Nvidia CUDA in 100 Seconds

Fireship
7 Mar 202403:12

Summary

TLDRCUDA, a parallel computing platform developed by Nvidia, has transformed the world of data computation by harnessing the power of GPUs. Originally designed for graphics rendering, GPUs are now utilized for their massive parallel processing capabilities, which are ideal for training complex machine learning models. The script explains how developers can write Cuda kernels in C++, manage data transfer between CPU and GPU, and optimize parallel processing for tasks like deep learning. It also teases an upcoming Nvidia GTC conference, highlighting the potential for building large-scale parallel systems with Cuda.

Takeaways

  • 🚀 CUDA is a parallel computing platform developed by Nvidia in 2007, enabling GPU usage beyond gaming.
  • 🌟 It has revolutionized computing by allowing parallel processing of large data blocks, crucial for deep neural networks and AI.
  • 🎮 GPUs are traditionally used for graphics computation, handling matrix multiplication and vector transformations for high-quality gaming visuals.
  • 📈 Modern GPUs, like the RTX 490, have over 16,000 cores, significantly more than a typical CPU like the Intel i9 with 24 cores.
  • 🔍 CUDA allows developers to harness the GPU's parallel processing power, which is widely used by data scientists for training machine learning models.
  • 📝 To develop a CUDA application, one needs an Nvidia GPU and the CUDA toolkit, which includes drivers, runtime, compilers, and development tools.
  • 📋 The code for CUDA is often written in C++, and it involves defining a CUDA kernel function that runs on the GPU.
  • 🔗 Managed memory in CUDA allows data to be accessed by both the host CPU and the device GPU without manual data transfer.
  • 🔧 The main function for the CPU initializes data, passes it to the GPU to run the kernel, and controls the parallel execution configuration.
  • 🔄 CUDA device synchronization ensures that the code waits for the GPU to complete its tasks before proceeding, allowing for data to be copied back to the host machine.
  • 📅 Nvidia's GTC conference is a resource for learning about building massive parallel systems with CUDA, and it is free to attend virtually.

Q & A

  • What is CUDA and what was its original purpose?

    -CUDA, or Compute Unified Device Architecture, is a parallel computing platform developed by Nvidia. It was originally designed to utilize GPUs for more than just playing video games, allowing for parallel computation of large data blocks.

  • When was CUDA developed and by whom?

    -CUDA was developed by Nvidia in 2007, building upon the prior work of Ian Buck and John Nichols.

  • How has CUDA impacted the field of artificial intelligence?

    -CUDA has revolutionized artificial intelligence by enabling the parallel computation of large blocks of data, which is essential for the deep neural networks that drive AI.

  • What is the primary historical use of a GPU?

    -Historically, GPUs (Graphics Processing Units) have been used to compute graphics, such as rendering over 2 million pixels on a screen at high resolutions and frame rates for video games.

  • How do modern GPUs differ from CPUs in terms of core count?

    -Modern GPUs, like the RTX 490, have over 16,000 cores, whereas a modern CPU, such as the Intel i9, typically has around 24 cores. GPUs are designed for parallel processing, while CPUs are designed for versatility.

  • What is a CUDA kernel and how does it work?

    -A CUDA kernel is a function that runs on the GPU. It is written by developers and executed in parallel, allowing for the processing of large amounts of data simultaneously. The CPU initiates the kernel execution, and the GPU performs the computation.

  • How does data transfer between the CPU and GPU work in CUDA?

    -Data is copied from the main RAM to the GPU's memory before the kernel is executed. After computation, the result is copied back to the main memory.

  • What is the significance of the triple brackets in CUDA code?

    -The triple brackets in CUDA code are used to configure the kernel launch, controlling the number of blocks and threads per block, which is crucial for optimizing parallel execution and handling multi-dimensional data structures like tensors in deep learning.

  • What does the 'cudaDeviceSynchronize()' function do?

    -The 'cudaDeviceSynchronize()' function pauses the execution of the code and waits for the GPU to complete its tasks before proceeding. This ensures that the data is ready to be used by the CPU before continuing.

  • What is the GTC conference and how does it relate to CUDA?

    -The GTC (GPU Technology Conference) is an event where talks are given about building massive parallel systems with CUDA. It is a resource for learning more about advanced CUDA applications and parallel computing.

  • What programming language is commonly used for writing CUDA code?

    -CUDA code is most often written in C++, which can be compiled and run using tools like the CUDA toolkit and integrated development environments (IDEs) such as Visual Studio.

Outlines

00:00

🚀 Introduction to CUDA and GPU Computing

This paragraph introduces CUDA as a parallel computing platform developed by Nvidia in 2007, which enables the use of GPUs for more than just gaming. It explains how CUDA has revolutionized computing by allowing parallel processing of large data blocks, which is crucial for deep neural networks and artificial intelligence. The paragraph also discusses the historical use of GPUs for graphics computation and contrasts the parallel processing capabilities of GPUs with the versatility of CPUs. It then describes the process of developing a CUDA application, including writing a CUDA kernel, copying data to GPU memory, and executing the kernel in parallel.

Mindmap

Keywords

💡CUDA

CUDA, or Compute Unified Device Architecture, is a parallel computing platform and application programming interface model created by Nvidia. It allows developers to use the GPU (Graphics Processing Unit) for general purpose processing, not just for graphics. In the video, CUDA is highlighted as a revolutionary technology that enables parallel computation, which is crucial for handling large data sets in machine learning and deep neural networks.

💡GPU

A Graphics Processing Unit (GPU) is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. In the context of the video, GPUs are used for their ability to perform matrix multiplication and vector transformations in parallel, which is essential for tasks like playing games at high resolutions and for deep learning computations.

💡Parallel Computing

Parallel computing is a type of computation in which multiple calculations or processes are carried out simultaneously. The video emphasizes the importance of parallel computing in unlocking the potential of deep neural networks and artificial intelligence by allowing the simultaneous processing of large data blocks. This is achieved through the use of CUDA, which leverages the parallel processing capabilities of GPUs.

💡Deep Neural Networks

Deep neural networks are a class of machine learning algorithms that are composed of multiple layers of artificial neurons. They are capable of learning complex patterns from large amounts of data. The video mentions that CUDA has revolutionized the world by enabling the parallel computation required for training these powerful models, which is a key component in the development of artificial intelligence.

💡Matrix Multiplication

Matrix multiplication is a mathematical operation that takes a pair of matrices, or other array types, and produces another matrix. It is a fundamental operation in linear algebra and is extensively used in various fields, including computer graphics and machine learning. In the video, matrix multiplication is highlighted as a computationally intensive task that GPUs are well-suited to handle, thanks to their parallel processing capabilities.

💡Vector Transformations

Vector transformations involve changing the coordinates of vectors in a multi-dimensional space. This is a common operation in graphics processing and is also important in machine learning. The video mentions that GPUs are designed to handle a large number of these transformations in parallel, which is crucial for rendering high-resolution images and performing complex computations in AI.

💡TeraFLOPs

TeraFLOPs, or trillions of floating-point operations per second, is a measure of a computer's performance, particularly its ability to perform floating-point calculations. In the video, modern GPUs are measured in TeraFLOPs to indicate their computational power, which is essential for handling the demands of parallel computing and data-intensive tasks like those in machine learning.

💡Cuda Kernel

A Cuda kernel is a function that runs on the GPU. It is written in a way that allows it to be executed in parallel across multiple threads. In the video, the process of writing a Cuda kernel is described as a key step in developing a Cuda application, where the kernel adds two vectors together, demonstrating the parallel processing capabilities of the GPU.

💡Managed Memory

In CUDA, managed memory is a type of memory allocation that is automatically managed by the CUDA runtime. It allows data to be accessed from both the host (CPU) and the device (GPU) without the need for explicit data transfer. The video mentions managed memory as a feature that simplifies the process of data movement between the CPU and GPU, which is crucial for efficient parallel computing.

💡Thread Blocks and Grids

In CUDA, thread blocks and grids are organizational structures used to manage the parallel execution of threads on the GPU. A grid is a collection of thread blocks, and each block contains a group of threads that can work together. The video explains that configuring the launch of a Cuda kernel involves specifying the number of blocks and threads per block, which is essential for optimizing the parallel execution of the code.

💡GTC Conference

The GTC (GPU Technology Conference) is an annual event hosted by Nvidia that focuses on parallel computing and AI. The video mentions the upcoming GTC conference as a resource for learning more about building massive parallel systems with CUDA, indicating that it is a valuable event for developers and data scientists interested in leveraging GPU technology.

Highlights

Cuda is a parallel computing platform developed by Nvidia in 2007.

Cuda enables the use of GPUs for tasks beyond video gaming, such as parallel data computation.

Cuda has revolutionized the world by unlocking the potential of deep neural networks in AI.

GPUs are designed for parallel matrix multiplication and vector transformations, crucial for high-resolution gaming.

Modern GPUs, like the RTX 490, have over 16,000 cores compared to a CPU's 24 cores.

Cuda allows developers to harness the GPU's parallel processing power.

Data scientists use Cuda to train powerful machine learning models.

A Cuda application involves writing a kernel function that runs on the GPU.

Data is copied from main RAM to the GPU's memory for processing.

The GPU executes the kernel function in parallel, organized into a multi-dimensional grid of threads.

The final result from the GPU is copied back to the main memory.

Cuda code is often written in C++ and compiled using the Cuda toolkit.

The global specifier is used to define a Cuda kernel function.

Managed memory allows data to be accessed by both the CPU and GPU without manual copying.

The main function for the CPU initializes arrays and runs the Cuda kernel on the GPU.

Triple brackets in Cuda code configure the kernel launch, controlling block and thread usage.

Cuda device synchronization pauses execution until the GPU completes the task and copies data back.

Nvidia's GTC conference features talks on building massive parallel systems with Cuda.

Transcripts

00:00

Cuda a parallel Computing platform that

00:02

allows you to use your GPU for more than

00:05

just playing video games compute unified

00:07

device architecture was developed by

00:09

Nvidia in 2007 based on the prior work

00:11

of Ian buck and John Nichols since then

00:14

Cuda has revolutionized the World by

00:16

allowing humans to compute large blocks

00:17

of data in parallel which is unlock the

00:19

true potential of the deep neural

00:21

networks behind artificial intelligence

00:23

the graphics Processing Unit or GPU is

00:25

historically used for what the name

00:27

implies to compute Graphics when you

00:29

play a game in 1080p at 60 FPS you've

00:31

got over 2 million pixels on the screen

00:33

that may need to be recalculated after

00:35

every frame which requires Hardware that

00:37

can do a lot of matrix multiplication

00:39

and Vector transformations in parallel

00:41

and I mean a lot modern gpus are

00:43

measured in teraflops or how many

00:45

trillions of floating Point operations

00:46

can it handle per second unlike modern

00:48

CPUs like the Intel I9 which has 24

00:51

cores a modern GPU like the RTX 490 has

00:54

over 16,000 cores a CPU is designed to

00:57

be versatile while a GPU is designed to

01:00

go really fast in parallel Cuda allows

01:02

developers to tap into the gpu's power

01:04

and data scientists all around the world

01:06

are using at this very moment trying to

01:07

train the most powerful machine learning

01:09

models it works like this you write a

01:11

function called a Cuda kernel that runs

01:13

on the GPU you then copy some data from

01:15

your main Ram over to the gpu's memory

01:18

then the CPU will tell the GPU to

01:20

execute that function or kernel in

01:22

parallel the code is executed in a block

01:24

which itself organizes threads into a

01:26

multi-dimensional grid then the final

01:27

result from the GPU is copied back to

01:30

the main memory a piece of cake let's go

01:31

ahead and build a Cuda application right

01:33

now first you'll need an Nvidia GPU then

01:36

install the Cuda toolkit Cuda includes

01:38

device drivers a runtime compilers and

01:40

Dev tools but the actual code is most

01:42

often written in C++ as I'm doing here

01:44

in Visual Studio first we use the global

01:47

specifier to define a function or Cuda

01:49

kernel that runs on the actual GPU this

01:51

function adds two vectors or arrays

01:54

together it takes pointer arguments A

01:55

and B which are the two vectors to be

01:57

added together and pointer C for the

01:59

result C equals a plus b but because

02:02

hypothetically we're doing billions of

02:03

operations in parallel we need to

02:05

calculate the global index of the thread

02:07

in the block that we're working on from

02:08

there we can use managed which tells

02:10

Cuda this data can be accessed from both

02:12

the host CPU and the device GPU without

02:15

the need to manually copy data between

02:17

them and now we can write a main

02:18

function for the CPU that runs the Cuda

02:20

kernel we use a for Loop to initialize

02:22

our arrays with data then from there we

02:24

pass this data to the ad function to run

02:27

it on the GPU but you might be wondering

02:29

what these weird triple brackets are

02:30

they allow us to configure the Cuda

02:32

kernel launch to control how many blocks

02:34

and how many threads per block are used

02:36

to run this code in parallel and that's

02:37

crucial for optimizing multi-dimensional

02:39

data structures like tensors used in

02:41

deep learning from there Cuda device

02:43

synchronize will pause the execution of

02:45

this code and wait for it to complete on

02:47

the GPU when it finishes and copies the

02:49

data back to the host machine we can

02:51

then use the result and print it to the

02:52

standard output now let's execute this

02:54

code with Auda compiler by clicking the

02:56

play button congratulations you just ran

02:58

256 threads in parallel on your GPU but

03:01

if you want to go beyond nvidia's GTC

03:03

conference is coming up in a few weeks

03:05

it's free to attend virtually featuring

03:07

talks about building massive parallel

03:08

systems with Cuda thanks for watching

03:10

and I will see you in the next one

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Etiquetas relacionadas
CUDAParallel ComputingGPU UtilizationAIMachine LearningNVIDIADeep Neural NetworksC++Visual StudioGTC Conference
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