In the realm of deep learning, the choice between Graphics Processing Units (GPUs) and Central Processing Units (CPUs) often arises, prompting the need for understanding the intricate differences and comparative advantages of both. This article dives deep into the distinct characteristics and performance of GPUs and CPUs in the context of deep learning, machine learning, and neural networks.
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Understanding the CPU
The Central Processing Unit (CPU) is often referred to as the brain of the computer. It handles the basic instructions of a computer system, including arithmetic, logical functions, and Input/Output (I/O) operations.
The CPU’s architecture includes several essential components: a collection of cores, cache, a memory management unit (MMU), and a clock and control unit. These elements work together, enabling the computer to run multiple applications simultaneously.
Traditionally, CPUs were single-core, but modern CPUs are multicore, boasting two or more processors to enhance performance. CPUs process tasks sequentially, dividing tasks among its multiple cores to achieve multitasking.
Introducing the GPU
The Graphics Processing Unit (GPU), initially designed for rendering high-resolution images and graphics, has evolved beyond its original function. Today, GPUs play a significant role in big data analytics and machine learning, a form of computing often referred to as “General Purpose GPU” or GPGPU.
Like CPUs, GPUs consist of similar components such as cores and memory. They can be integrated into the CPU or discrete, separate from the CPU with its own Random Access Memory (RAM). The significant distinction lies in their processing style – GPUs use parallel processing, distributing tasks into smaller subtasks amongst a vast number of processor cores. This process results in faster processing of specialized computing tasks.
GPU vs. CPU: The Fundamental Differences
The primary distinction between GPUs and CPUs lies in their processing styles. While CPUs excel at performing sequential tasks quickly, GPUs leverage parallel processing to simultaneously compute tasks with enhanced speed and efficiency.
CPUs, as general-purpose processors, can handle various calculations and allocate significant power to multitasking between several sets of linear instructions. However, they are less efficient at parallel processing.
On the other hand, GPUs excel at specialized computations. They can consist of thousands of cores running operations in parallel across multiple data points. By batching instructions and processing vast amounts of data at high volumes, GPUs can accelerate workloads beyond the capabilities of a CPU. This feature makes GPUs particularly advantageous for specialized tasks like machine learning, data analytics, and artificial intelligence (AI) applications.
The Inner Workings of a GPU
Unlike CPUs, which typically have fewer cores running at high speeds, GPUs possess numerous processing cores operating at lower speeds. When assigned a task, a GPU divides it into thousands of smaller subtasks and processes them concurrently instead of serially.
In graphics rendering, GPUs manage complex mathematical and geometric calculations essential for creating realistic visual effects and imagery. For smooth visual experiences, these instructions must be executed simultaneously, drawing and redrawing images hundreds of times per second.
GPUs also perform pixel processing, a complex process requiring substantial processing power to render multiple layers and create intricate textures necessary for realistic graphics. This high level of processing power makes GPUs suitable for machine learning, AI, and other tasks demanding hundreds or thousands of complex computations.
GPU vs. CPU in Machine Learning
Machine learning, a subset of AI, utilizes algorithms and historical data to identify patterns and predict outcomes with minimal human intervention. It requires large continuous data sets to improve the accuracy of the algorithm.
While GPUs are preferred for data-intensive machine learning processes, CPUs serve as cost-effective options for specific use cases. These use cases include machine learning algorithms such as time series data that don’t require parallel computing and recommendation systems for training that need lots of memory for embedding layers.
GPUs, with their advanced technology, provide the parallel processing necessary to support the complex multistep processes involved in machine learning. Therefore, the more data available, the better and faster a machine learning algorithm can learn.
CPU vs. GPU in Neural Networks
Neural networks, attempting to mimic the human brain’s behavior, learn from vast amounts of data. During the training phase, a neural network scans data for input and compares it against standard data to form predictions and forecasts.
As neural networks involve massive data sets, training time can increase with the growth of the data set. While CPUs can manage smaller-scale neural networks, they become less efficient at processing large volumes of data, causing the training time to increase as more layers and parameters are added.
Given that neural networks, the foundation of deep learning, are designed to run in parallel, GPUs are more suitable for processing the enormous data sets and complex mathematical data required for training neural networks.
CPU vs. GPU in Deep Learning
Deep learning models, essentially neural networks with three or more layers, have highly flexible architectures. They can learn directly from raw data, and training these networks with large data sets can increase their predictive accuracy.
CPUs are less efficient than GPUs for deep learning as they process tasks in a sequential order. As more data points are used for input and forecasting, it becomes increasingly challenging for a CPU to manage all associated tasks.
In deep learning, speed and high performance are crucial, and models learn more quickly when all operations are processed at once. With their thousands of cores, GPUs are optimized for training deep learning models, processing multiple parallel tasks up to three times faster than a CPU.
The Role of Next-gen AI Infrastructure
GPUs play a vital role in developing machine learning applications. When selecting a GPU for your machine learning applications, various manufacturers are available, with NVIDIA, a pioneer and leader in GPU hardware and software, leading the pack.
The modern AI infrastructure, such as AIRI//S™ by Pure Storage® and NVIDIA, and powered by the latest NVIDIA DGX systems and Pure Storage FlashBlade//S™, simplifies AI deployment. It delivers simple, fast, next-generation, future-proof infrastructure to meet your AI demands at any scale.
Bridging the Performance Gap: CPU vs. GPU in Deep Learning Models
Notably, CPUs are ubiquitous and serve as more cost-effective options for running AI-based solutions compared to GPUs. However, finding models that are both accurate and can run efficiently on CPUs can be challenging.
Typically, GPUs are three times faster than CPUs. The performance of a model depends on several factors, including the model’s architecture, the target inference hardware, model compression methods like quantization and pruning, and runtime compilers such as OpenVino or TensorRT. These compilers optimize the connection between the software, network, and target hardware.
The Impact of Hardware and Compilation Techniques
While the hardware is often predetermined by the end-use application and business needs, it’s essential to note that models perform differently on different hardware.
Compilation and quantization techniques can improve runtime, reduce the memory footprint, and minimize model size, but they don’t work predictably across all models. For instance, while you might expect that halving a model would make it twice as efficient, this is not always the case.
Hardware has limitations when working with quantized models, but using compilation and quantization techniques can help close the performance gap between GPUs and CPUs for deep learning inference.
Neural Architecture Search: A Powerful Ally
Rather than manually probing all the factors impacting inference performance, Neural Architecture Search (NAS) can be leveraged for better model selection. NAS is a class of algorithms that automatically generate neural networks under specific constraints of budget, latency, accuracy, and more.
However, traditional NAS approaches are time-consuming and expensive. To address these constraints, production-aware NAS can be considered. This approach optimizes the architecture to ensure that it meets the inference requirements in production.
Closing the Performance Gap
By leveraging technologies like Deci’s AutoNAC and hardware-specific optimizations, the gap between a model’s inference performance on a GPU versus a CPU can be significantly reduced, without sacrificing the model’s accuracy.
In conclusion, to maximize your CPU’s performance, consider the various parameters impacting inference and production constraints. Hardware awareness early in the model development stage is critical for better model selection and successful performance optimization.
To boost your deep learning models’ performance on a CPU, consider automating the model compilation and quantization for Intel’s CPUs or get a DeciNet model optimized for CPU and your desired performance requirements.
As we’ve seen, the choice between CPUs and GPUs for deep learning is not a simple one. While GPUs offer superior performance for highly parallel tasks, CPUs are more cost-effective for certain use cases. By understanding the strengths and weaknesses of each, along with the various factors that can influence performance, you can make the most informed decision for your specific needs.
Whether you’re a seasoned practitioner or just starting your journey into deep learning, understanding the intricacies of these processing units can help you optimize your models and achieve your performance goals.