Distributing and Load Balancing Sparse Fluid Simulations
This paper describes a general algorithm and a system for load balancing sparse fluid simulations. Automatically distributing sparse fluid simulations efficiently is challenging because the computational load varies across the simulation domain and time. A key challenge with load balancing is that o...
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| Veröffentlicht in: | Computer graphics forum Jg. 37; H. 8; S. 35 - 46 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Oxford
Blackwell Publishing Ltd
01.12.2018
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | This paper describes a general algorithm and a system for load balancing sparse fluid simulations. Automatically distributing sparse fluid simulations efficiently is challenging because the computational load varies across the simulation domain and time. A key challenge with load balancing is that optimal decision making requires knowing the fluid distribution across partitions for future time steps, but computing this state for an arbitrary simulation requires running the simulation itself. The key insight of this paper is that it is possible to predict future load by running a speculative low resolution simulation in parallel. We mathematically formulate the problem of load balancing over multiple time steps and present a polynomial time algorithm to compute an approximate solution to it. Our experimental results show that distributing and speculatively load balancing sparse FLIP simulations over 8 nodes speeds them up by 5.3× to 7.9×, and that speculative load balancing generates assignments that perform within 20% of optimal. |
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| AbstractList | This paper describes a general algorithm and a system for load balancing sparse fluid simulations. Automatically distributing sparse fluid simulations efficiently is challenging because the computational load varies across the simulation domain and time. A key challenge with load balancing is that optimal decision making requires knowing the fluid distribution across partitions for future time steps, but computing this state for an arbitrary simulation requires running the simulation itself. The key insight of this paper is that it is possible to predict future load by running a speculative low resolution simulation in parallel. We mathematically formulate the problem of load balancing over multiple time steps and present a polynomial time algorithm to compute an approximate solution to it. Our experimental results show that distributing and speculatively load balancing sparse FLIP simulations over 8 nodes speeds them up by 5.3× to 7.9×, and that speculative load balancing generates assignments that perform within 20% of optimal. |
| Author | Qu, H. Levis, P. Shah, C. Hyde, D. |
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| Copyright | 2018 The Author(s) Computer Graphics Forum © 2018 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2018 The Eurographics Association and John Wiley & Sons Ltd. |
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| SubjectTerms | Algorithms CCS Concepts Computer graphics Computer simulation Computing methodologies → Distributed computing methodologies Computing time Decision making Distributed simulation Distributing Load balancing Network switching Servers Simulation |
| Title | Distributing and Load Balancing Sparse Fluid Simulations |
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