Fluid: A framework for approximate concurrency via controlled dependency relaxation

In this work, we introduce the Fluid framework, a set of language, compiler and runtime extensions that allow for the expression of regions within which dataflow dependencies can be approximated in a disciplined manner. Our framework allows the eager execution of dependent tasks before their inputs have finalized in order to capitalize on situations where an eagerly-consumed input has a high probability of sufficiently resembling the value or structure of the final value that would have been produced in a conservative/precise execution schedule. We introduce controlled access to the early consumption of intermediate values and provide hooks for user-specified quality assurance mechanisms that can automatically enforce re-execution of eagerly-executed tasks if their output values do not meet heuristic expectations. Our experimental analysis indicates that the fluidized versions of the applications bring 22.2% average execution time improvements, over their original counterparts, under the default values of our fluidization parameters. The Fluid approach is largely orthogonal to approaches that aim to reduce the task effort itself and we show that utilizing the Fluid framework can yield benefits for both originally precise and originally approximate versions of computation.


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Work Title Fluid: A framework for approximate concurrency via controlled dependency relaxation
Open Access
  1. Huaipan Jiang
  2. Haibo Zhang
  3. Xulong Tang
  4. Vineetha Govindaraj
  5. Jack Sampson
  6. Mahmut Taylan Kandemir
  7. Danfeng Zhang
License In Copyright (Rights Reserved)
Work Type Article
Publication Date June 18, 2021
Publisher Identifier (DOI)
  1. https://doi.org/10.1145/3453483.3454042
Deposited September 27, 2022




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Work History

Version 1

  • Created
  • Added pldi21.pdf
  • Added Creator Huaipan Jiang
  • Added Creator Haibo Zhang
  • Added Creator Xulong Tang
  • Added Creator Vineetha Govindaraj
  • Added Creator Jack Sampson
  • Added Creator Mahmut Taylan Kandemir
  • Added Creator Danfeng Zhang
  • Published