GPU computing uses a graphics processing unit or GPU as a co-processor to accelerate CPUs for general-purpose scientific and computational engineering.
What is GPU computing and its history?
Here, the GPU accelerates applications running on the CPU by offloading some of the code’s compute-intensive and time-consuming portions. Hence, the rest of the application still runs on the CPU. The application runs faster because it uses the GPU’s massively parallel processing power to boost a user’s perspective. It is known as “heterogeneous” or “hybrid” computing.
Here, a CPU consists of four to eight CPU cores, while the GPU consists of hundreds of smaller centers. Hence, they operate to crunch through the data in the application together. It is massively parallel architecture is what gives the GPU its high compute performance. Several GPU-accelerated applications provide an easy way to access high-performance computing or HPC. Hope you know what is GPU scaling, if not learn it there.
Comparison of Core between a CPU and a GPU:
Here, the application developers harness the parallel GPU architecture’s performance using a similar programming model invented by NVIDIA called “CUDA.” Here, all NVIDIA GPUs – GeForce, Quadro, and Tesla – support the NVIDIA CUDA parallel programming model.
Tesla GPUs are designing as computational accelerators or companion processors optimized for scientific and technical computing applications. The latest Tesla 20-series GPUs are base on the newest implementation of the CUDA platform called the “Fermi architecture.” Here, Fermi has key computing features such as 500+ gigaflops of IEEE standard floating-point hardware with double-precision support, L1 and L2 caches. Then, ECC memory error protection, local user-managed data caches in the form of shared memory dispersed throughout the GPU, combine memory accesses, and more.
History of GPU computing:
Here, Graphics chips started as fixed-function graphics pipelines. These graphics chips became increasingly programmable over the years, which led NVIDIA to introduce the first GPU. In the 1999-2000 timeframe, computer scientists and researchers in medical imaging and electromagnetics started using GPUs to accelerate a range of scientific applications. It was the advent of the movement called GPGPU, or General Purpose GPU computing.
The challenge was that GPGPU required graphics programming languages like OpenGL and Cg to program the GPU. Here, the developers had to make their scientific applications look like graphics applications and map them into problems that drew triangles and polygons. It limited the accessibility to the tremendous performance of GPUs for science.
Here, NVIDIA realized the potential to bring this performance to the larger scientific community and invest in modifying the GPU to fully programmable for scientific applications. Here, it added support for high-level languages like C, C++, and Fortran. It led to the CUDA parallel computing platform for the GPU.
What does a GPU do?
Here, “GPU” became a popular term for the component, which powers graphics on a machine in the 1990s when coined by chip manufacturer Nvidia. Hence, the company’s GeForce range of graphics cards was the first to be popularised and make sure related to technologies such as hardware acceleration, programmable shading, and stream processing could evolve.
While the task of rendering necessary objects can usually handle by the limited graphics processing functionalities built into the CPU like an operating system’s desktop, here, some more exacting workloads require the extra firepower, which is where a dedicated GPU comes in. A GPU is a processor, which is specially designing to handle intensive graphics rendering tasks in short.
Here, Computer-generated graphics – such as those found in video games or other animated mediums – require each separate frame to be individually drawn by the computer, which requires a large amount of power.
Hence, most high-end desktop PCs will feature a dedicated graphics card, which occupies one of the motherboard’s PCIe slots. These usually have their dedicated memory allocation built into the menu, reserved exclusively for graphical operations. Some incredibly advanced PCs will even use two GPUs hooked up together to provide even more processing power.
Thus, Laptops, meanwhile, often carry mobile ships, which are smaller and less powerful than their desktop counterparts. It allows them to fit an otherwise bulky GPU into a smaller chassis at the expense of some of the desktop cards’ raw performance.
Conclusion:
If you’re looking for a specialized, enterprise kind of processor that aims for specific applications, it recommends that you choose a GPU with additional in-depth support.
