Best CPU or Best GPU : get the most out of both
Best CPU or Best GPU : get the most out of both, Today’s systems are expected to do much more than ever before – be it
deep learning applications, massively parallel data processing,
intensive 3D gaming, or other demanding applications. CPU
(Central Processing Unit, “main processor”) and GPU (Graphics Processing
Unit, “graphics processor”) have very different tasks. What are
CPUs used for? What are GPUs used for? When buying a new computer
computer and comparing the technical features, it is important to know
important to know what role each of these devices plays.
What is a CPU?
Made up of millions or billions of transistors, the CPU can have
have several processor cores (“computing cores”) and are also commonly referred to
referred to as the brain of the computer. It is the basic component
of all modern computer systems, as it executes the program instructions and the processes
processes required for the computer and the operating system.
The CPU also plays a key role in determining how quickly programs can be
can be executed, from surfing the Internet to spreadsheets.
What is a GPU?
The GPU is a processor that consists of many smaller cores designed for
cores designed for more specialized tasks. Their
cores offer enormous performance when data processing processes are
data processing can be divided among many cores.
What is the difference between a CPU and a GPU?
CPUs and GPUs have a lot in common. Both are crucial
important computer elements. Both are microprocessors based on semiconductor
microprocessors. And both process data. However, CPUs and GPUs have
different architectures and are used for different purposes.
used for different purposes.
The CPU is suitable for a variety of tasks, especially for
those where latency or performance per core is important.
is important. As a powerful building block for the execution of program
instructions, the CPU uses a smaller number of cores for individual
tasks and completing them quickly. This makes the CPU
particularly suitable for jobs ranging from serial data processing to the
operation of databases.
GPUs were initially specialized ASICs designed to accelerate certain 3D rendering processes.
certain 3D rendering processes. Over time
these graphics engines with originally defined functions became more
more programmable and flexible. While the
graphics and the increasingly lifelike rendering of today’s top computer
computer games are the main function of GPUs, they have also evolved into
have also developed into multi-purpose parallel processors for a growing range of
range of applications.
What is integrated graphics?
In the case of integrated graphics or graphics with shared
memory, the graphics unit is located on the same chip as the CPU.
Certain CPUs may come with an integrated GPU,
instead of relying on dedicated or discrete graphics.
Integrated graphics processors offer several advantages. Their
integration with CPUs allows them to realize space, cost, and
energy efficiency advantages over dedicated graphics processors.
offer. They provide sufficient performance for processing graphics
graphics-related data and commands for common tasks such as web browsing
web browsing, streaming 4K movies, and casual gaming.
casual gaming.
This concept is most commonly applied to devices where
compact dimensions and energy efficiency are important, such as
laptops, tablets, smartphones, and some desktop PCs.
Accelerating deep learning and AI
GPUs are currently being used for more and more tasks such as deep learning and
artificial intelligence (AI). A GPU or other accelerators
are ideal for deep learning training with neural network layers
or for massive sets of specific data, such as 2D images.
Deep learning algorithms have been adapted for a GPU-accelerated approach.
approach. The acceleration gives these algorithms a significant
performance boost and brings the training time to real-world
problems into a feasible and sustainable range.
CPUs and the software libraries created for them have evolved over the
developed into much more powerful tools for deep learning tasks.
deep learning tasks. Through extensive
software optimizations and the addition of dedicated AI hardware, such as
Intel® Deep Learning Boost (Intel® DL Boost) in the latest
scalable Intel® Xeon® processors, CPU-based systems enjoy
for example, enjoy improvements in deep learning performance.
For many applications, such as high-definition, 3D and non-image-based
image-based deep learning with speech, text, and time series data,
the strengths of CPUs come into their own. CPUs can handle much larger
memory than even today’s best GPUs for complex models or deep learning
or deep learning applications (e.g. 2D image recognition).
The combination of CPU and GPU and sufficiently large RAM provides excellent testing capabilities for deep learning and AI.
Decades of leadership in CPU development
Intel can look back on many years of experience in CPU innovation
which began in 1971 with the introduction of the 4004 as the first commercial
microprocessor fully integrated on a semiconductor chip,
it’s beginning.
Today, with Intel® CPUs based on the familiar x86 architecture
the desired AI solution can be realized where it makes sense.
appears. From the high-performance, scalable Intel® Xeon®
processor series in the data center and the cloud to
energy-efficient Intel® Core™ processors at the network edge, Intel offers
CPUs for every need.
Intelligent performance features of 13th generation Intel® Core™ processors
Intel® Core™ processors of the 13th generation
utilize a performance hybrid architecture with faster
performance cores (P-cores) and more efficiency cores (E-cores) with industry-leading
industry-leading tools to optimize performance and multitasking capabilities.
optimized.
Select laptops with 13th generation Intel Core processors can include Intel® Iris® Xe graphics or dedicated Intel® Iris® Xe MAX graphics
the first discrete GPU architecture with Intel Xe. The Intel®
Iris® Xe MAX dedicated graphics provide a big leap forward in the thin and light
in the field of thin and light notebooks. You also get more
performance and new features for improved content creation
content creation and an enhanced gaming experience.
Intel® Iris® Xe graphics with Intel® Deep Learning
Boost-powered AI for better content creation, e.g. for photo and video
and video editing as well as a power-saving architecture for a longer
longer battery life.
Separate GPUs from Intel
Intel offers three separate GPU options.
Intel® Iris® Xe MAX with dedicated graphics
is a separate GPU with graphics card options for laptops and desktops.
desktops. Based on the Xe architecture, you get even more performance
performance and new features, such as Intel® Arc™ Control Center for
improved content creation and gaming.
The Intel® Data Center GPU
is a GPU that supports new technologies such as AI, rendering, analytics, and
simulations. It also provides powerful parallel processing
processing capabilities for data center CPUs.
Create engaging content, captivate your audience and
enhance the gaming experience many times over with the latest high-performance
high-performance graphics solution, Intel Arc™ Graphics.
Based on the Xe HPG microarchitecture, Intel Arc Graphics enables
integrated machine learning, graphics acceleration and
ray tracing hardware for laptops, desktops, and professional
workstations.
Today, it’s no longer about balancing CPU and GPU against each other.
More than ever, both are needed together to meet the diverse
requirements for data processing. The best
results are undoubtedly achieved when the right tool is used for each
the right tool is used for each task.