title: Advection-Based Sparse Data Management for Visualizing Unsteady Flow.
abstract: When computing integral curves and integral surfaces for large-scale unsteady flow fields, a major bottleneck is the widening gap between data access demands and the available bandwidth (both I/O and in-memory). In this work, we explore a novel advection-based scheme to manage flow field data for both efficiency and scalability. The key is to first partition flow field into blocklets (e.g. cells or very fine-grained blocks of cells), and then (pre)fetch and manage blocklets on-demand using a parallel key-value store. The benefits are (1) greatly increasing the scale of local-range analysis (e.g. source-destination queries, streak surface generation) that can fit within any given limit of hardware resources; (2) improving memory and I/O bandwidth-efficiencies as well as the scalability of naive task-parallel particle advection. We demonstrate our method using a prototype system that works on workstation and also in supercomputing environments. Results show significantly reduced I/O overhead compared to accessing raw flow data, and also high scalability on a supercomputer for a variety of applications.
authors.forename: Hanqi
authors.initials: H
authors.lastname: Guo
authors.forename: Jiang
authors.initials: J
authors.lastname: Zhang
authors.forename: Richen
authors.initials: R
authors.lastname: Liu
authors.forename: Lu
authors.initials: L
authors.lastname: Liu
authors.forename: Xiaoru
authors.initials: X
authors.lastname: Yuan
authors.forename: Jian
authors.initials: J
authors.lastname: Huang
authors.forename: Xiangfei
authors.initials: X
authors.lastname: Meng
authors.forename: Jingshan
authors.initials: J
authors.lastname: Pan