CUDA Explained - Why Deep Learning uses GPUs
Summary
TLDRThis 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
🚀 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.
🛠 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.
🌐 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.
Mindmap
Keywords
💡CUDA
💡GPU (Graphics Processing Unit)
💡CPU (Central Processing Unit)
💡Parallel Computing
💡Core
💡Neural Networks
💡Deep Learning
💡Convolution Operation
💡NVIDIA
💡PyTorch
💡GPGPU (General-Purpose Computing on Graphics Processing Units)
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
the single most important tool that we
have ever created over the last 25 years
the computer has advanced in performance
100 thousand times scientists and
researchers are at the brink of
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
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