Why does machine learning use GPUs?

The GPU (graphics processing unit) is now the backbone of AI. Originally developed to speed up graphics processing, GPUs can greatly expedite the computing operations needed in deep learning. Many modern applications failed because machine learning needed more active, accurate, or both. Large neural networks benefited significantly from the incorporation and use of GPUs.

Autonomous vehicles and face recognition are two examples of how deep learning has revolutionized technology. In this article, we'll discuss why GPUs are so useful for machine learning applications −

How do Graphics Processing Units Work?

As with every neural network, the deep learning model's training phase is the process's most time- and energy-consuming part. The original intent of these chips was to handle visual information. To improve predictions, weights are tweaked to identify patterns. But these days, GPUs are also used to speed up other kinds of processing, like deep learning. This is because GPUs lend themselves well to parallelism, making them ideal for large-scale distributed processing.

The Function of Graphics Processing Units (GPU)

First, let's take a step back while ensuring we fully grasp how GPUs work.

Nvidia's GeForce 256, released in 1999, was instrumental in popularizing the phrase "graphics processing unit" due to its ability to do graphical operations such as change, illumination, and triangle clipping. Processes can be optimized and hastened thanks to the engineering that is specific to these tasks. This involves complex calculations that aid in the visualization of three-dimensional environments. Repetition is produced when millions of calculations are performed, or floating point values are used. The conditions are ideal for parallel execution of tasks.

With cache and additional cores, GPUs can easily outperform dozens of CPUs. Let's take an example −

Adding more processors will increase the speed linearly. Even with 100 CPUs, the procedure would still take over a week, and the cost would be fairly high. The issue can be resolved in less than a day using parallel computing on a small number of GPUs. So, we were able to accomplish the unimaginable by developing this gear.

How Machine Learning got Benefits Through GPU?

GPU has a lot of processor cores, which is great for running parallel programs. Graphics processing units (GPUs) enable the accumulation of numerous cores that consume fewer resources without compromising efficiency or power. As a result of its ability to handle several computations simultaneously, GPUs are particularly well-suited for use in the training of artificial intelligence and deep learning models. This allows for the decentralization of training, which in turn speeds up machine learning processes. Furthermore, machine learning computations need to deal with massive amounts of data, so the memory bandwidth of a GPU is ideal.

Usage of GPU

Quantity of Data

In order to train a model with deep learning, a sizable amount of data must be collected. A graphics processing unit (GPU) is the best option for fast data computation. The size of the dataset is irrelevant to the scalability of GPUs in parallel, which makes processing large datasets much quicker than on CPUs.

Bandwidth of Memory

One of the key reasons GPUs are quicker for computing is that they have more bandwidth. Memory bandwidth, particularly that provided by GPUs, is available and necessary for processing massive datasets. Memory on the central processing unit (CPU) can be depleted rapidly during instruction regarding a large dataset. This is because modern GPUs come equipped with their own video RAM (VRAM), freeing up your CPU for other uses.

Optimization

Due to the extensive work involved, parallelization in dense neural networks is notoriously challenging. One drawback of GPUs is that it can be more challenging to optimize long-running individual operations than it is with CPUs.

Choices in GPU Hardware for Machine Learning

There are a number of possibilities for GPUs to use in deep learning applications, with NVIDIA being the industry leader. You have the choice of picking among managed workstations, GPUs designed for use in data centers, or GPUs aimed at consumers.

GPUs Designed for Home Use

These GPUs are an inexpensive add-on to your existing system that can help with model development and basic testing.

The NVIDIA Titan RTX has 130 teraflops of processing power and 24 GB of RAM. Built on NVIDIA's Turing GPU architecture, it features Tensor and RT Core technologies.

NVIDIA Titan V − Depending on the variant, this GPU offers anywhere from 110 to 125 teraflops of speed and 12 to 32 terabytes of memory. The NVIDIA Volta architecture and Tensor Cores are utilized.

Graphics Processing Units in the Data Center

These graphics processing units (GPUs) are made for massive undertakings and can deliver server-level performance.

NVIDIA A100 − It gives you 624 teraflops of processing power and 40 gigabytes of memory. With its multi-instance GPU (MIG) technology, it is capable of large scaling for use in high-performance computing (HPC), data analytics, and machine learning.

NVIDIA Tesla P100 − The NVIDIA Tesla P100 has 16 GB of RAM and can process 21 teraflops. Based on the Pascal architecture, it's made with high-performance computing and machine learning in mind.

NVIDIA v100 − The newest NVIDIA v100 graphics card supports up to 32 GB of RAM and 149 TFLOPS of processing power. NVIDIA Volta technology forms the basis for this product, which was made for HPC, ML, and DL.

GPU Performance Indicators for Deep Learning

Due to inefficient allocation, the GPU resources of many deep learning projects are only used between 10% to 30% of the time. The following KPIs should be tracked and used to ensure that your GPU investments are being put to good use.

Use of Graphics Processing Units

Metrics for GPU utilization track how often your graphics processing unit's kernels are used. These measurements can be used to pinpoint where your pipelines are lagging and how many GPUs you need.

Temperature and Power Consumption

Metrics like power utilization and temperature allow you to gauge the system's workload, allowing you to better foresee and manage energy needs. Power consumption is measured at the PSU and includes the power needed by the CPU, RAM, and any other cooling components.

Using and Accessing GPU Memory

GPU memory use and access metrics calculate the memory controller's utilization rate. These indicators can help you determine the best training batch size and evaluate the performance of your advanced machine-learning software.

Conclusion

GPUs are the safest choice for fast learning algorithms because the bulk of data analysis and model training comprises simple matrices math operations, the performance of which may be significantly enhanced if the calculations are performed in parallel. Consider purchasing a GPU if your neural network requires extensive computation with hundreds of thousands of parameters.