CPU vs GPU vs DPU vs TPU vs APU
CPU vs GPU
- CPU: Fewer cores, but each core is much faster and much more capable; great at sequential tasks
- GPU: More cores, but each core is much slower and “dumber”; great for parallel tasks
CPU is for general-purpose computing, the GPU is for accelerated computing, and the DPU, which moves data around the data center, does data processing.
DPUs, or data processing units, a new class of programmable processor and will join CPUs and GPUs as one of the three pillars of computing.
A DPU generally contains a CPU, NIC and programmable data acceleration engines. DPUs have been increasingly used in data centers and supercomputers.
GPU: parallel processing capabilities make them ideal for accelerated computing tasks of all kinds.
A DPU is a system on a chip, or SoC. The DPU can be used as a stand-alone embedded processor. But it’s more often incorporated into a SmartNIC, a network interface controller used as a critical component in a next-generation server.
functions: Data packet parsing, bypass the CPU and feed networked data directly to GPUs, Network virtualization
APU - Associative Process Unit
Tensor Processing Unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google. TPUs are designed for a high volume of low precision computation (e.g. as little as 8-bit precision)[2] with more input/output operations per joule, without hardware for rasterisation/texture mapping.
using DPUs to accelerate network functions and secure workloads.