CPUs have multiple cores and are suited for a variety of tasks requiring low latency, while GPUs have many smaller cores optimized for parallel processing. GPUs can perform thousands of basic operations simultaneously. Though CPUs and GPUs both process data, CPUs are designed for serial tasks and general computing while GPUs are specialized for graphics processing and parallel tasks like those in AI and supercomputing. Efficient parallel processing is what allows GPUs to outperform CPUs for certain applications.

2. What Is a CPU?
Constructed from millions of transistors, the CPU can
have multiple processing cores and is commonly
referred to as the brain of the computer. It is essential to
all modern computing systems as it executes the
commands and processes needed for your computer
and operating system. The CPU is also important in
determining how fast programs can run, from surfing
the web to building spreadsheets.

3. What Is a GPU?
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. The clock speed of a GPU may be
lower than modern CPUs (normally in the range of 500-800 MHz), but the
number of cores on each chip is much denser. This is one of the most distinct
differences between a graphics card vs CPU. This allows a GPU to perform a
lot of basic tasks at the same time.

5. Difference Between a CPU and GPU?
CPUs and GPUs have a lot in common. Both are critical computing
engines. Both are silicon-based microprocessors. And both handle data.
But CPUs and GPUs have different architectures and are built for
different purposes.
The CPU is suited to a wide variety of workloads, especially those for
which latency or per-core performance are important. A powerful
execution engine, the CPU focuses its smaller number of cores on
individual tasks and on getting things done quickly. This makes it
uniquely well equipped for jobs ranging from serial computing to running

6. GPUs: Key to AI, Computer Vision, Supercomputing and
More
Over the past decade that’s proven key to a growing range of
applications.
GPUs perform much more work for every unit of energy than
CPUs. That makes them key to supercomputers that would
otherwise push past the limits of today’s electrical grids.
In AI, GPUs have become key to a technology called “deep
learning.” Deep learning pours vast quantities of data through
neural networks, training them to perform tasks too complicated

9. Central Processing Unit Graphics Processing Unit
Several cores Many cores
Low latency High throughput
Good for serial processing Good for parallel processing
Can do a handful of operations at once Can do thousands of operations at once

11. Why Have Two Different Processor Types?
Everyone is somewhat familiar with CPUs. Known as the “brain” of a computer, they
are composed of millions upon millions of tiny transistors with multiple “cores.” It is
critical for handling the main processing functions of a computer. Actions like running
the operating system and applications would not be possible without it. The CPU is
also what determines the general speed of a computer.
GPUs are more specialized in nature. Originally designed to help with 3D rendering,
they can do more processing in parallel. This is perfect for use in graphic-intensive
applications that rely on displaying dynamic content for gaming, or
compressing/decompressing streaming videos. GPUs are also being used in many
other areas beyond rendering and image processing, like Artificial
Intelligence and Bitcoin mining.
The main difference between a CPU and a GPU is how they process the instructions
given to them. In human terms, you could say that a CPU is the master of taking on
one task at a time, whereas a GPU can take on many tasks at once. While there are
some that work better doing things in sequential order, others can multitask.

12. Summary
The CPU is responsible for the serial processing while the GPU is
responsible for the parallel processing of tasks. Thus for the high
performance of the computer both CPU and GPU are required.
This will make parallel portions of code run on GPU and serial
portions on the CPU.
If seen individually both have their own limitations. A CPU has
heavy-weight instruction sets, slow switch latency, and defy
Moore’s law. While GPU has less powerful cores, can focus on
only one task at a time, has limited APIs.

13. THANK YOU
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