CPU vs. GPU for machine learning

Featuring hundreds to thousands of processing cores, graphics processing units (GPUs) excel at the type of parallel processing and floating point calculations necessary for training machine learning models. However, in some types of AI models, CPUs can be capable—especially for lighter workloads. 

Originally designed for graphics rendering, GPUs are often referred to as graphics cards. But these powerful processors are capable of so much more. High-speed computational power and advanced parallel processing capabilities have made GPUs highly desirable across industries such as robotics, high-performance computing (HPC), data centers and, especially, artificial intelligence. 

While not as powerful as GPUs, central processing units (CPUs) are the most critical component of any computer system. Commonly considered “the brain of the computer,” CPUs handle all high-level computer management tasks, including managing GPUs (when present).

While most machine learning tasks do require more powerful processors to parse large datasets, many modern CPUs are sufficient for some smaller-scale machine learning applications. While GPUs are more popular for machine learning projects, increased demand can lead to increased costs. GPUs also require more energy than CPUs, adding to both energy costs and environmental impact.

When selecting a processor for a machine learning project, CPUs might be more cost-effective, although most moderately advanced AI projects benefit from the parallel processing of a GPU. 

In computer science, machine learning (ML) is the study, practice and application of certain types of algorithms that enable computers to mimic the ways in which humans learn to perform tasks autonomously. Computers capable of machine learning can improve their performance accuracy over time through repetition as they are exposed to more data.

Machine learning algorithms can be broken down into three basic components: a decision process, an error function and a model optimization process.

1. Decision process: Machine learning systems are designed to make educated decisions to deliver desirable results with high accuracy and require low or no human intervention. The decision process responds to some degree of input data and formulates a response in the form of a prediction or classification.
2. Error function: When a machine learning algorithm has decided, it evaluates its own output for accuracy. The error function can check outputs against known or previously identified errors to determine whether an output meets a satisfactory accuracy threshold.
3. Model optimization process: The defining characteristic of a machine learning algorithm is the ability to “learn” from its mistakes and automatically adjust its decision-making process to deliver more accurate results. The model optimization process uses data points in the model’s training materials to continuously make and evaluate predictions. By repeating this process, the model can self-calibrate for improved accuracy over time.

Machine learning can be broken down into three main types depending on the types of algorithms employed and the scale of the data used. While the term deep learning is often used interchangeably with machine learning, deep learning is a subset of neural networks, and neural networks are a subset of machine learning.

All three terms can be considered subsets of artificial intelligence (AI), and all three terms can be referred to under the umbrella of machine learning; however, there are nuanced differences:

• Machine learning: Classical machine learning uses algorithms to analyze historical to first surface patterns and then provide predictions with little to no human intervention. This type of machine learning requires large and continuously updating datasets to improve its ability to predict wanted or accurate outcomes. 

• Neural networks: Neural networks are trained on massive amounts of data and use nodes to imitate the decision-making processes of the human brain. When training a neural network, the algorithm compares input data against a standardized dataset, checking the validity of likely predictions against potential errors. 

• Deep learning: An evolution of neural networks, the term deep learning refers to a type of algorithmic AI that uses a neural network model with three or more layers of decision-making nodes.

Recent advancements in AI technology have led to a proliferation of machine learning applications in industry and everyday life. Some common machine learning use cases include:

• Speech recognition: Machine learning is used in computer speech recognition to identify natural speech patterns and interpret the implied meaning of voice commands. Speech recognition is the driving technology behind tools such as smart speakers and digital assistants such as Siri.

• Customer service: Services such as AI customer service chatbots use machine learning to help consumers along their customer journey. Examples include virtual agents on e-commerce sites, messaging bots and automated moderators on messaging platforms such as Slack and Discord.

• Recommendation engines: Faced with more options than ever before, recommendation engines driven by AI help curate information to deliver quality suggestions aligned with users’ tastes. Search engines such as Google or Bing rely on machine learning to deliver better search results. Media platforms such as Spotify or Netflix use ML to surface new programs or songs based on consumers’ past preferences.

• Robotic process automation (RPA): Also known as software robotics, robotic process automation (RPA) uses intelligent automation technologies to perform repetitive manual tasks.

• Fraud detection: Banks and other financial institutions can use machine learning to spot suspicious transactions through fraud detection. Supervised learning can train a model by using information about known fraudulent transactions. Anomaly detection can identify transactions that look atypical and deserve further investigation.

The main difference between CPUs and GPUs comes down to sequential versus parallel processing. CPUs are designed to process instructions and quickly solve problems sequentially. GPUs are designed for larger tasks that benefit from parallel computing. Because GPUs are better able to break down significant problems into smaller problems that can be solved simultaneously, GPUs can offer improved speed and efficiency in intensive machine learning applications. 

CPUs are designed for general computing tasks such as basic calculations, media playback and web browsing. As the computer’s “brain,” they also handle all the behind-the-scenes processes and functions necessary for the smooth operation of the computer’s hardware and operating systems. 

Features:

• Standard components include one or more logic cores where data is processed, memory units, the CPU clock and a control unit. Since CPUs process tasks sequentially, having access to more cores enables CPUs to multitask by spreading problems across multiple processors.

• CPUs process data sequentially, breaking down problems one after another with good speed, but limited capacity. Massive datasets can cause significant bottlenecks.    

• CPUs have comparatively few cores that run at high speeds. 

Pros:

• Designed for general-purpose use cases, CPUs can handle most types of calculations required in common applications. 

• CPUs are foundational pieces of computing equipment. As such, they are commonly available, low cost and easy to program. 

Cons:

• Even with more cores, sequential CPUs will always be slower than GPUs for certain types of problems, for which parallel computing is the only way to optimize processing speeds. 

GPUs were originally designed for rendering graphics, but since the introduction of the GPU programming platform CUDA by Nvidia in 2006, developers have found countless applications for these powerful processors. GPUs are used in addition to CPUs to add power to systems rendering high-quality video content or processing large and complex datasets.

Features:

• GPUs are designed with many more cores running at slower speeds optimized for parallel processing. GPUs break down complex problems into thousands of smaller tasks to be processed simultaneously instead of serially. 

Pros:

• The GPU’s parallel processing capabilities can batch instructions to run niche computations exceptionally well. While GPU cores are slower than CPU cores, cumulatively parallel processing can solve large and complicated problems faster than sequential alternatives. 

• Although GPUs are more complicated to program than CPUs, they are well optimized for popular machine learning programming languages and frameworks such as Python and Tensorflow. 

Cons:

• GPUs are more expensive and less readily available than CPUs.

• Programming GPUs requires some specialized knowledge. 

The differences between CPUs and GPUs come down to three key areas: architecture, processing speed and accessibility.

1. Architecture: CPUs are designed with fewer cores to process data sequentially. GPUs typically feature hundreds to thousands more cores designed for parallel processing.
2. Processing speed: CPUs are designed to handle general and top-level tasks quickly, however, they struggle with extremely large datasets, such as the kind used in machine learning. GPUs are tuned specifically for processing these types of large datasets. GPUs massively outperform CPUs in most machine learning applications. 
3. Accessibility: CPUs are more common than GPUs and cost less to acquire and operate. GPUs also require more specialized training to program. However, GPUs are common in machine learning and AI use cases, with robust libraries and communities offering support. 

Both CPUs and GPUs are processing units. They are both capable of handling similar tasks, with varying degrees of performance based on the demands of a specific application. And while both can be thought of as singular units, they are each a collection of different components designed and arranged for different types of operations.

Predating GPUs, the CPU is the most important and fundamental part of any computer system, from laptops and smartphones to satellites and supercomputers. Acting like an invisible manager, CPUs read and interpret inputs and requests, issue instructions to carry out calculations and oversee all the operations of a computer system. 

Despite being more powerful, GPUs are not used to replace CPUs. Instead, as a co-processor, GPUs are used to augment a computer system’s capabilities. In systems that use GPUs, the CPU still plays an important role in managing the GPUs tasks and all other processing tasks that, while not as resource-intensive, are still integral to the computer’s functions. 

In large-scale data processing, using underpowered CPUs frequently creates frustrating bottlenecks. Existing at the intersection of computer science and data science, machine learning algorithms often rely on GPUs to speed up the massive dataset processing used for deep learning model training with reduced latency. That’s because even multi-core CPUs process data differently from GPUs. 

Structurally, GPU cores typically number in the thousands, while most consumer-grade CPUs only contain one, two, four or six cores. Server-grade CPUs might contain hundreds or even thousands of cores, but the number of cores alone does not dictate performance. 

Multi-core CPUs are better at multitasking than single-core CPUs, but they still process data sequentially. GPUs handle data differently, through a process known as parallel computing. Instead of processing tasks sequentially, GPUs break down problems into component parts and use their multitude of cores to work on different parts of a problem concurrently. 

For demanding tasks such as achieving computer vision for AI systems or generative AI programs, parallel computing easily outperforms sequential processing.

GPUs, with their parallel processing capabilities, continue to be a critical component for AI projects. Within machine learning specifically, GPUs are used to speed up training times for machine learning applications and perform the kinds of tensor math and matrix multiplication ML systems require to make inferences and produce useful results.