CPU vs. GPU

  • The CPU (Central Processing Unit) is the general-purpose processor responsible for handling most computing tasks in a system.
  • The GPU (Graphics Processing Unit) is now widely used for accelerating scientific and high-performance computing tasks.
  • GPU computing uses the GPU as a co-processor to accelerate CPU tasks by offloading compute-intensive, time-consuming portions of code onto the GPU.
CPU GPU
4–8 cores (on laptop/workstation PC) 100s–1000s of cores
Up to 48 cores (on Rivanna) High throughput
Low latency Good for parallel processing
Good for serial processing Breaks jobs into separate tasks to process simultaneously
Quickly process interactive tasks Requires additional software to convert CPU functions to GPU functions for parallel execution
Traditionally sequential execution Typically parallel execution

Metaphor: GPU vs CPU

CPUs are like scooters: simple, efficient, and great for quickly handling one task or a few tasks at a time.

GPUs are like sports cars: powerful, fast, and ideal for processing many tasks in parallel.

When you have many packages to deliver, a fleet of scooters (parallel delivery) can outperform one fast car — just like GPUs excel at parallel processing.

When you need to deliver one package as fast as possible, a sports car is the better choice — similar to how CPUs can be better for low-latency or interactive tasks.

The best option depends on your use case!

AlphaFold is designed for GPU computing, making it an ideal workload for systems like Rivanna that support GPU acceleration.

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