Many applications are increasingly becoming I/O-bound. To improve scalability, analytical models of parallel I/O performance are often consulted to determine possible I/O optimizations. However, I/O performance modeling has predominantly focused on applications that directly issue I/O requests to a parallel file system or a local storage device. These I/O models are not directly usable by applications that access data through standardized I/O libraries, such as HDF5, FITS, and NetCDF, because a single I/O request to an object can trigger a cascade of I/O operations to different storage blocks. The I/O performance characteristics of applications that rely on these libraries is a complex function of the underlying data storage model, user-configurable parameters and object-level access patterns. As a consequence, I/O optimization is predominantly an ad-hoc process that is performed by application developers, who are often domain scientists with limited desire to delve into nuances of the storage hierarchy of modern computers.This paper presents an analytical cost model to predict the end-to-end execution time of applications that perform I/O through established array management libraries. The paper focuses on the HDF5 and Zarr array libraries, as examples of I/O libraries with radically different storage models: HDF5 stores every object in one file, while Zarr creates multiple files to store different objects. We find that accessing array objects via these I/O libraries introduces new overheads and optimizations. Specifically, in addition to I/O time, it is crucial to model the cost of transforming data to a particular storage layout (memory copy cost), as well as model the benefit of accessing a software cache. We evaluate the model on real applications that process observations (neuroscience) and simulation results (plasma physics). The evaluation on three HPC clusters reveals that I/O accounts for as little as 10% of the execution time in some cases, and hence models that only focus on I/O performance cannot accurately capture the performance of applications that use standard array storage libraries. In parallel experiments, our model correctly predicts the fastest storage library between HDF5 and Zarr 94% of the time, in contrast with 70% of the time for a cutting-edge I/O model.
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HintStor: A Framework to Study I/O Hints in Heterogeneous Storage
To bridge the giant semantic gap between applications and modern storage systems, passing a piece of tiny and useful information, called I/O access hints, from upper layers to the storage layer may greatly improve application performance and ease data management in storage systems. This is especially true for heterogeneous storage systems that consist of multiple types of storage devices. Since ingesting external access hints will likely involve laborious modifications of legacy I/O stacks, it is very hard to evaluate the effect and take advantages of access hints. In this article, we design a generic and flexible framework, called HintStor, to quickly play with a set of I/O access hints and evaluate their impacts on heterogeneous storage systems. HintStor provides a new application/user-level interface, a file system plugin, and performs data management with a generic block storage data manager. We demonstrate the flexibility of HintStor by evaluating four types of access hints: file system data classification, stream ID, cloud prefetch, and I/O task scheduling on a Linux platform. The results show that HintStor can execute and evaluate various I/O access hints under different scenarios with minor modifications to the kernel and applications.
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- Award ID(s):
- 1812537
- NSF-PAR ID:
- 10326850
- Date Published:
- Journal Name:
- ACM Transactions on Storage
- Volume:
- 18
- Issue:
- 2
- ISSN:
- 1553-3077
- Page Range / eLocation ID:
- 1 to 24
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3489143
