What architecture type combines CPUs and GPUs?

What architecture type combines CPUs and GPUs?

• A. Vector architecture
• B. Heterogeneous architecture
• C. Homogeneous architecture
• D. Distributed architecture

Answer: (B)

Heterogeneous architecture is characterized by a combination of different types of processing units, such as CPUs (Central Processing Units) and GPUs (Graphics Processing Units). This architecture leverages the strengths of each type of processor to optimize performance across various workloads, particularly in high-performance computing and big data environments. CPUs are well-suited for general-purpose tasks and complex decision-making processes, while GPUs excel at parallel processing tasks, making them highly effective for tasks that require handling large volumes of data simultaneously, such as machine learning and graphics rendering. By integrating both CPUs and GPUs, heterogeneous architecture can improve overall efficiency and speed up data processing tasks, which is essential in HPC and big data applications. In contrast, vector architecture focuses on processing vector data types and may not inherently include a combination of CPUs and GPUs. Homogeneous architecture consists of identical processors, which would not include the diversity of CPUs and GPUs. Distributed architecture, while involving teamwork across multiple machines, does not necessarily focus on combining CPU and GPU capabilities within individual processing units. Thus, heterogeneous architecture is the most accurate term for the integration of CPUs and GPUs.

Heterogeneous architecture is characterized by a combination of different types of processing units, such as CPUs (Central Processing Units) and GPUs (Graphics Processing Units). This architecture leverages the strengths of each type of processor to optimize performance across various workloads, particularly in high-performance computing and big data environments.

CPUs are well-suited for general-purpose tasks and complex decision-making processes, while GPUs excel at parallel processing tasks, making them highly effective for tasks that require handling large volumes of data simultaneously, such as machine learning and graphics rendering. By integrating both CPUs and GPUs, heterogeneous architecture can improve overall efficiency and speed up data processing tasks, which is essential in HPC and big data applications.

In contrast, vector architecture focuses on processing vector data types and may not inherently include a combination of CPUs and GPUs. Homogeneous architecture consists of identical processors, which would not include the diversity of CPUs and GPUs. Distributed architecture, while involving teamwork across multiple machines, does not necessarily focus on combining CPU and GPU capabilities within individual processing units. Thus, heterogeneous architecture is the most accurate term for the integration of CPUs and GPUs.