High-Performance Computing (HPC) systems are designed to handle complex computational tasks that require massive processing power. These systems are employed in various domains, including scientific simulations, weather forecasting, molecular dynamics, and data analytics. However, the growing scale and complexity of HPC applications have resulted in substantial challenges in managing data movement between storage devices and computational resources.
I/O operations play a critical role in HPC applications, as they involve reading and writing large volumes of data from storage systems. Efficient I/O is crucial for minimizing data transfer times, reducing storage system bottlenecks, and maximizing the overall performance of HPC workloads. As the gap between computer performance and I/O performance widens, there is a pressing need for advancements in I/O optimization techniques.
This Research Topic addresses the main challenges in optimizing I/O performance for HPC systems. One significant challenge lies in predicting and understanding I/O behaviour to design effective performance optimization strategies. HPC systems generate vast amounts of log data, capturing the I/O behaviour of applications and storage systems. Researchers aim to develop innovative techniques for analysing this data at a large scale, enabling the design and implementation of predictive models that can anticipate I/O requirements and optimize system configurations, accordingly. Another challenge is the autotuning of I/O performance for HPC applications. Due to the diversity of HPC workloads and the heterogeneity of storage systems, it is challenging to develop a one-size-fits-all solution. Research community is focus on developing autotuning mechanisms that dynamically adjust I/O parameters and configurations based on the specific requirements and characteristics of different applications, resulting in improved performance, and reduced I/O bottlenecks.
This article collection is focus on the advancements in I/O performance optimization specifically tailored for HPC systems. While I/O optimization has been a subject of study, the challenges, and requirements specific to HPC applications are demanding specialized approaches.
Topics of interest include, but are not limited to:
• I/O Performance Tuning
• Design and analysis of I/O-intensive applications
• Parallel I/O characterization
• I/O architecture design and evaluation
• Run-time libraries
• Parallel file system design and analysis
Keywords:
Parallel I/O, High-Performance Computing, Performance Optimization, I/O library, Parallel File System.
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
High-Performance Computing (HPC) systems are designed to handle complex computational tasks that require massive processing power. These systems are employed in various domains, including scientific simulations, weather forecasting, molecular dynamics, and data analytics. However, the growing scale and complexity of HPC applications have resulted in substantial challenges in managing data movement between storage devices and computational resources.
I/O operations play a critical role in HPC applications, as they involve reading and writing large volumes of data from storage systems. Efficient I/O is crucial for minimizing data transfer times, reducing storage system bottlenecks, and maximizing the overall performance of HPC workloads. As the gap between computer performance and I/O performance widens, there is a pressing need for advancements in I/O optimization techniques.
This Research Topic addresses the main challenges in optimizing I/O performance for HPC systems. One significant challenge lies in predicting and understanding I/O behaviour to design effective performance optimization strategies. HPC systems generate vast amounts of log data, capturing the I/O behaviour of applications and storage systems. Researchers aim to develop innovative techniques for analysing this data at a large scale, enabling the design and implementation of predictive models that can anticipate I/O requirements and optimize system configurations, accordingly. Another challenge is the autotuning of I/O performance for HPC applications. Due to the diversity of HPC workloads and the heterogeneity of storage systems, it is challenging to develop a one-size-fits-all solution. Research community is focus on developing autotuning mechanisms that dynamically adjust I/O parameters and configurations based on the specific requirements and characteristics of different applications, resulting in improved performance, and reduced I/O bottlenecks.
This article collection is focus on the advancements in I/O performance optimization specifically tailored for HPC systems. While I/O optimization has been a subject of study, the challenges, and requirements specific to HPC applications are demanding specialized approaches.
Topics of interest include, but are not limited to:
• I/O Performance Tuning
• Design and analysis of I/O-intensive applications
• Parallel I/O characterization
• I/O architecture design and evaluation
• Run-time libraries
• Parallel file system design and analysis
Keywords:
Parallel I/O, High-Performance Computing, Performance Optimization, I/O library, Parallel File System.
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.