Kāpēc mēs joprojām izmantojam CPU nevis GPU?

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Kāpēc mēs joprojām izmantojam CPU nevis GPU?
Kāpēc mēs joprojām izmantojam CPU nevis GPU?

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Arvien vairāk GPU tiek izmantoti tādiem grafiskiem uzdevumiem kā riska aprēķini, šķidruma dinamikas aprēķini un seismiskā analīze. Kas mūs apturēs no GPU vadītu ierīču pieņemšanas?
Arvien vairāk GPU tiek izmantoti tādiem grafiskiem uzdevumiem kā riska aprēķini, šķidruma dinamikas aprēķini un seismiskā analīze. Kas mūs apturēs no GPU vadītu ierīču pieņemšanas?

Šodienas jautājumu un atbilžu sesija mums priecājas par SuperUser - Stack Exchange dalību, Q & A tīmekļa vietņu kopienas diskusiju grupu.


SuperUser lasītājs Ell turpina sekot līdzi tehnoloģiju jaunumiem, un ir interesanti, kāpēc mēs neizmantojam vairāk uz GPU balstītu sistēmu:

Man šķiet, ka šodienas GPU veic daudz aprēķinu. Acīmredzot grafika tiek veikta tur, bet izmantojot CUDA un tamlīdzīgus, AI, maiņas algoritmi (domā Bitcoins) un citi tiek veikti arī GPU. Kāpēc mēs nevaram vienkārši atbrīvoties no CPU un izmantot pašu GPU? Kas padara GPU tik daudz ātrāk kā CPU?

Kāpēc patiešām? Kas padara CPU unikālu?


SuperUser ieguldītājs DragonLord piedāvā labi atbalstītu pārskatu par atšķirībām starp GPU un CPU:

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 operations such as video processing and physics simulations.

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 operations 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 instructions per second) and can perform complex operations 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 consumer applications 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, modifications 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 operations 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 operations, such as in scientific computing, where a series of specialized GPU compute cards can be a viable replacement for a small compute cluster as in NVIDIA Tesla Personal Supercomputers. Consumers with modern GPUs who are experienced with Folding@home can use them to contribute with GPU clients, which can perform protein folding simulations 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 applications 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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