Introducing the Task-Aware Storage I/O (TASIO) Library

11/27/2020
by   Aleix Roca Nonell, et al.
0

Task-based programming models are excellent tools to parallelize and seamlessly load balance an application workload. However, the integration of I/O intensive applications and task-based programming models is lacking. Typically, I/O operations stall the requesting thread until the data is serviced by the backing device. Because the core where the thread was running becomes idle, it should be possible to overlap the data query operation with either computation workloads or even more I/O operations. Nonetheless, overlapping I/O tasks with other tasks entails an extra degree of complexity currently not managed by programming models' runtimes. In this work, we focus on integrating storage I/O into the tasking model by introducing the Task-Aware Storage I/O (TASIO) library. We test TASIO extensively with a custom benchmark for a number of configurations and conclude that it is able to achieve speedups up to 2x depending on the workload, although it might lead to slowdowns if not used with the right settings.

READ FULL TEXT
research
11/02/2021

Towards Enabling I/O Awareness in Task-based Programming Models

Storage systems have not kept the same technology improvement rate as co...
research
01/10/2019

Integrating Blocking and Non-Blocking MPI Primitives with Task-Based Programming Models

In this paper we present the Task-Aware MPI library (TAMPI) that integra...
research
06/29/2022

AAE: An Active Auto-Estimator for Improving Graph Storage

Nowadays, graph becomes an increasingly popular model in many real appli...
research
08/14/2020

Consideration for effectively handling parallel workloads on public cloud system

We retrieved and analyzed parallel storage workloads of the FUJITSU K5 c...
research
05/15/2022

Sibyl: Adaptive and Extensible Data Placement in Hybrid Storage Systems Using Online Reinforcement Learning

Hybrid storage systems (HSS) use multiple different storage devices to p...
research
10/26/2021

Endure: A Robust Tuning Paradigm for LSM Trees Under Workload Uncertainty

Log-Structured Merge trees (LSM trees) are increasingly used as the stor...

Please sign up or login with your details

Forgot password? Click here to reset