Papers | Parallel Computing
2023
Alberto Riccardo Martinelli, Massimo Torquati, Marco Aldinucci, Iacopo Colonnelli, Barbara Cantalupo
CAPIO: a Middleware for Transparent I/O Streaming in Data-Intensive Workflows Proceedings Article
In: 2023 IEEE 30th International Conference on High Performance Computing, Data, and Analytics (HiPC), IEEE, Goa, India, 2023.
Abstract | Links | BibTeX | Tags: admire, capio, eupex, icsc
@inproceedings{23:hipc:capio,
title = {CAPIO: a Middleware for Transparent I/O Streaming in Data-Intensive Workflows},
author = {Alberto Riccardo Martinelli and Massimo Torquati and Marco Aldinucci and Iacopo Colonnelli and Barbara Cantalupo},
url = {https://iris.unito.it/retrieve/27380f37-0978-409e-a9d8-2b5e95a4bb85/CAPIO-HiPC23-preprint.pdf},
doi = {10.1109/HiPC58850.2023.00031},
year = {2023},
date = {2023-12-01},
booktitle = {2023 IEEE 30th International Conference on High Performance Computing, Data, and Analytics (HiPC)},
publisher = {IEEE},
address = {Goa, India},
abstract = {With the increasing amount of digital data available for analysis and simulation, the class of I/O-intensive HPC workflows is fated to quickly expand, further exacerbating the performance gap between computing, memory, and storage technologies. This paper introduces CAPIO (Cross-Application Programmable I/O), a middleware capable of injecting I/O streaming capabilities into file-based workflows, improving the computation-I/O overlap without the need to change the application code. The contribution is twofold: 1) at design time, a new I/O coordination language allows users to annotate workflow data dependencies with synchronization semantics; 2) at run time, a user-space middleware automatically and transparently to the user turns a workflow batch execution into a streaming execution according to the semantics expressed in the configuration file. CAPIO has been tested on synthetic benchmarks simulating typical workflow I/O patterns and two real-world workflows. Experiments show that CAPIO reduces the execution time by 10% to 66% for data-intensive workflows that use the file system as a communication medium.},
keywords = {admire, capio, eupex, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
With the increasing amount of digital data available for analysis and simulation, the class of I/O-intensive HPC workflows is fated to quickly expand, further exacerbating the performance gap between computing, memory, and storage technologies. This paper introduces CAPIO (Cross-Application Programmable I/O), a middleware capable of injecting I/O streaming capabilities into file-based workflows, improving the computation-I/O overlap without the need to change the application code. The contribution is twofold: 1) at design time, a new I/O coordination language allows users to annotate workflow data dependencies with synchronization semantics; 2) at run time, a user-space middleware automatically and transparently to the user turns a workflow batch execution into a streaming execution according to the semantics expressed in the configuration file. CAPIO has been tested on synthetic benchmarks simulating typical workflow I/O patterns and two real-world workflows. Experiments show that CAPIO reduces the execution time by 10% to 66% for data-intensive workflows that use the file system as a communication medium.