CPU vs. GPU Cores Difference

CPU

• CPUs have few strong cores
  

• Suited for serial workloads
  

• Designed for general purpose calculations
  

GPU

• GPUs have thousands of weaker cores
  

• Suited for parallel workloads
  

• Specialize in graphic processing
  

CPU vs. GPU Architecture Difference

CPU

• Low compute density
  

• Complex control logic
  

• Large caches (L1$/L2$, etc.)
  

• Optimized for serial operations
  

• Shallow pipelines (<30 stages)
  

• Low latency tolerance
  

• Newer CPUs have more parallelism
  

GPU

• High compute density
  

• High computations per Memory Access
  

• Built for parallel operations
  

• Deep pipelines (hundreds of stages)
  

• High throughput
  

• High latency tolerance
  

• Newer GPUs have better flow control logic (becoming more CPU-like)
  

HPC Architecture

HPC architecture is not limited to any set number of clusters. Instead, the number of clusters is defined by the processing power necessary to solve a particular problem.

To build a high-performance computing architecture, compute servers are networked together into a “cluster”. Software programs and algorithms are run simultaneously on the servers in the cluster. The cluster is networked to the data storage to capture the output. Together, these components operate seamlessly to complete a diverse set of tasks.

To operate at maximum performance, each component must keep pace with the others. For example, the storage component must be able to feed and ingest data to and from the compute servers as quickly as it is processed. Likewise, the networking components must be able to support the high-speed transportation of data between compute servers and the data storage. If one component cannot keep up with the rest, the performance of the entire HPC infrastructure suffers.

Calculation of Data
HPC calculates large amounts of data using nodes. These nodes contain many CPU/GPU’s, RAM, and very fast drives to crunch a tremendous amount of data. The HPC is a cluster of computers, so instead of a faculty member using only one CPU with a few cores, the HPC nodes provide access to thousands of cores to focus on the problem at hand.

Impact of the Top 10 HPC Trends 2024

AI addresses the challenges in rapid algorithm testing and validation with scalable and cloud computing expands computational capabilities while improving accessibility. Besides, innovations in blockchain and cybersecurity make data processing over HPC networks secure and traceable. Further, edge computing and digital twins enable simulations and analytics of more complex processes. Innovations in distributed computing are overcoming many of the limitations of HPC data centers while thermal management technologies are improving the carbon footprint of HPC. Lastly, quantum algorithms improve the performance of classical HPC systems by providing more efficient ways to solve specific problems.