # Kernel declarations
utils/
check_cuda.hpp # Error-checking utilities
```
## GPU Architecture Notes (RTX 4070 SUPER - Ada 8.9)
- SM count: 46
- Warp size: 32
- Max threads/block: 1024
- Memory bandwidth: 504 GB/s
- Concurrent copy/compute supported
- Best performance achieved when:
- You use pinned memory
- H2D and D2H overlap with compute
- Kernels maintain good occupancy
## Roadmap
- Add shared-memory tiled blur
- Add constant-memory kernel variants
- Add half-precision path (FP16)
- Add Tensor Core WMMA version
- Add occupancy analysis + roofline plot
- Add Nsight Compute performance tables
- Add multi-kernel pipelines
- Compare against cuDNN for 3×3 conv
---