Efficiency of Parallel Computing in High-Performance Applications in Germany

Purpose: The purpose of this article was to analyze efficiency of parallel computing in high-performance applications in Germany. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: In Germany, parallel computing has improved efficiency in high-performance applications, reducing processing times by 35-40%. Energy-efficient architectures cut energy use by 20-25%, and hybrid systems optimize resource utilization. However, challenges like communication overhead and scalability remain, requiring further advancements in algorithms and system design. Unique Contribution to Theory, Practice and Policy: Amdahl’s law, gustafson’s law & the roofline model may be used to anchor future studies on the analyze efficiency of parallel computing in high-performance applications in Germany. Organizations should invest in state-of-the-art parallel computing infrastructures and adopt emerging frameworks and libraries that optimize algorithm performance across distributed systems. Governments and industry bodies should promote funding for research and development in parallel computing and establish standards that facilitate interoperability and scalability across high-performance platforms.