GPU | 圖形處理器
What is it?
GPUs (graphics processing units) are similar to CPUs (central processing units) in the sense that they are both processors, but different in many other fundamental ways. Compared with a CPU, a GPU tends to have less complicated control units and arithmetic logic units (ALUs), as well as smaller caches. This simpler, more specialized parallel structure allows GPUs to have a much higher number of cores than the more complex CPUs.
To illustrate, ARM CPUs have more cores than their x86 counterparts because they adopt the RISC architecture, but even they cap out at a couple hundred cores, while a GPU can support thousands of cores. The upshot of this is while a CPU is general-purpose and can tackle just about any computing task with its comprehensive toolset, a GPU is very good at doing some certain tasks very quickly by using its more abundant provision of the simplified versions of same tools. Said tasks include generating the computer graphics we see on our PC displays, or processing the massive amount of graphical data that is used in AI training.
Historically, GPUs were developed as a cheaper alternative to the expensive CPUs. They were mainly used to render graphics for computer games. Technological advances have led to the rise of discrete or dedicated graphics cards, which are often also called GPUs, but that's a misnomer; these are in fact expansion cards with GPUs at the core. Graphics cards offer GPUs additional support with dedicated memory, heat sinks, and other components. This is why discrete GPUs generally outperform the integrated GPUs that are built into motherboards or CPUs. For this reason, graphics cards have become a mainstay of the esports and gaming industry.
In recent years, scientists and engineers have discovered that the execution of many computing tasks can be expedited by using GPUs. GPUs used for something other than generating graphics are sometimes called GPGPUs. High performance computing (HPC), artificial intelligence, and many other astounding breakthroughs have been made possible by using servers that utilize a large number of GPGPUs.
To illustrate, ARM CPUs have more cores than their x86 counterparts because they adopt the RISC architecture, but even they cap out at a couple hundred cores, while a GPU can support thousands of cores. The upshot of this is while a CPU is general-purpose and can tackle just about any computing task with its comprehensive toolset, a GPU is very good at doing some certain tasks very quickly by using its more abundant provision of the simplified versions of same tools. Said tasks include generating the computer graphics we see on our PC displays, or processing the massive amount of graphical data that is used in AI training.
Historically, GPUs were developed as a cheaper alternative to the expensive CPUs. They were mainly used to render graphics for computer games. Technological advances have led to the rise of discrete or dedicated graphics cards, which are often also called GPUs, but that's a misnomer; these are in fact expansion cards with GPUs at the core. Graphics cards offer GPUs additional support with dedicated memory, heat sinks, and other components. This is why discrete GPUs generally outperform the integrated GPUs that are built into motherboards or CPUs. For this reason, graphics cards have become a mainstay of the esports and gaming industry.
In recent years, scientists and engineers have discovered that the execution of many computing tasks can be expedited by using GPUs. GPUs used for something other than generating graphics are sometimes called GPGPUs. High performance computing (HPC), artificial intelligence, and many other astounding breakthroughs have been made possible by using servers that utilize a large number of GPGPUs.
Why do you need it?
In the consumer market, a good GPU (in the form of a top-notch graphics card) has become an indelible part of the immersive gaming or media viewing experience. The mining of cryptocurrency also led to a surge in demand for GPUs.
As mentioned before, GPUs are essential to gaming and esports because the specialized parallel structure can render high-quality images on the display very quickly, allowing for photorealistic visual effects, higher refresh rates, higher frame rates (FPS), and an ultra-smooth gaming experience at high resolutions. The same attributes are also great for rendering computer graphics and creating visual content; for animators and visual artists, having a powerful GPU can vastly accelerate the creative process. The demand for GPU computing is so pressing, professional creatives either set up their own workstations or servers in their studios, or they hire services from a remote render farm to bring their vision into reality more efficiently.
For the GPUs (or GPGPUs) that are installed in servers to help with computing tasks, the possibilities are endless. Computer vision, machine learning, and deep learning are just some of the applications that GPUs excel at. Generative AI, the process by which artificial intelligence generates texts or images, was made possible with GPU computing. GPUs can be used in both AI training and AI inference, but especially the former, since the massive amount of big data that must be "studied" by the AI model can be processed more effectively through parallel computing. More and more industry leaders are offering complete solutions that combine hardware and software to help various vertical markets reap the benefits of GPUs, whether it's manufacturing, transportation, healthcare, education, or entertainment.
As mentioned before, GPUs are essential to gaming and esports because the specialized parallel structure can render high-quality images on the display very quickly, allowing for photorealistic visual effects, higher refresh rates, higher frame rates (FPS), and an ultra-smooth gaming experience at high resolutions. The same attributes are also great for rendering computer graphics and creating visual content; for animators and visual artists, having a powerful GPU can vastly accelerate the creative process. The demand for GPU computing is so pressing, professional creatives either set up their own workstations or servers in their studios, or they hire services from a remote render farm to bring their vision into reality more efficiently.
For the GPUs (or GPGPUs) that are installed in servers to help with computing tasks, the possibilities are endless. Computer vision, machine learning, and deep learning are just some of the applications that GPUs excel at. Generative AI, the process by which artificial intelligence generates texts or images, was made possible with GPU computing. GPUs can be used in both AI training and AI inference, but especially the former, since the massive amount of big data that must be "studied" by the AI model can be processed more effectively through parallel computing. More and more industry leaders are offering complete solutions that combine hardware and software to help various vertical markets reap the benefits of GPUs, whether it's manufacturing, transportation, healthcare, education, or entertainment.
How is GIGABYTE helpful?
GIGABYTE is an industry leader in the discrete graphics cards market, with an expansive product line that incorporates GPUs from major brands like AMD and NVIDIA. The GPUs used in GIGABYTE's PC products, such as the AORUS and AERO laptops, also boast incredible performance.
GIGABYTE's graphics cards are mainly separated into four product lines: AORUS, AERO, EAGLE, and GAMING. The AORUS series is designed for high-end PC gaming; it incorporates innovative features to bring the best performance out of the GPUs, such as the Waterforce liquid cooling system, which dissipates the heat generated by the GPUs with liquid coolant. The GAMING and EAGLE series are mainstream graphics cards that strike a balance between performance and affordability. The AERO series, on the other hand, is aimed at creatives who use GPUs to render graphics, edit videos, and create content. In addition to a plethora of features that give users an edge in the creative process, these graphics cards also sport a simple silver-white design that's favored by consumers with an eye for aesthetics.
What's more, GIGABYTE is dedicated to exploring the potential of heterogeneous computing—the combination of CPUs and GPUs—in servers that are used by enterprises and research institutes. The G-Series GPU Servers are an entire product line that offers scalable, high-density GPU designs well-suited for data analytics and scientific research. To share just a few examples, the European Organization for Nuclear Research (CERN) uses GIGABYTE's G482-Z51 to conduct quantum physics research with the Large Hadron Collider (LHC). Lowell Observatory in Arizona, USA, where Pluto was first discovered in 1894, uses GIGABYTE's G482-Z50 to help look for a "Twin Earth" in outer space. The G593-Series servers are some of the most powerful AI servers on the planet, combining the latest CPUs by AMD (for the G593-ZD2) or Intel (for the G593-SD0) with NVIDIA HGX H100 8-GPU, a powerful computing module that brings GPU acceleration to AI training. Wherever there are groundbreaking discoveries to be made or inventions to be created, GPUs—and the servers that support them—have an important role to play.
GIGABYTE's graphics cards are mainly separated into four product lines: AORUS, AERO, EAGLE, and GAMING. The AORUS series is designed for high-end PC gaming; it incorporates innovative features to bring the best performance out of the GPUs, such as the Waterforce liquid cooling system, which dissipates the heat generated by the GPUs with liquid coolant. The GAMING and EAGLE series are mainstream graphics cards that strike a balance between performance and affordability. The AERO series, on the other hand, is aimed at creatives who use GPUs to render graphics, edit videos, and create content. In addition to a plethora of features that give users an edge in the creative process, these graphics cards also sport a simple silver-white design that's favored by consumers with an eye for aesthetics.
What's more, GIGABYTE is dedicated to exploring the potential of heterogeneous computing—the combination of CPUs and GPUs—in servers that are used by enterprises and research institutes. The G-Series GPU Servers are an entire product line that offers scalable, high-density GPU designs well-suited for data analytics and scientific research. To share just a few examples, the European Organization for Nuclear Research (CERN) uses GIGABYTE's G482-Z51 to conduct quantum physics research with the Large Hadron Collider (LHC). Lowell Observatory in Arizona, USA, where Pluto was first discovered in 1894, uses GIGABYTE's G482-Z50 to help look for a "Twin Earth" in outer space. The G593-Series servers are some of the most powerful AI servers on the planet, combining the latest CPUs by AMD (for the G593-ZD2) or Intel (for the G593-SD0) with NVIDIA HGX H100 8-GPU, a powerful computing module that brings GPU acceleration to AI training. Wherever there are groundbreaking discoveries to be made or inventions to be created, GPUs—and the servers that support them—have an important role to play.
