(1) Computation-intensive programs. The so-called Compute-intensive program is that most of its running time is spent on register operations. The speed of registers is comparable to that of processors. There is almost no delay in reading and writing data from registers. For comparison, the latency for reading memory is about a few hundred clock cycles; the speed of reading a hard disk is not stated, and even an SSD is really too slow.
(2) Easy to parallel programs. GPU is actually a SIMD (Single InstrucTon MulTiple Data) architecture. He has hundreds of cores, and each core can do the same thing at the same time.
To meet the above two points, you can use the GPU to do the operation. However, you still have to use Open CL to write programs that can run on the GPU. This is also very troublesome. And the architecture of the GPU is rather special. It takes a lot of time to write efficient programs. A long time ago, probably around 2000, the graphics card was also called a graphics accelerator card. What is generally called an accelerator card is not a core component, and is similar to the M7 coprocessor used by Apple. This kind of thing is better, neither is nor is it not, as long as there is a basic graphic output you can access the monitor. Until then, only a few high-end workstations and home consoles could see such a separate graphics processor. Later, with the popularity of PCs, the development of games and market hegemony such as Windows appeared, simplifying the workload of graphics hardware manufacturers. Graphics processors, or graphics cards, gradually became popular.
To understand the difference between GPU and CPU, you need to understand what the GPU is designed to do. Modern GPU functionality covers all aspects of graphic display. We only take one of the simplest directions as an example.
Everyone may have seen the above picture, this is a test of the old version of the Direct X band, is a rotating cube. Showing a cube like this takes a lot of steps. Let's think about it first. Imagine he is a wireframe with no side "X" image. Simplify it a little, there is no connection, it is eight points (the cube has eight vertices). Then the question is simplified into how to make these eight points turn. First of all, when you create this cube, you must have the coordinates of the eight vertices. The coordinates are represented by vectors, and therefore at least a three-dimensional vector. Then "rotate" this transformation, which is represented by a matrix in linear algebra. Vector rotation is to multiply this matrix by vector. To turn these eight points is to multiply the vector and matrix eight times. This kind of calculation is not complicated. It is nothing more than a few product additions, which means that the calculation is relatively large. Eight points will count eight times, and 2000 points will count 2,000 times. This is part of the GPU work, vertex transformations, and this is the simplest part. There are still a lot of more trouble than that.
Most of the GPU's work is like this. It is computationally intensive, but it has no technical content and it is repeated many times. Just as if you have a job that needs to count hundreds of millions of times less than one hundred, plus, minus, multiply and divide, the best way is to hire dozens of elementary school students to count together, and one person to count them. In any case, these calculations have no technical content and are purely physical. The CPU is like an old professor. The integral differential will be calculated. It means that the salary is high. An old professor is a top 20 primary school students. If you were Foxconn, which one would you hire? The GPU is like this, using many simple computing units to complete a large number of computing tasks, pure human-sea tactics. This strategy is based on the premise that the work of Pupil A and Pupil B is not dependent on each other and is independent of each other. Many problems involving large numbers of calculations have such characteristics, such as cracking passwords, mining, and many graphics calculations. These calculations can be broken down into multiple identical simple tasks, each of which can be assigned to a primary school student. But there are still some tasks that involve "flow" issues. For example, when you go to a blind date, both parties look pleasing to continue to develop. There is no way you haven't met yet. There's someone who's got the cards. This more complicated issue is done by the CPU.
All in all, the CPU and GPU are different because of the tasks they were originally used to handle, so there is a big difference in design. Some tasks are similar to the ones that the GPU was originally used to solve, so use the GPU to calculate it. The speed of the GPU's computing depends on how many elementary school students are employed. The CPU's speed of operation depends on how powerful the professor has been. Professor's ability to handle complex tasks is to squash primary school students, but for less complex tasks, it still can not withstand more people. Of course, the current GPU can also do some more complicated work, which is equivalent to upgrading to junior high school students. But also need the CPU to feed the data to the mouth in order to start working, it still depends on the CPU to control.
As for the simple task of how to divide mining and cracking passwords into elementary school students, it is the work of programmers. So whoever later tells you that the programmer's job is physical work, you can just pump him.
Brake Disc For MAZDA
Mazda Brake Disc,Mazda Auto Brake Disc,Mazda Car Brake Disc,Mazda Automobile Brake Disc
Zhoushan Shenying Filter Manufacture Co., Ltd. , https://www.renkenfilter.com