CUDA Explained - Why Deep Learning uses GPUs

Summary

This video tutorial introduces CUDA, a software platform by NVIDIA that enables developers to utilize GPUs for parallel computing, accelerating neural network programming. It explains the difference between CPUs and GPUs, emphasizing the latter's superiority in handling parallel tasks, ideal for deep learning. The video also touches on the evolution of GPUs from graphics processing to general-purpose computing, highlighting NVIDIA's pioneering role. It simplifies complex concepts like convolution operations and demonstrates how to leverage CUDA with PyTorch for efficient computation, providing a foundational understanding for beginners in neural network programming.

Takeaways

• 🚀 CUDA is a software platform developed by NVIDIA that allows developers to utilize the parallel processing power of NVIDIA GPUs for accelerated computations.
• 🔍 GPUs are specialized processors that excel at handling parallel computations, unlike CPUs which are better at general computations.
• 🔄 Parallel computing involves breaking down a large computation into smaller, independent tasks that can be executed simultaneously.
• 🔢 The number of parallel tasks a computation can be broken into is determined by the number of cores in the hardware, with GPUs potentially having thousands of cores compared to a few in CPUs.
• 🌟 GPUs are particularly suited for 'embarrassingly parallel' tasks, such as those found in neural networks, where many computations can be performed independently.
• 🧠 Neural networks benefit from GPUs due to their ability to handle the large number of independent computations required for tasks like the convolution operation in deep learning.
• 🛠️ NVIDIA has been a pioneer in GPU computing, with CUDA being created nearly a decade ago to support general-purpose computing on GPUs.
• 📚 The CUDA toolkit includes specialized libraries like cuDNN (CUDA Deep Neural Network library) that facilitate the development of deep learning applications.
• 💡 Using PyTorch with CUDA is straightforward; developers can move computations to the GPU by calling the .cuda() function on tensors.
• ⚠️ Not all computations are faster on a GPU; it is beneficial for tasks that can be parallelized and may not be efficient for smaller or less parallelizable tasks.
• 🌐 The advancement of GPU computing has implications beyond graphics; it's now a driving force in fields like deep learning, scientific computing, and AI.

Q & A

What is CUDA and why is it significant in neural network programming?

CUDA is a software platform created by NVIDIA that pairs with their GPU hardware, making it easier for developers to build software that accelerates computations using the parallel processing power of NVIDIA GPUs. It is significant in neural network programming because it allows for efficient computation acceleration, especially for tasks that can be performed in parallel, which is common in deep learning.

What is the difference between a CPU and a GPU in terms of computation capabilities?

A CPU, or Central Processing Unit, is designed for handling general computations and typically has a few cores, ranging from four to sixteen. On the other hand, a GPU, or Graphics Processing Unit, is specialized for handling computations that can be done in parallel and can have thousands of cores. GPUs are much faster at computing for parallel tasks compared to CPUs.

What is parallel computing and why is it beneficial for neural networks?

Parallel computing is a type of computation where a larger computation is broken down into independent, smaller computations that can be carried out simultaneously. This is beneficial for neural networks because they consist of many computations that can be performed in parallel, allowing for significant speedups when using hardware like GPUs that are designed for such tasks.

Why are GPUs particularly well-suited for deep learning tasks?

GPUs are well-suited for deep learning tasks because neural networks are 'embarrassingly parallel,' meaning they can be easily broken down into a large number of smaller, independent tasks that can be executed in parallel. High-end GPUs have thousands of cores that can perform these computations simultaneously, greatly accelerating the training and inference processes.

What is an example of a computation in deep learning that can be parallelized and why is it efficient on a GPU?

The convolution operation is an example of a computation in deep learning that can be parallelized. It involves applying a filter over an input image in a way that each position's computation is independent of the others. This allows all positions to be processed in parallel on a GPU, making efficient use of its many cores and accelerating the overall computation.

What does NVIDIA provide in terms of software and hardware to support GPU computing?

NVIDIA provides the hardware in the form of GPUs that are capable of parallel computations. On the software side, they offer CUDA, a software platform that includes an API for developers to leverage the power of NVIDIA GPUs. Additionally, they provide specialized libraries like cuDNN (CUDA Deep Neural Network library) to further facilitate the development of deep learning applications.

What is the relationship between PyTorch and CUDA?

PyTorch is a deep learning framework that can take advantage of CUDA to accelerate computations on NVIDIA GPUs. PyTorch integrates CUDA seamlessly, allowing developers to perform GPU-accelerated computations without needing to use the CUDA API directly, making it easier to work with while still benefiting from GPU performance.

Why might running computations on a GPU not always be faster than on a CPU?

Running computations on a GPU is not always faster due to several factors. For instance, moving data between the CPU and GPU can be costly in terms of performance. Additionally, if the computation task is simple or small, the overhead of parallelizing it may not yield significant speedups, and in some cases, it could slow down the process.

What is the significance of the term 'embarrassingly parallel' in the context of neural networks?

The term 'embarrassingly parallel' refers to tasks that can be easily broken down into many smaller, independent sub-tasks with little to no effort. Neural networks are considered embarrassingly parallel because their computations, such as the forward and backward passes during training, can be naturally decomposed into many parallelizable operations.

What is GPGPU and how does it relate to CUDA?

GPGPU stands for General-Purpose computing on Graphics Processing Units. It is a programming model that allows GPUs to be used for computations that are not necessarily related to graphics processing. CUDA is a key component of GPGPU, as it provides the software layer that enables developers to write programs that can run on GPUs for a wide range of applications beyond graphics.

How does the process of moving a tensor to a GPU using PyTorch differ from using it on a CPU?

In PyTorch, tensors are created on the CPU by default. To move a tensor to a GPU, you call the `.cuda()` method on the tensor object. This transfers the tensor to the GPU memory, allowing subsequent operations on that tensor to be performed on the GPU, leveraging its parallel processing capabilities for potentially faster computation.

Outlines

00:00

🚀 Introduction to CUDA and GPU in Neural Networks

This paragraph introduces the concept of CUDA and the role of GPUs in neural network programming. It explains that GPUs are specialized processors designed for handling parallel computations, which makes them faster than CPUs for certain tasks. The explanation includes the difference between CPUs and GPUs, the concept of parallel computing, and how many cores are typically found in each. It also touches on why GPUs are particularly well-suited for neural networks due to their ability to handle 'embarrassingly parallel' tasks, such as the convolution operation in deep learning, which can be broken down into many smaller, independent computations that can be executed simultaneously on a GPU.

05:03

🛠 Understanding CUDA and Its Integration with PyTorch

The second paragraph delves into the specifics of CUDA as a software platform developed by NVIDIA to facilitate the use of their GPU hardware for accelerated computations. It discusses the necessity of having an NVIDIA GPU to use CUDA and how developers can download and install it for free. The paragraph also explains the role of libraries in GPU computing and how PyTorch simplifies the use of CUDA by integrating it from the start, allowing developers to leverage GPU acceleration without needing to understand the CUDA API directly. It further explores the trade-offs of using Python for PyTorch's ease of use and the performance benefits of dropping into C, C++, and CUDA at bottleneck points. The summary also includes a practical example of how to move tensors to a GPU using PyTorch and discusses the considerations of when to use GPU versus CPU for computations.

10:04

🌐 The Evolution of GPU Computing and Its Future

The final paragraph discusses the evolution of GPU computing from its origins in computer graphics to its current role in a variety of parallel tasks, including deep learning and scientific computing. It highlights NVIDIA's pioneering role in the development of general-purpose GPU (GPGPU) computing and CUDA, which has been instrumental in enabling the growth of this field. The paragraph also touches on the broader implications of advancements in computing, such as the potential for breakthroughs in precision medicine, weather prediction, climate understanding, material science, and artificial intelligence. It concludes with a reflection on the importance of computing as a transformative human invention and its role in driving scientific discovery and innovation.

Keywords

💡CUDA

CUDA, which stands for Compute Unified Device Architecture, is a parallel computing platform and application programming interface (API) model created by NVIDIA. It allows developers to use NVIDIA GPUs for general purpose processing, not just graphics rendering. In the context of the video, CUDA is essential for neural network programming as it enables the acceleration of computations through parallel processing power, making it a key component in deep learning tasks.

💡GPU (Graphics Processing Unit)

A GPU is a specialized processor designed to handle complex computations, particularly those that can be performed in parallel. Unlike a CPU, which is optimized for general computations, a GPU contains potentially thousands of cores that can execute multiple threads simultaneously. In the video, GPUs are highlighted as crucial for neural network programming due to their ability to perform parallel computations, which is ideal for tasks in deep learning.

💡CPU (Central Processing Unit)

The CPU is the primary processing unit in a computer, designed to handle a wide range of computations. It is typically optimized for sequential processing and contains fewer cores compared to a GPU. In the script, the CPU is contrasted with the GPU to illustrate the difference in their capabilities, particularly in the context of parallel computing and neural network programming.

💡Parallel Computing

Parallel computing refers to the simultaneous execution of multiple tasks or computations. It is a method where a larger computation is divided into smaller, independent tasks that can be carried out at the same time. The video emphasizes the importance of parallel computing in neural networks, as it allows for the efficient processing of data in deep learning algorithms.

💡Core

In the context of computing, a core refers to a processing unit within a CPU or GPU that is capable of executing tasks. CPUs typically have fewer cores, while GPUs can have thousands, making them more suitable for parallel processing. The script explains that the number of cores is a determining factor in the ability to perform parallel computing.

💡Neural Networks

Neural networks are a set of algorithms designed to recognize patterns and represent data in a way that is inspired by the human brain. They are the foundation of deep learning and are used in various applications, including image and speech recognition. The video discusses how neural networks are 'embarrassingly parallel,' making them highly suitable for processing with GPUs.

💡Deep Learning

Deep learning is a subset of machine learning that uses neural networks with many layers to learn complex patterns in large amounts of data. The video script explains that deep learning heavily utilizes GPUs due to the parallel nature of the computations involved in training neural networks.

💡Convolution Operation

The convolution operation is a key process in neural networks, particularly in convolutional neural networks (CNNs), where it is used to apply a filter to an input image to produce an output feature map. The script uses an animation to illustrate how this operation can be performed in parallel on a GPU, highlighting the efficiency gains possible with parallel processing.

💡NVIDIA

NVIDIA is a technology company renowned for its design and manufacturing of GPUs. In the video, NVIDIA is credited with creating CUDA, which is a significant development in the field of GPU computing. The company's vision and work in GPU computing have positioned it as a leader in the industry, especially in the area of deep learning.

💡PyTorch

PyTorch is an open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing. The script mentions PyTorch as a framework that integrates seamlessly with CUDA, allowing developers to leverage the power of NVIDIA GPUs for accelerated computation in neural network programming.

💡GPGPU (General-Purpose Computing on Graphics Processing Units)

GPGPU refers to the use of GPUs for computations that are not directly related to graphics processing. The video discusses how the advent of CUDA and the increasing capability of GPUs have led to a new programming model where GPUs are used for a wide range of scientific and computational tasks beyond their traditional role in graphics rendering.

Highlights

Introduction to CUDA and its role in neural network programming with PyTorch.

Explanation of GPUs as processors specialized in handling parallel computations, contrasting with CPUs designed for general computations.

The concept of parallel computing and its suitability for tasks that can be broken down into independent, simultaneous computations.

The advantage of GPUs with thousands of cores over CPUs with fewer cores for parallel tasks.

Neural networks are described as 'embarrassingly parallel', making them ideal for GPU acceleration.

Illustration of the convolution operation in deep learning as an example of a parallelizable task.

The history and development of CUDA by NVIDIA as a software platform for GPU computing.

Necessity of an NVIDIA GPU to utilize CUDA and its free availability for developers.

Integration of CUDA within PyTorch for seamless GPU utilization without direct API usage.

Demonstration of how to move tensors to GPU using PyTorch for accelerated computations.

Discussion on the potential bottlenecks and performance costs associated with moving data between CPU and GPU.

The evolution of GPUs from primarily graphics tasks to a broader range of parallel tasks including deep learning.

NVIDIA's pioneering role in general-purpose GPU computing and the establishment of CUDA a decade ago.

The layered structure of GPU computing, from hardware to CUDA software, libraries, and frameworks like PyTorch.

The strategic importance of understanding the full stack in computer science for effective GPU programming.

The significance of computer and computational advancements in scientific research and potential breakthroughs.

Invitation to visit the blog for more information and mention of exclusive perks on the deep lizard hotline.

Transcripts

welcome back to the series on neural

network programming with PI George in

this video we're going to introduce CUDA

at a high level the goal of this post is

to help beginners understand what CUDA

is and how it fits in with pi Jorge and

more importantly why we even use GPUs in

neural network programming in the first

place without further ado let's get

started to understand CUDA we need to

have a working knowledge of graphics

processing units or GPUs a GPU is a

processor that is good at handling

specialized computations this is in

contrast to a central processing unit or

CPU which is a processor that is good at

handling general computations CPUs are

the processors that power most of the

typical computations on our electronic

devices a GPU can be much faster at

computing than a CPU however this is not

always the case the speed of a GPU

relative to a CPU depends on the type of

computation being performed the type of

computation most suitable for GPU is a

computation that can be done in parallel

this brings us to parallel computing

parallel computing is a type of

computation whereby a particular

computation is broken into independent

smaller computations that can be carried

out simultaneously the resulting

computations are then recombined or

synchronized to form the result of the

original larger computation the number

of tasks that a larger computation can

be broken into depends on the number of

cores contained on a particular piece of

hardware cores are the units that

actually do the computation within a

given processor and CPUs typically have

four a or 16 cores while GPUs have

potentially thousands of cores there are

other technical specifications that

matter but this description is meant to

drive the general idea with this working

knowledge we can conclude that parallel

computing is done using GPUs we can also

conclude that tasks which are best

suited to be

of using a GPU our tasks that can be

done in parallel if a computation can be

done in parallel we can accelerate our

computation using parallel programming

approaches and GPUs let's turn our

attention now to neural networks and see

why GPUs are so heavily used in deep

learning we have just seen that GPUs are

will suited for parallel computing and

this fact about GPUs is why deep

learning uses them neural networks are

embarrassingly parallel seriously in

parallel computing an embarrassingly

parallel task is a problem where little

to no effort is needed to break the task

down into an independent set of smaller

tasks neural networks are embarrassingly

parallel and GPUs typically have 3000

like high-end GPUs have 3000 cores that

can run computations in parallel many of

the computations we do in neural

networks can indeed be easily broken

into smaller computations that are

independent with respect to one another

so it's the nature of computations used

in neural networks that makes GPUs so

useful in deep learning let's look at an

example computation that's often used in

deep learning the convolution operation

this animation showcases the convolution

process without numbers we have an input

channel in blue on the bottom a

convolutional filter shaded on top of

the input channel that is sliding across

the input channel and a green output

channel for each position of the

convolutional filter on top of the input

channel there's a corresponding green

region on the output channel this is the

output of the convolution operation at

each point in the animation these

computations are happening sequentially

one after the other however each

computation is independent from the

others this means that none of the

computations depend on the results of

any of the other computations as a

result all of these independent

computations can happen in parallel on a

GPU and the overall output channel can

then be produced after all of the

computations have been completed this

allows us to see that the convolution

operation can be accelerated using a

parallel programming approach and a GPU

this is where kou

comes into play when invidious GPU

computing approach pioneer was the

entire stack thinking from architecture

to processor two systems system software

api's libraries and application solvers

we optimized across the entire stack one

domain at a time one domain at a time

and it is incredibly hard working that's

one the reasons why staking us almost

ten years nvidia is a technology company

that designs GPUs and they have created

CUDA as a software platform that pairs

with their GPU hardware making it easier

for developers to build software that

accelerates computations using the

parallel processing power of NVIDIA GPUs

and NVIDIA GPU is the hardware that

enables parallel computations while CUDA

is the software layer that provides an

API for developers developers developers

developers developers developers

developers developers developers

developers developers

as a result you might have guessed that

an NVIDIA GPU is required to use CUDA

once you have an NVIDIA GPU CUDA can be

downloaded and installed from Nvidia's

website for free developers use CUDA by

downloading the CUDA toolkit in with the

toolkit come specialized libraries like

kudi and in the CUDA deep learning

neural network library the stack GPU

computing basically works in several

ways the first step of course is to

build an amazing GPU that's the first

step the first type is building the

amazing GPU the second step is to create

the libraries for that domain the system

software the system's architecture the

api's and the libraries accelerated

libraries for that domain and in the

case of high-performance computing it's

linear algebra as FFTs it's all kinds of

different ups of libraries and we have

all the libraries created and now we

deep learning KU DNN and with inference

tensor RT the libraries are in place the

third step is to work with all of the

application developers the solvers

technical teams work hand-in-hand to

accelerate to refactor their algorithms

of their application and run it on our

libraries with pi torch cuda comes baked

in from the start there are no

additional downloads required all we

need is to have a supported NVIDIA GPU

and we can leverage CUDA using PI torch

we don't need to know how to use the

CUDA API directly now if we want it to

work on the PI torch core development

team or right PI torch extensions it

would definitely be useful to know how

to use CUDA directly much of pi torch is

written in Python however at bottleneck

points PI tortes drops into C C++ and

CUDA to speed up processing and get that

performance boost we fight it in various

ways one of the simplest ways is we just

move all of our functions to succeed C

or C++ that are actually important it's

a subtle trade-off because as use of Pi

torch itself you want to make sure it's

very easy to debug and extend while

you're working with the day-to-day but

if you want performance then the biggest

hotspots cannot be in Python so the

reason we went down python like instead

of using c++ directly why we went to use

python is because python is the most

popular data science language but we

have to make these constant trade-offs

and fight Python all the time I'm in a

Jupiter notebook now and I want to show

you how to use

with pi torch taking advantage of CUDA

is extremely easy with pi torch if we

want a particular computation to be

performed on the GPU we can instruct PI

torch to do so by calling the CUDA

function on our data structures suppose

we have the following code we assign T

to be equal to a new torch dot tensor

we'll learn more about this in future

videos for now let's just focus on the

tensor output so we see the tensor

output we have a tensor with three

elements the numbers one two and three

the tensor object created in this way is

on the CPU by default as a result any

operations that we do using this tensor

object will be carried out on the CPU

now if we want to move this tensor onto

the GPU we just write T CUDA calling the

CUDA function on a tensor where it turns

the same tensor but on the GPU so after

running this code and we look at the

tensor output we have the same tensor

with three elements one two and three

but we also have a device specified and

this is what happens whenever the device

is not the CPU we actually get the value

in the output so we can see that our

device is cuda zero the zero stands for

the first index and the reason for this

is that pi torch supports multiple GPUs

so if you had multiple GPUs you could

put this tensor on a particular GPU this

ability makes PI quartz very versatile

because computations can be selectively

carried out either on the CPU or on the

GPU with that being said I want to talk

to you about a looming question we said

that we can selectively run our

computations on the GPU or on the CPU

but why not just run every computation

on the GPU is it a GPU faster than a CPU

the answer is that a GPU is only faster

for particular specialized tasks one

issue that we can run into is

bottlenecks that slow our performance

for example moving data from the CPU to

the GPU is costly so when we do this the

overall performance might be slower if

the computation task is a simple one

moving relatively small computational

tasks to the GPU won't speed us up very

much and may indeed slow us down

remember the GPU works well for tasks

that can be broken into many smaller

tasks and if the compute task is already

small we won't have much to gain by

breaking it up and moving it to the GPU

for this reason it's often acceptable to

simply use a CPU especially when just

starting out and as we tackle larger

more complicated problems we can begin

using the GPU more heavily in the

beginning the main tests that were

accelerated using GPUs or computer

graphics tasks hence the name graphics

processing unit but in recent years many

more varieties of parallel tasks have

emerged one such task as we have seen is

the task of training neural networks for

deep learning deep learning along with

many other scientific computing tasks

that use parallel programming techniques

are leading to a new type of programming

model called GP GPU or

general-purpose GPU computing NVIDIA has

been a pioneer in this space Nvidia CEO

Jensen Wong has envisioned GPU computing

very early on which is why Kudo was

created nearly 10 years ago even though

cuda has been around for a long time

it's just now beginning to really take

flight and invidious work on CUDA up

until now is why Nvidia is leading the

way in terms of GPU computing for deep

learning when we hear Jensen talk about

the GPU computing stack he is referring

to the GPU as the hardware on the bottom

cuda as the software architecture on the

top of the GPU and finally libraries

like KU DN in on top of cuda this GPU

computing stack is what supports the

general-purpose computing capabilities

on a chip that is otherwise very

specialized we often see stacks like

this in computer science as technology

is built in layers sitting on top of

CUDA and ku D and n in this stack is PI

torch which is the framework we'll be

working with that ultimately supports

applications on top the paper I'm

showing here takes a deep dive into GPU

programming and CUDA but it goes much

deeper than we need we will be working

near the top of the

stack here with pie George however it's

beneficial to have a bird's-eye view of

just where we're operating within the

overall stack we are ready now to jump

in with section 2 of this neural network

programming series which is all about

tensors remember to check the blog for

this video on deep lizard calm and don't

forget to check out the deep lizard

hotline for exclusive perks and rewards

thanks for watching and supporting

collective intelligence I'll see you in

the next one computing is the most

important invention of humanity it is

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the computer has advanced in performance

100 thousand times scientists and

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discovering solutions for precision

medicine they're at the brink of being

able to solve weather prediction and

understanding climate we're at the brink

of being able to discover the next

groundbreaking material that's light and

strong or new ways of store energy we're

at the brink of discovering away from

machines to operate themselves we're at

the brink of discovering artificial

intelligence

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