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Prerita Agarwal

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23 Jul, 2024 @ 01:30 PM

Introduction

Hello Reader!!

GPU Programming is a technique for executing extremely parallel general-purpose calculations using GPU accelerators. In this article, we will learn about GPU programming and using GPU with Theano.

So, let’s get started!

What is GPU Programming in Deep Learning?

GPU programming is a way of using the graphics processing unit (GPU) of a computer to perform general-purpose computation. In the context of deep learning, GPU programming is often used to accelerate the training of large neural networks by taking advantage of the parallel architecture of the GPU.

Deep learning algorithms involve a large number of computations, and training a neural network can be a time-consuming process. By using a GPU to perform these computations, it is possible to speed up the training process significantly. GPU programming is particularly useful for deep learning applications because the operations involved in training a neural network are highly parallelizable, making them well-suited to the parallel architecture of a GPU.

To use a GPU for deep learning, you will need to write code that runs on the GPU and takes advantage of its parallel processing capabilities. This can be done using various programming languages and libraries, such as CUDA, OpenCL, and PyTorch.

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Using GPU with Theano

Introduction to Theano

Theano is a Python package that allows you to effectively write, optimize, and evaluate mathematical expressions that include multi-dimensional arrays. It was developed as a tool for machine learning research and includes several features that make it well-suited for deep learning.

Example

To get started, consider a very simple example. Consider the following function:

f(a,b)=a*b

In mathematics, we express f as a function with two arguments, a and b. The function's body is simply a*b.

That would be written in Python as:

def f(a, b):
return a * b

In Theano, we write symbolic expressions using Python.

To begin, we import the necessary components Tc and function:

import theano.tensor as Tc
from theano import function

Following that, we define two symbols, a and b. These are Python variables as well as Theano symbols. The quoted 'a' is the Theano symbol, which is allocated to the same name Python variable (for easier understanding and debugging).

a = Tc.scalar('a')

Scalar simply indicates that it has only one value. This is known as a zero-dimensional array in Theano.

Repeating the same with b:

b = Tc.scalar('b')

We can also write both together:

a, b = Tc.scalars('a', 'b')

As can be seen, a is of the type TensorVariable.

Input:

type(a)

Output:

theano.tensor.var.TensorVariable

Then, a Theano function is defined. This is done in two steps. First, we will give the function's body:

Mul = a* b

This appears to be a multiplication of a and b. However, a and b are symbols; they do not yet have values.

The next step is to write a Python function. We employ the Theano “function” function to do this. It accepts a list of arguments as symbols, followed by the body of the Theano function:

newpyfunc = function(inputs=[a, b], outputs=Mul)

Input:

newpyfunc(3, 6)

Output:

array(18.0)

A Theano function body can be shown symbolically, possibly in a manner similar to the Python source code:

Input:

from theano import pp
pp(Mul)

Output:

'(a*b)'

Using GPU with Theano

To use a GPU with Theano, you will need to install a GPU-enabled version of Theano and set up your system to use the GPU. This typically involves installing the necessary drivers and software libraries, such as CUDA, and configuring Theano to use the GPU.

Once you have set up your system to use a GPU with Theano, you can use Theano's GPU-accelerated functions and operations to perform computations on the GPU. For example, using Theano's GPU-accelerated tensor operations to perform element-wise arithmetic on multi-dimensional arrays or using Theano's GPU-accelerated convolutional neural network (CNN) functions to train a CNN on a GPU.

To take full advantage of the parallel processing capabilities of the GPU, it is important to write optimized code for the GPU. This may involve using specialized GPU-accelerated functions and operations and structuring your code to maximize parallelism.

Improving Performance with Theano on GPU

There are a few ways to improve the performance of Theano on a GPU:

Use GPU-accelerated functions and operations: Theano includes several functions and operations optimized for use on a GPU. By using these GPU-accelerated functions and operations, you can take advantage of the GPU's parallel processing capabilities and improve your code's performance.

Structure your code to maximize parallelism: To get the most out of a GPU, it is important to structure your code in a way that maximizes parallelism. This can involve breaking up large computations into smaller, independent tasks that can be processed in parallel.

Use efficient data structures: The performance of your code can also be affected by the data structures you use. Using efficient data structures, such as contiguous arrays, can help improve the performance of your code on a GPU.

Use shared variables: Theano's shared variables allow you to store data on the GPU and access it from multiple functions and operations. Using shared variables can help reduce the overhead of transferring data between the CPU and GPU, improving the overall performance of your code.

Tune your code: The performance of your code can be affected by several factors, including the size of your data, the complexity of your computations, and the hardware you are using. Tuning your code to optimize these factors can help improve the performance of your code on a GPU.

Frequently Asked Questions

What is Theano used for?

Theano is a Python module that allows us to efficiently assess mathematical operations such as multi-dimensional arrays. It is widely utilized in the development of Deep Learning Projects.

What is a GPUs job?

A graphics processing unit (GPU) is a specialized processor that was initially created to speed up graphics rendering. GPUs can handle large amounts of data at the same time, making them ideal for machine learning, video editing, and gaming applications.

What is GPU in machine learning?

GPUs facilitate the complicated multistep processes involved in machine learning by providing parallel computing.

Conclusion

In this article, we learned about GPU Programming in Deep Learning and working on GPUs with Theano.

We hope that the article helped you learn the GPUs with Theano and its uses in an easy and insightful manner. You may read more about the Machine Learning concepts and much more here.