diff --git a/Examples/cuda_sum_vec.cu b/Examples/cuda_sum_vec.cu
new file mode 100644
index 0000000000000000000000000000000000000000..a5dc39eb821af6f43e6fed08ec8ec6770e16d201
--- /dev/null
+++ b/Examples/cuda_sum_vec.cu
@@ -0,0 +1,83 @@
+#include <iostream>
+#include <vector>
+
+constexpr size_t kVecSize = 100000;
+
+__device__ constexpr size_t getIdx() {
+ return blockDim.x * blockIdx.x + threadIdx.x;
+}
+
+__global__ void sumVectors(float *a, float *b, float *c) {
+ size_t idx = getIdx();
+ if (idx < kVecSize) {
+ c[idx] = a[idx] + b[idx];
+ }
+}
+
+int main() {
+ constexpr size_t kBytes = sizeof(float) * kVecSize;
+
+ // Host initialization
+ float *host_vec_a = nullptr;
+ float *host_vec_b = nullptr;
+ float *host_vec_c = nullptr;
+ cudaMallocHost(&host_vec_a, kBytes);
+ cudaMallocHost(&host_vec_b, kBytes);
+ cudaMallocHost(&host_vec_c, kBytes);
+
+ for (size_t i = 0; i < kVecSize; i++) {
+ host_vec_a[i] = static_cast<float>(i);
+ host_vec_b[i] = 2.0 * static_cast<float>(i);
+ }
+
+ // Device initialization
+ float *dev_vec_a = nullptr;
+ float *dev_vec_b = nullptr;
+ float *dev_vec_c = nullptr;
+ cudaMalloc(&dev_vec_a, kBytes);
+ cudaMalloc(&dev_vec_b, kBytes);
+ cudaMalloc(&dev_vec_c, kBytes);
+
+ // Copy host vectors on device
+ cudaMemcpy(dev_vec_a, host_vec_a, kBytes, cudaMemcpyHostToDevice);
+ cudaMemcpy(dev_vec_b, host_vec_b, kBytes, cudaMemcpyHostToDevice);
+
+ // Compute device execution parameters
+ constexpr size_t kThreadPerBlock = 256;
+ constexpr size_t kBlockPerGrid = kVecSize / kThreadPerBlock + ((kVecSize % kThreadPerBlock) != 0 ? 1 : 0);
+
+ // Run kernel
+ sumVectors<<<kBlockPerGrid, kThreadPerBlock>>>(dev_vec_a,
+ dev_vec_b,
+ dev_vec_c);
+
+ // Get back the resulting vector to host
+ cudaMemcpy(host_vec_c, dev_vec_c, kBytes, cudaMemcpyDeviceToHost);
+
+ constexpr float tol = 1e-5;
+ bool is_correct = true;
+ for (size_t i = 0; i < kVecSize; i++) {
+ float expected = 3.0 * static_cast<float>(i);
+ if (std::abs(host_vec_c[i] - expected) > tol) {
+ is_correct = false;
+ break;
+ }
+ }
+ std::cout << "The sum of the two vector is ";
+ if (is_correct) {
+ std::cout << "correct";
+ } else {
+ std::cout << "wrong";
+ }
+ std::cout << std::endl;
+
+ // Host cleanup
+ cudaFreeHost(host_vec_a);
+ cudaFreeHost(host_vec_b);
+ cudaFreeHost(host_vec_c);
+
+ // Device cleanup
+ cudaFree(dev_vec_a);
+ cudaFree(dev_vec_b);
+ cudaFree(dev_vec_c);
+}
\ No newline at end of file
diff --git a/Examples/makefile b/Examples/makefile
index 500779bd58195143e36a4158f42e09425a273956..c613d8094c971be3603ea8f58cd506f16d16d23a 100644
--- a/Examples/makefile
+++ b/Examples/makefile
@@ -5,13 +5,16 @@ FLAGS=--std=c++20 -stdpar=gpu
INCLUDES_WARNINGS:=--diag_suppress useless_using_declaration
-all: artefacts/future
+all: artefacts/future artefacts/cuda_sum_vec
-artefacts/future: artefacts/future.o
+artefacts/%: artefacts/%.o
$(CC) $(FLAGS) $^ -o $@
artefacts/%.o: %.cpp
$(CC) $(FLAGS) $(INCLUDES_WARNINGS) -c $^ -o $@
+artefacts/%.o: %.cu
+ $(CC) $(FLAGS) $(INCLUDES_WARNINGS) -c $^ -o $@
+
clean:
rm -vf artefacts/**
\ No newline at end of file
diff --git a/GPGPU.typ b/GPGPU.typ
new file mode 100644
index 0000000000000000000000000000000000000000..6050f240c09e9c918defc66cde66a0973ab5c9b6
--- /dev/null
+++ b/GPGPU.typ
@@ -0,0 +1,101 @@
+// https://polylux.dev/book/polylux.html
+// https://typst.app/docs
+#import "@preview/polylux:0.3.1": *
+#import themes.metropolis : *
+
+#show: metropolis-theme.with(aspect-ratio: "16-9", footer: "Parallel STL - HPC 2024")
+
+#set text(font: "Fira Sans", weight: "light", size: 20pt)
+#show math.equation: set text(font: "Fira Math")
+
+#title-slide(
+ title: "Algorithmes parallèles STL avec C++ 20",
+ subtitle: "GPGPU",
+ author: "Michaël El Kharroubi",
+ date: "27.02.2024",
+ extra: "HPC 2024 - HEPIA",
+)
+
+#new-section-slide("Introduction")
+
+#slide(
+ title: "Définition",
+)[
+ Le terme "_general-purpose computing on graphics processing units_" (GPGPU)
+ désigne l'ensemble des programmmes qui ne sont pas graphiques et qu'on réalise
+ sur GPU.
+
+ Par exemple :
+ - Simulation de phénomènes physiques
+ - IA et Machine learning
+ - Minage de crypto-monnaie
+]
+
+#slide(
+ title: "Pourquoi a-t-on besoin de GPU?",
+)[
+ Un CPU est conçu pour suivre une série d'instructions séquentiellement (ce qui
+ n'est plus le cas, dans une moindre mesure, avec les instructions vectorielles
+ ex: AVX).
+
+ #box(columns(2, gutter: 22pt)[
+ == Adapté :
+ - Ordonancer des tâches
+ - Accéder aux périphériques
+ - Réseau
+ - Disque
+ - Clavier/souris
+ #colbreak()
+ == Moins adapté :
+ - Calcul vectoriel
+ - Calcul matriciel
+ ])
+
+ Les GPU apportent une réponse à ces problèmes.
+]
+
+#slide(
+ title: "CPU vs GPU",
+)[
+ Un GPU est un processeur vectoriel, son objectif est d'effectuer massivement des
+ calculs en parallèle.
+
+ #figure(
+ image("assets/cpu-gpu.svg", width: 70%),
+ caption: [NVIDIA CUDA Programming Guide version 3.0 - 2010],
+ )
+
+ #link("https://youtu.be/-P28LKWTzrI")[Partenariat Myth Busters & NVidia]
+]
+
+#new-section-slide("La programmation sur GPU")
+
+#slide(
+ title: "CUDA",
+)[
+ CUDA est le kit de développement de NVIDIA pour faire du GPGPU. C'est le kit le
+ plus populaire de développement GPGPU.
+
+ CUDA permet d'écrire du code en C/C++ et de le compiler pour qu'il soit exécuté
+ sur des GPU de NVidia.
+
+ Dans la concurrence, on peut également citer OpenCL (multi-plateforme), ROCm
+ pour AMD ou OneAPI pour Intel.
+]
+
+#slide(title: "Déroulement classique d'un programmme GPGPU")[
+ + Le CPU envoie les données à traiter au GPU #pause
+ + Le GPU applique un kernel sur les données #pause
+ + Le CPU récupère le résultat #pause
+]
+
+#new-section-slide("Exemple de code CUDA et notions à retenir")
+
+#slide(title: "Notions essentielles à retenir")[
+ - Le CPU et le GPU sont deux périphériques distincts
+ - Le GPU est prévu pour effectuer massivement des calculs
+ - La mémoire n'est pas partagée
+ - On parle de *Host* pour le CPU et de *Device* pour le GPU
+]
+
+#new-section-slide("Questions ?")
\ No newline at end of file
diff --git a/assets/.gitkeep b/assets/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/assets/cpu-gpu.svg b/assets/cpu-gpu.svg
new file mode 100644
index 0000000000000000000000000000000000000000..59b428ab1ac07a67c6642c2d6edc4d3b63d81c52
--- /dev/null
+++ b/assets/cpu-gpu.svg
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+ <g
+ transform="matrix(6.25,0,0,-6.25,-1706.3903,4528.6016)"
+ id="g5616">
+ <g
+ clip-path="url(#clipPath5620)"
+ id="g5618">
+ <g
+ id="g5624">
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+ clip-path="url(#clipPath5628)"
+ id="g5626">
+ <text
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+ id="text5632">
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diff --git a/intro.typ b/intro.typ
index 89c053edbffa114c8d60a152239e4266fcc02b39..6e87c2a441a34b67aa22ed9d5953626c53822599 100644
--- a/intro.typ
+++ b/intro.typ
@@ -3,20 +3,17 @@
#import "@preview/polylux:0.3.1": *
#import themes.metropolis : *
-#show: metropolis-theme.with(
- aspect-ratio: "16-9",
- footer: "Parallel STL - HPC 2024"
-)
+#show: metropolis-theme.with(aspect-ratio: "16-9", footer: "Parallel STL - HPC 2024")
#set text(font: "Fira Sans", weight: "light", size: 20pt)
#show math.equation: set text(font: "Fira Math")
#title-slide(
title: "Algorithmes parallèles STL avec C++ 20",
- subtitle: "HPC 2024",
+ subtitle: "Introduction",
author: "Michaël El Kharroubi",
date: "27.02.2024",
- extra: "HEPIA",
+ extra: "HPC 2024 - HEPIA",
)
//#slide(title: "Table des matières")[
@@ -25,38 +22,45 @@
#new-section-slide("Introduction")
-#slide(title: "Objectif du cours")[
-Dans ce cours, nous verrons comment exécuter du code sur GPU de façon moderne avec C++.
+#slide(
+ title: "Objectif du cours",
+)[
+ Dans ce cours, nous verrons comment exécuter du code sur GPU de façon moderne
+ avec C++.
-Nous utiliserons le kit de développement HPC de Nvidia, en particulier le compilateur _nvc++_.
+ Nous utiliserons le kit de développement HPC de Nvidia, en particulier le
+ compilateur _nvc++_.
-Le but de ce cours est de vous donner une courte introduction au calcul générique sur processeur graphique (GPGPU).
+ Le but de ce cours est de vous donner une courte introduction au calcul
+ générique sur processeur graphique (GPGPU).
]
#slide(title: "Contenu du cours")[
- #box(
- columns(1)[
- Dans ce cours nous verrons les concepts suivants :
-
- + Qu'est-ce que le GPGPU, et pour quel usage s'en sert-on?
- + Comment fait-on du GPGPU de manière classique
- + La problématique de la mémoire en GPGPU #pause
- + Les itérateurs C++
- + Les vecteurs, les tableaux et les spans
- + Les captures
- + Les algorithmes STL #pause
- + Présentation du kit HPC Nvidia
- + Le compilateur nvc++
- + Comment paralléliser son code avec nvc++
- + Le futur du paradigme STL avec les mdspans et les views
- ]
- )
+ #box(columns(1)[
+ Dans ce cours nous verrons les concepts suivants :
+
+ + Qu'est-ce que le GPGPU, et pour quel usage s'en sert-on?
+ + Comment fait-on du GPGPU de manière classique
+ + La problématique de la mémoire en GPGPU #pause
+ + Les itérateurs C++
+ + Les vecteurs, les tableaux et les spans
+ + Les captures
+ + Les algorithmes STL #pause
+ + Présentation du kit HPC Nvidia
+ + Le compilateur nvc++
+ + Comment paralléliser son code avec nvc++
+ + Le futur du paradigme STL avec les mdspans et les views
+ ])
]
-#slide(title: "Planning du cours")[
- Le cours sera donné sur une semaine (5 périodes). Nous ferons 2 périodes de théorie et 3 périodes de pratique.
+#slide(
+ title: "Planning du cours",
+)[
+ Le cours sera donné sur une semaine (5 périodes). Nous ferons 2 périodes de
+ théorie et 3 périodes de pratique.
- Pour la partie pratique, nous implémenterons le calcul des équations de la chaleur sur GPU.
+ Pour la partie pratique, nous implémenterons le calcul des équations de la
+ chaleur sur GPU.
]
#new-section-slide("Questions ?")
\ No newline at end of file