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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)

Architecture Diagram

Streams Overlap Diagram (SVG)

Streams Timeline

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

cmake -S . -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build -j
./build/deepvision_rtx

Profiling Instructions (Nsight Systems & Nsight Compute)

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