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How are GPUs more faster then CPUs? I've read articles that talk about how GPU's are much faster in breaking passwords than CPUs. If thats the case then why can't CPUs be designed in the same way as GPUs to be even in speed?
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High Data Throughput: a GPU consist of hundreds of cores performing the same operation on multiple data items in parallel. Because of that, a GPU can push vast volumes of processed data through a workload, speeding up specific tasks beyond what a CPU can handle.
Less Powerful CoresAlthough GPUs have many more cores, they are less powerful than their CPU counterparts in terms of clock speed. GPU cores also have less diverse, but more specialized instruction sets. This is not necessarily a bad thing, since GPUs are very efficient for a small set of specific tasks.
The GPU is a processor that is made up of many smaller and more specialized cores. By working together, the cores deliver massive performance when a processing task can be divided up and processed across many cores.
GPU vs CPU Performance in Deep Learning Models CPUs are everywhere and can serve as more cost-effective options for running AI-based solutions compared to GPUs. However, finding models that are both accurate and can run efficiently on CPUs can be a challenge. Generally speaking, GPUs are 3X faster than CPUs.
GPU get their speed for a cost. A single GPU core actually works much slower than a single CPU core. For example, Fermi GTX 580 has a core clock of 772MHz. You wouldn't want your CPU with such a low core clock nowadays... The GPU however has several cores (up to 16) each operating in a 32-wide SIMD mode. That brings 500 operations done in parallel. Common CPUs however have up to 4 or 8 cores and can operate in 4-wide SIMD which gives much lower parallelism.
Certain types of algorithms (graphics processing, linear algebra, video encoding, etc...) can be easily parallelized on such a huge number of cores. Breaking passwords falls into that category. Other algorithms however are really hard to parallelize. There is ongoing research in this area... Those algorithms would perform really badly if they were run on the GPU.
The CPU companies are now trying to approach the GPU parallelism without sacrificing the capability of running single-threaded programs. But the task is not an easy one. The Larabee project (currently abandoned) is a good example of the problems. Intel has been working on it for years but it is still not available on the market.
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GPUs are designed with one goal in mind: process graphics really fast. Since this is the only concern they have, there have been some specialized optimizations in place that allow for certain calculations to go a LOT faster than they would in a traditional processor.
In the case of password cracking (or the molecular dynamic "folding at home" project) what has happened is that programmers have found ways of leveraging these optimized processes to do things like crunch passwords at a faster rate.
Your standard CPU has to do a lot more different calculation and processing types that what graphics processors do, so they can't be optimized in a similar manner.
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