πŸ‘¨β€πŸŽ“
Somesh Fengade
  • ME
    • πŸ‘‹About
    • πŸ“šPersonal Projects
      • πŸŒ„AI meditations
      • πŸ€–GPT-Compare
      • πŸŒ™Learning to see in the dark
      • πŸ’ΈLending Club analysis
      • ⏬Moodle Downloader
      • πŸ“šAnalyzing the complexity of literary passages.
      • πŸ§ͺUnit test analysis
      • πŸ•Food Vision
      • πŸ“œSkim Lit project
      • πŸš‹Driver's attention detection
      • ⏳React Countdown Timer app
  • Achievements
    • πŸŽ“Achievements directory
      • 🐣Kaggle Expert (notebook)
      • ❗Amazon Machine learning competition (top 100)
      • 🐢PetFinder.my - Pawpularity Contest ( top 20%)
      • 🐳Happywhale - Whale and Dolphin Identification (top 20%)
      • πŸ–ŠοΈNBME - Score Clinical Patient Notes ( top 50%)
      • πŸ•ΈοΈTensorflow Barrier Riff challenge(top 42%)
  • Notes and courses
    • πŸ‚Foundations of LLMs Lesson 1
    • GPT from scratch
    • Introduction to CUDA Python with Numba
      • Custom CUDA kernels in python with Numba
      • Multidimensional grids and shared memory for cuda python with Numba
Powered by GitBook
On this page
  • Content covered
  • Outcome:
  1. Notes and courses
  2. Introduction to CUDA Python with Numba

Multidimensional grids and shared memory for cuda python with Numba

Content covered

  • utiliizng the on chip memory space on gpu called shared memory

Outcome:

  • writing the GPU accelerated code in python using numba on 1D and 2D datasets while utilizing the several of the most important strategies for writing consistently fast GPU accelerated code.

PreviousCustom CUDA kernels in python with Numba

Last updated 2 years ago