Lessons Learned from Optimizing the Sunway Storage System for Higher Application IO Performance It is hard for applications to make full utilization of the peak bandwidth of the storage system in highperformance computers because of IO interferences, storage resource misallocations and complex long IO paths. We performed several studies to bridge this gap in the Sunway storage system, which serves the supercomputer Sunway TaihuLight. To locate these issues and connections between them, an endtoend performance monitoring and diagnosis tool was developed to understand IO behaviors of applications and the system. With the help of the tool, we were about to find out the root causes of such performance barriers at the IO forwarding layer and the parallel file system layer. An applicationaware IO forwarding allocation framework was used to address the IO interferences and resource misallocations at the IO forwarding layer. A performanceaware data placement mechanism was proposed to mitigate the impact of IO interferences and performance variations of storage devices in the PFS. Together, applications obtained much better IO performance. During the process, we also proposed a lightweight storage stack to shorten the IO path of applications with N IO pattern. This paper summarizes these studies and presents the lessons learned from the process.

IO Acceleration via MultiTiered Data Buffering and Prefetching Modern HighPerformance Computing (HPC) systems are adding extra layers to the memory and storage hierarchy, named deep memory and storage hierarchy (DMSH), to increase IO performance. New hardware technologies, such as NVMe and SSD, have been introduced in burst buffer installations to reduce the pressure for external storage and boost the burstiness of modern IO systems. The DMSH has demonstrated its strength and potential in practice. However, each layer of DMSH is an independent heterogeneous system and data movement among more layers is significantly more complex even without considering heterogeneity. How to efficiently utilize the DMSH is a subject of research facing the HPC community. Further, accessing data with a highthroughput and lowlatency is more imperative than ever. Data prefetching is a wellknown technique for hiding read latency by requesting data before it is needed to move it from a highlatency medium (e.g., disk) to a lowlatency one (e.g., main memory). However, existing solutions do not consider the new deep memory and storage hierarchy and also suffer from underutilization of prefetching resources and unnecessary evictions. Additionally, existing approaches implement a clientpull model where understanding the applicationxe2x80x99s IO behavior drives prefetching decisions. Moving towards exascale, where machines run multiple applications concurrently by accessing files in a workflow, a more datacentric approach resolves challenges such as cache pollution and redundancy. In this paper, we present the design and implementation of Hermes: a new, heterogeneousaware, multitiered, dynamic, and distributed IO buffering system. Hermes enables, manages, supervises, and, in some sense, extends IO buffering to fully integrate into the DMSH. We introduce three novel data placement policies to efficiently utilize all layers and we present three novel techniques to perform memory, metadata, and communication management in hierarchical buffering systems. Additionally, we demonstrate the benefits of a truly hierarchical data prefetcher that adopts a serverpush approach to data prefetching. Our evaluation shows that, in addition to automatic data movement through the hierarchy, Hermes can significantly accelerate IO and outperforms by more than 2x stateoftheart buffering platforms. Lastly, results show 10%xe2x80x9335% performance gains over existing prefetchers and over 50% when compared to systems with no prefetching.