Deep learning is becoming popular and has multiple applications. To simplify the whole process of deep learning, parallel and distributed algorithms are required. This can be explained by parallelizing the problem. Then an empirical analysis of CPU and GPU time shall be done. To understand these terminologies, it is also important to understand stochastic gradient descent along with the pseudo-code for analysis of distributed gradient update algorithms.
What Is Deep Learning?
It is a machine learning type based on artificial neural networks. After multiple layers of processing data, higher-level features are extracted progressively from the given data. Deep neural networks proficiently find correlations in unsupervised data. That is the reason why deep learning is extensively used in speech analysis, computer vision and natural language processing (NLP).
This extracted data is stored in a distributed form. That means data distribution is done across different layers in different layers of a neural network and across different neurons. The number of ways for a combination of information can be calculated by the permutation of the distributed elements and the number of layers. This is done to minimize the loss of functions. This is a proxy for testing a neural network's efficiency and performance in achieving its objectives.
Why Parallel and Distributed Algorithms Are Required In Deep Learning?
In normal neural networks, millions of parameters define the deep learning model. A large amount of data is required to learn all these parameters. This process is computationally intensive. Naturally, a lot of time is required for this computation. It takes days to train a DNN like Visual Geometry Group Network on a single core CPU. Let us assume that a single machine has 8 cores in the CPU; it will take around 10 hours approximately to train a model like VGGNet. There have been instances when the dataset is extensive for being stored on a single machine.
That is why parallel and distributed algorithms are required. These can be faster and can reduce training times considerably.
What is a Parallel Deep Learning Algorithm?
A parallel algorithm is a training algorithm which is capable of executing multiple instructions at the same time on different processing devices. After that, these machines combine individual outputs from all the machines to compute the final result.
These problems are divided into various sub-problems for procuring separate outputs. Parallel algorithms can be used to solve puzzles like the Rubik's cube.
What is a Distributed Deep Learning Algorithm?
A distributed algorithm is a particular type of algorithm which is used on software which uses several processors that are interconnected. The distributed algorithm helps in running different parts of the algorithm simultaneously on different processors, which communicate with each other to run the software properly.
Distributed algorithms find their applications in social networks, internet banking, and VoIP (voice over internet protocols) chat messengers.
A Brief Insight into Parallel and Distributed Algorithm Methods
As explained above, Parallel and Distributed Algorithms methods can help in reducing training times. But there are many methods for parallelizing or distributing computation across several machines and several cores. Some methods which are used for accomplishing quicker training times are given below:
- Local training: The machine learning model and datasets are stored on a single machine i.e. a single CPU.
- Multi-core processing: An assumption is made here that the machine learning model and the dataset can fit entirely into a single machine's CPU, which has multiple cores (at least 8 cores).
Multiple cores share the memory, probably in the PRAM (Parallel random access machine) model, which is very helpful in training the models at a faster speed. Here the cores which have to process many images at once are used in each layer.
Differences between Parallel and Distributed Algorithm methods
- In Distributed Systems, the systems have been loosely coupled. On the other hand, in the Parallel System, the system coupling is tighter.
- Distributed Systems, fault tolerance, synchronization and scalability are the primary objectives. In the Parallel System, the primary issue is speeding up the data handling capability.
- In both algorithms, the events are partially ordered.
- Distributed systems and parallel systems don't have a clear distinction, but their objectives are entirely different.
To conclude, the concept of deep learning is evolving with each passing year, and newer concepts are coming into play to improve it. Hopefully, this write-up has shed light on all your questions related to parallel and distributed deep learning and helped you better understand the same.