Introduction of CPU
Devices to turn PCs into small supercomputers have been known for a long times already. Back in the 1980s the market offered so-called transputers to be installed into ISA expansion slots. At first their performance was impressive in certain applications. But then performance of general-purpose processors started to grow faster, they became stronger in parallel computing, so there was no point in using transputers anymore. However, such devices exist even now -- they are specialized accelerators. But their field of application is very narrow, and such accelerators are not widely spread.
However, parallel computing has already entered the mass market and 3D games. Universal devices with multi-core processors for parallel vector computing in 3D graphics reach high peak performance, CPUs cannot keep up with it. Of course, maximum performance is reached only in a number of convenient tasks and has some limits. But such devices are already widely used in the new fields they were not initially designed for. An excellent example of such parallel processors is Cell, designed by Sony-Toshiba-IBM for Sony PlayStation 3, as well as all modern graphics cards from market leaders -- NVIDIA and AMD.
We'll not touch upon Cell today, even though it had been launched earlier, and it's a general-purpose processor with additional vector features. The first GPGPU technologies (General-Purpose computation on GPUs) for 3D graphics cards appeared several years ago. Modern GPUs contain hundreds of arithmetic units, and their power can be used to accelerate a lot of compute-intensive applications. The existing generation of GPUs possesses a flexible architecture. Together with high-level programming languages and firmware architectures, such as the ones described in this article, it reveals these features and makes them much more accessible.
GPCPU was inspired by the appearance of relatively fast and flexible shader programs that can be executed by modern GPUs. Developers decided to employ GPUs not only for rendering in 3D applications, but also for other parallel computations. GPGPU used graphics APIs for this purpose: OpenGL and Direct3D. Data were fed to a GPU in the form of textures, and computing programs were loaded as shaders. This method had its shortcomings -- relatively high programming complexity, low data exchange rate between a CPU and a GPU, and other limitations to be described below.
However, parallel computing has already entered the mass market and 3D games. Universal devices with multi-core processors for parallel vector computing in 3D graphics reach high peak performance, CPUs cannot keep up with it. Of course, maximum performance is reached only in a number of convenient tasks and has some limits. But such devices are already widely used in the new fields they were not initially designed for. An excellent example of such parallel processors is Cell, designed by Sony-Toshiba-IBM for Sony PlayStation 3, as well as all modern graphics cards from market leaders -- NVIDIA and AMD.
We'll not touch upon Cell today, even though it had been launched earlier, and it's a general-purpose processor with additional vector features. The first GPGPU technologies (General-Purpose computation on GPUs) for 3D graphics cards appeared several years ago. Modern GPUs contain hundreds of arithmetic units, and their power can be used to accelerate a lot of compute-intensive applications. The existing generation of GPUs possesses a flexible architecture. Together with high-level programming languages and firmware architectures, such as the ones described in this article, it reveals these features and makes them much more accessible.
GPCPU was inspired by the appearance of relatively fast and flexible shader programs that can be executed by modern GPUs. Developers decided to employ GPUs not only for rendering in 3D applications, but also for other parallel computations. GPGPU used graphics APIs for this purpose: OpenGL and Direct3D. Data were fed to a GPU in the form of textures, and computing programs were loaded as shaders. This method had its shortcomings -- relatively high programming complexity, low data exchange rate between a CPU and a GPU, and other limitations to be described below.
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