Graphics card CUDA refers to "Unified Computing Device Architecture". It is a computing platform launched by graphics card manufacturer NVIDIA. It is a general parallel computing architecture that enables GPU to solve complex computing problems; it includes CUDA Instruction Set Architecture (ISA) and the parallel computing engine inside the GPU.
The operating environment of this tutorial: Windows 10 system, Dell G3 computer.
CUDA (Compute Unified Device Architecture) is a computing platform launched by graphics card manufacturer NVIDIA. CUDA™ is a general-purpose parallel computing architecture introduced by NVIDIA that enables GPUs to solve complex computing problems. It includes the CUDA instruction set architecture (ISA) and the parallel computing engine inside the GPU.
Developers can use C language to write programs for the CUDA™ architecture. C language is the most widely used high-level programming language. The programs you write can run at ultra-high performance on CUDA™-enabled processors. CUDA3.0 has begun to support C and FORTRAN.
Scope of application
The computing industry is developing from "central processing" that only uses CPU to "co-processing" that uses both CPU and GPU. In order to create this new computing paradigm, NVIDIA™ invented the CUDA (Compute Unified Device Architecture) programming model to fully utilize the advantages of CPU and GPU in applications. This architecture is already used in GeForce™, ION™, Quadro and Tesla GPUs, making it a huge market for application developers.
In the consumer market, almost every important consumer video application has been accelerated using CUDA or will soon be accelerated using CUDA, including those from Elemental Technologies, MotionDSP and LoiLo. product.
In the scientific research community, CUDA has always been popular. For example, CUDA can now accelerate AMBER. AMBER is a molecular dynamics simulation program used by more than 60,000 researchers worldwide in academia and pharmaceutical companies to accelerate the discovery of new drugs.
In financial markets, Numerix and CompatibL released CUDA support for a new counterparty risk application and achieved an 18x speedup. Numerix is used by nearly 400 financial institutions.
The wide application of CUDA has led to the rise of Tesla GPU dedicated for GPU computing. More than 700 GPU clusters have been installed by global Fortune 500 companies in various fields, such as Schlumberger and Chevron in the energy field and BNP Paribas in the banking industry.
With the advent of Microsoft Windows 7 and Apple Snow Leopard operating systems, GPU computing will surely become mainstream. In these new operating systems, the GPU will be more than just a graphics processor, it will become a general-purpose parallel processor that can be used by all applications.
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