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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Henosis: workload-driven small array consolidation and placement for HDF5 applications on heterogeneous data stores
Scientific data analysis pipelines face scalability bottlenecks when processing massive datasets that consist of millions of small files. Such datasets commonly arise in domains as diverse as detecting supernovae and post-processing computational fluid dynamics simulations. Furthermore, applications often use inference frameworks such as TensorFlow and PyTorch whose naive I/O methods exacerbate I/O bottlenecks. One solution is to use scientific file formats, such as HDF5 and FITS, to organize small arrays in one big file. However, storing everything in one file does not fully leverage the heterogeneous data storage capabilities of modern clusters.
This paper presents Henosis, a system that intercepts data accesses inside the HDF5 library and transparently redirects I/O to the in-memory Redis object store or the disk-based TileDB array store. During this process, Henosis consolidates small arrays into bigger chunks and intelligently places them in data stores. A critical research aspect of Henosis is that it formulates object consolidation and data placement as a single optimization problem. Henosis carefully constructs a graph to capture the I/O activity of a workload and produces an initial solution to the optimization problem using graph partitioning. Henosis then refines the solution using a hill-climbing algorithm which migrates arrays between data stores to minimize I/O cost. The evaluation on two real scientific data analysis pipelines shows that consolidation with Henosis makes I/O 300× faster than directly reading small arrays from TileDB and 3.5× faster than workload-oblivious consolidation methods. Moreover, jointly optimizing consolidation and placement in Henosis makes I/O 1.7× faster than strategies that perform consolidation and placement independently.
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- Award ID(s):
- 1816577
- NSF-PAR ID:
- 10104370
- Date Published:
- Journal Name:
- Proceedings of the 33rd ACM International Conference on Supercomputing - ICS '19
- Page Range / eLocation ID:
- 392 to 402
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3330345.3330380
