In high-performance computing applications, a high-level I/O call will trigger activities on a multitude of hardware components. These are massively parallel systems supported by huge storage systems and internal software layers. Their complex interplay currently makes it impossible to identify the causes for and the locations of I/O bottlenecks. Existing tools indicate when a bottleneck occurs but provide little guidance in identifying the cause or improving the situation.We have thus initiated Scalable I/O for Extreme Performance to find solutions for this problem. To achieve this goal in SIOX, we will build a system to record access information on all layers and components, to recognize access patterns, and to characterize the I/O system. The system will ultimately be able to recognize the causes of the I/O bottlenecks and propose optimizations for the I/O middleware that can improve I/O performance, such as throughput rate and latency. Furthermore, the SIOX system will be able to support decision making while planning new I/O systems.In this paper, we introduce the SIOX system and describe its current status: We first outline our approach for collecting the required access information. We then provide the architectural concept, the methods for reconstructing the I/O path and an excerpt of the interface for data collection. This paper focuses especially on the architecture, which collects and combines the relevant access information along the I/O path, and which is responsible for the efficient transfer of this information. An abstract modelling approach allows us to better understand the complexity of the analysis of the I/O activities on parallel computing systems, and an abstract interface allows us to adapt the SIOX system to various HPC file systems.
|Journal||Software-Intensive Cyber-Physical Systems|
|Publication status||Published - 1 May 2013|
- I/O analysis, I/O path, Causality tree