Lab

.ipynb_checkpoints/GPU-HW CUDA Programming-checkpoint.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "378a2536-6b82-48ca-9dd4-56365cda8800",
+   "metadata": {},
+   "source": [
+    "# CUDA Programing"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "7fe5a4bb-83a2-44b7-b663-41e6635ba81c",
+   "metadata": {},
+   "source": [
+    "CUDA Programming (Numba) Lab Ref: https://nyu-cds.github.io/python-numba/05-cuda/ "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/GPU-Lab Kernel-checkpoint.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8d4a6a3-aeb6-4cb4-a61d-8bb52f4849dc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

GPU-HW CUDA Programming.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "378a2536-6b82-48ca-9dd4-56365cda8800",
+   "metadata": {},
+   "source": [
+    "# CUDA Programing"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "7fe5a4bb-83a2-44b7-b663-41e6635ba81c",
+   "metadata": {},
+   "source": [
+    "CUDA Programming (Numba) Lab Ref: https://nyu-cds.github.io/python-numba/05-cuda/ "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

GPU-Lab Kernel.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7efd9f5e-2046-4727-b611-fefdd2f05238",
+   "metadata": {},
+   "source": [
+    "# Your First GPU Kernel"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "8d1a297e-6e49-4cbc-9142-442dc60f6598",
+   "metadata": {},
+   "source": [
+    "ref: https://carpentries-incubator.github.io/lesson-gpu-programming/first_program.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d8d4a6a3-aeb6-4cb4-a61d-8bb52f4849dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def vector_add(A, B, C, size):\n",
+    "    for item in range(0, size):\n",
+    "        C[item] = A[item] + B[item]\n",
+    "    \n",
+    "    return C"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "54efe984-e538-47bf-a3ad-5fc808f7b1c7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}