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In the past, the central processing unit (CPU) handled computations on a computer. However, as applications with more graphics and increasing demands on the CPU developed, its performance declined. Specialized engineering systems and fast GPU processing emerged as a way to offload these tasks from CPUs and enhance the rendering of 3D graphics. Graphics processing units (GPUs) operate using a method called parallel processing, in which multiple processors manage separate parts of a task.
Amirkabir Simulators Company, consisting of a group of specialists from Amirkabir University students, focuses on simulating industrial projects and renting specialized engineering systems and fast GPU processing.
A Graphics Processing Unit (GPU) is a computer chip responsible for rendering 2D and 3D images, animations, and videos by performing rapid mathematical calculations. Specialized engineering systems and fast GPU processing are used for both professional and personal computations, and their applications have expanded significantly.
GPUs are well-known in computer gaming (PCs) for enabling smooth, high-quality graphics rendering. Developers also use specialized engineering systems and fast GPU processing to accelerate workloads in fields like Artificial Intelligence (AI).
Specialized chips for processing graphics have existed since the early video games of the 1970s. Initially, graphical capabilities were included as part of a graphics card—a dedicated board with specific silicon chips and cooling systems to enable 2D, 3D, and even general-purpose GPU (GPGPU) computations for computers.
GPUs were introduced to high-performance enterprise computers in the late 1990s, with NVIDIA launching the first GPU for personal computers, the GeForce 256, in 1999. Over time, GPUs’ processing power made them popular for tasks unrelated to graphics, such as scientific computing and modeling. By the mid-2010s, GPUs were also widely used for machine learning and AI software.
In 2012, NVIDIA released a virtual GPU that offloads graphical processing power from a server CPU in a Virtual Desktop Infrastructure (VDI). Historically, graphical performance was one of the most common complaints among desktop and application users. Amirkabir Simulators Company aims to address this issue for users by renting specialized engineering systems and fast GPU processing.
Modern GPUs have become more versatile due to their programmability and are now suited for a variety of tasks, including:
GPUs have a structure similar to CPUs, but their purposes differ. CPUs handle basic instructions that run a computer, while GPUs are specifically designed for quickly rendering high-resolution images and videos. Essentially, CPUs interpret most computer commands, while GPUs focus on rendering graphics.
GPUs excel at parallel processing, applying the same instruction to multiple data points (SIMD). In contrast, CPUs are designed for parallel execution of varied operations. Due to their parallel architecture, GPUs can render images much faster than CPUs, which are better equipped for general-purpose computing tasks.
Key differences:
CPUs typically have higher clock speeds, allowing them to perform single computations faster.
GPUs can perform multiple calculations simultaneously due to their parallel architecture, making them better suited for tasks like rendering and simulations.
While the terms GPU and graphics card are often used interchangeably, they are distinct. A GPU is a specific unit within the graphics card, performing the actual image and graphics processing. Meanwhile, a graphics card refers to an add-on board that delivers images to the display unit.
Amirkabir Simulators Company provides affordable services, including:
Is a GPU the same as a graphics card?
While the terms are often used interchangeably, the GPU is the specific chip responsible for processing graphics, while a graphics card is the hardware housing the GPU.
Which is better: CPU or GPU?
GPUs have a greater impact on modern video game performance than CPUs, except when the CPU is significantly underpowered. However, most non-graphics computational tasks are handled by the CPU.
Why is a GPU faster?
One key reason GPUs outperform CPUs is bandwidth. CPUs use significant memory when training models, whereas GPUs have dedicated VRAM, which is more efficient for handling large datasets.