Increasingly GPUs are being used for non-graphical tasks like risk computati***, fluid dynamics calculati***, and sei**ic ****ysis. What’s to stop us from adopting GPU-driven devices?

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問題

超級使用者讀者Ell關注科技新聞，很好奇為什麼我們不使用更多基於GPU的系統：

在我看來，這些天很多計算都是在GPU上完成的。顯然，圖形是在那裡完成的，但是使用CUDA之類的，AI、雜湊演算法（比如比特幣）和其他演算法也是在GPU上完成的。為什麼我們不能擺脫CPU，自己使用GPU呢？是什麼讓GPU比CPU快這麼多？

為什麼呢？CPU的獨特之處是什麼？

答案

超級使用者貢獻者DragonLord對GPU和CPU之間的差異提供了一個受支援的概述：

TL;DR answer: GPUs have far more processor cores than CPUs, but because each GPU core runs significantly slower than a CPU core and do not have the features needed for modern operating systems, they are not appropriate for performing most of the processing in everyday computing. They are most suited to compute-intensive operati*** such as video processing and physics simulati***.

The detailed answer: GPGPU is still a relatively new concept. GPUs were initially used for rendering graphics only; as technology advanced, the large number of cores in GPUs relative to CPUs was exploited by developing computational capabilities for GPUs so that they can process many parallel streams of data simultaneously, no matter what that data may be. While GPUs can have hundreds or even thousands of stream processors, they each run slower than a CPU core and have fewer features (even if they areTuring complete and can be programmed to run any program a CPU can run). Features missing from GPUs include interrupts and virtual memory, which are required to implement a modern operating system.

In other words, CPUs and GPUs have significantly different architectures that make them better suited to different tasks. A GPU can handle large amounts of data in many streams, performing relatively simple operati*** on them, but is ill-suited to heavy or complex processing on a single or few streams of data. A CPU is much faster on a per-core basis (in terms of instructi*** per second) and can perform complex operati*** on a single or few streams of data more easily, but cannot efficiently handle many streams simultaneously.

As a result, GPUs are not suited to handle tasks that do not significantly benefit from or cannot be parallelized, including many common c***umer applicati*** such as word processors. Furthermore, GPUs use a fundamentally different architecture; one would have to program an application specifically for a GPU for it to work, and significantly different techniques are required to program GPUs. These different techniques include new programming languages, modificati*** to existing languages, and new programming paradigms that are better suited to expressing a computation as a parallel operation to be performed by many stream processors. For more information on the techniques needed to program GPUs, see the Wikipedia articles on stream processing and parallel computing.

Modern GPUs are capable of performing vector operati*** and floating-point arithmetic, with the latest cards capable of manipulating double-precision floating-point numbers. Frameworks such as CUDA and OpenCL enable programs to be written for GPUs, and the nature of GPUs make them most suited to highly parallelizable operati***, such as in scientific computing, where a series of specialized GPU compute cards can be a viable replacement for a **all compute cluster as in NVIDIA Tesla Personal Supercomputers. C***umers with modern GPUs who are experienced with Folding@home can use them to contribute with GPU clients, which can perform protein folding simulati*** at very high speeds and contribute more work to the project (be sure to read the FAQs first, especially those related to GPUs). GPUs can also enable better physics simulation in video games using PhysX, accelerate video encoding and decoding, and perform other compute-intensive tasks. It is these types of tasks that GPUs are most suited to performing.

AMD is pioneering a processor design called the Accelerated Processing Unit (APU) which combines conventional x86 CPU cores with GPUs. This could allow the CPU and GPU components to work together and improve performance on systems with limited space for separate components. As technology continues to advance, we will see an increasing degree of convergence of these once-separate parts. However, many tasks performed by PC operating systems and applicati*** are still better suited to CPUs, and much work is needed to accelerate a program using a GPU. Since so much existing software use the x86 architecture, and because GPUs require different programming techniques and are missing several important features needed for operating systems, a general transition from CPU to GPU for everyday computing is extremely difficult.

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