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CPU vs. GPU | Best Use Cases For Each
Summary
The article discusses the differences between CPUs and GPUs, and the best use cases for each. CPUs are better suited for general computing tasks, while GPUs are better for handling large amounts of data in parallel. However, GPUs are more expensive than CPUs and are not as good at multitasking.
Q&As
What are the differences between CPU and GPU?
CPU (central processing unit) is a generalized processor that is designed to carry out a wide variety of tasks. GPU (graphics processing unit) is a specialized processing unit with enhanced mathematical computation capability, ideal for computer graphics and machine-learning tasks.
What are the advantages and disadvantages of a CPU?
Some of the advantages of CPU architecture include the following: flexibility, contextual power, precision, access to memory, cost and availability. Some of the disadvantages of CPU architecture include the following: parallel processing, slow evolution, compatibility.
What are the advantages and disadvantages of a GPU?
Some of the advantages of GPU architecture include the following: high data throughput, massive parallel computing. Some of the disadvantages of GPU architecture include the following: multitasking, cost, power and complexity.
What are the best use cases for each?
The best use cases for a CPU are general computing tasks, while the best use cases for a GPU are computer graphics, bitcoin mining, machine learning, and analytics.
How does WekaIO support software development using GPUs?
WekaIO supports software development using GPUs to maximize performance. Some packages include using GPUs specifically for deep-learning algorithms and data mining.
AI Comments
👍 This is a great article that explains the difference between CPU and GPU.
👎 This article is too technical and difficult to understand.
AI Discussion
Me: It's about the differences between CPUs and GPUs.
Friend: Oh, that's interesting. I didn't know that GPUs were specialized for graphics processing.
Me: Yeah, I found it interesting too. I didn't know that GPUs could be used for other things like Bitcoin mining and machine learning.
Friend: Yeah, I've heard of people using GPUs for those things. I didn't know that they were better suited for those tasks than CPUs.
Action items
- Learn more about high-performance computing and how it can benefit your organization.
- Consider implementing a GPU-accelerated environment to maximize performance for specific workloads.
- Contact WekaIO to learn more about how our software can help you leverage GPUs for high-performance computing.
Technical terms
- CPU
- central processing unit; the "brain" of a computer that handles all computation
- GPU
- graphics processing unit; a specialized processor designed for computer graphics and machine-learning tasks
- Cache
- super-fast memory built either within the CPU or in CPU-specific motherboards to facilitate quick access to data the CPU is currently using
- L1, L2, L3 cache
- levels of cache memory, with L1 being the fastest and L3 the slowest
- MMU
- memory management unit; controls data movement between the CPU and RAM during the instruction cycle
- CPU clock
- determines the frequency at which the CPU can generate electrical pulses, its primary way of processing and transmitting data
- Flexibility
- the ability of a CPU to handle a variety of tasks outside of graphics processing
- Contextual power
- the ability of a CPU to outperform a GPU in specific situations
- Precision
- the ability of a CPU to work on mid-range mathematical equations with a higher level of precision
- Access to memory
- the ability of a CPU to handle a larger set of linear instructions and, hence, more complex system and computational operations
- Cost and availability
- the fact that CPUs are more readily available and cost-effective for consumer and enterprise use
- Parallel processing
- the ability of a GPU to handle large amounts of parallel computing and data throughput
- Massive parallel computing
- the ability of a GPU to excel in extensive calculations with numerous similar operations
- Bitcoin mining
- the process of using computational power to solve complex cryptographic hashes in order to earn bitcoins
- Machine learning
- the process of using algorithms to parse data, learn from it, and make predictions about it
- Analytics
- the process of examining data in order to draw conclusions from it
- Data science
- the process of using data to solve problems
- Multitasking
- the ability of a CPU to switch rapidly between multiple tasks
- Compatibility
- the ability of a CPU to work with different types of hardware and software