Computers are getting faster. Hard drives store more data. Internet connections are growing ever faster. So why aren’t the things we do on computers keeping pace?
The answer, in part, is that the speed of individual computer chips has largely plateaued. The speed of a computer is ultimately limited by how fast its processor can fetch and execute instructions. Despite years of Moore’s Law-style exponential gains in transistor density, individual chips have only gotten incrementally faster.
One way to get around this is to buy a more expensive processor with more cores. A processor with two cores can theoretically execute twice as many instructions in a given period of time as a single-core processor. In practice, it’s not quite that simple, but more cores can still give you a significant performance boost.
Another way to get more out of your processor is to use parallel processing. Parallel processing is a type of computing where multiple processors work on different parts of a task at the same time.
GPUs, or graphics processing units, are a type of processor that is particularly well-suited to parallel processing. GPUs were originally designed to render graphics for video games and other graphics-intensive applications. But GPUs can also be used for general-purpose computing, and they are often used for machine learning and other data-intensive tasks.
The advantage of GPUs is that they have many cores, and each core is relatively simple. This makes them well-suited to parallel processing, as multiple cores can work on different parts of a task at the same time.
GPUs are also more energy-efficient than general-purpose CPUs. This is because GPUs are designed to be able to perform many computations in parallel, so they can do more work with the same amount of power.
The power of parallel processing and GPU computing is that it can help you get more out of your processor without having to buy a more expensive one. If you are working on a task that can be parallelized, using a GPU can give you a significant performance boost.
There are a few things to keep in mind when using parallel processing and GPU computing. First, not all tasks can be parallelized. If a task can only be done sequentially, then it cannot be parallelized.
Second, even if a task can be parallelized, it may not be worth the effort. If a task can be completed in a reasonable amount of time using a single processor, then it may not be worth the effort to parallelize it.
Third, you need to have a GPU in order to use GPU computing. Most computers do not have a GPU, so you may need to buy one.
Fourth, you need to know how to program in order to use parallel processing and GPU computing. If you are not a programmer, you will need to hire someone who is.
Parallel processing and GPU computing can be a great way to get more out of your processor. But it is not a magic bullet, and it is not always the best solution. If you are considering using parallel processing or GPU computing, make sure to consider the trade-offs carefully.