Modern computationally-intensive applications often operate under time
constraints, necessitating acceleration methods and distribution of
computational workloads across multiple entities. However, the outcome is
either achieved within the desired timeline or not, and in the latter case,
valuable resources are wasted. In this paper, we introduce solutions for
layered-resolution computation. These solutions allow lower-resolution results
to be obtained at an earlier stage than the final result. This innovation
notably enhances the deadline-based systems, as if a computational job is
terminated due to time constraints, an approximate version of the final result
can still be generated. Moreover, in certain operational regimes, a
high-resolution result might be unnecessary, because the low-resolution result
may already deviate significantly from the decision threshold, for example in
AI-based decision-making systems. Therefore, operators can decide whether
higher resolution is needed or not based on intermediate results, enabling
computations with adaptive resolution. We present our framework for two
critical and computationally demanding jobs: distributed matrix multiplication
(linear) and model inference in machine learning (nonlinear). Our theoretical
and empirical results demonstrate that the execution delay for the first
resolution is significantly shorter than that for the final resolution, while
maintaining overall complexity comparable to the conventional one-shot
approach. Our experiments further illustrate how the layering feature increases
the likelihood of meeting deadlines and enables adaptability and transparency
in massive, large-scale computations.

我们介绍了一种分层分辨率计算的解决方案，使得在时间约束下可以在较早的阶段获得低分辨率的结果，提高了基于截止日期的系统的效果；并且我们的实验结果显示，分层特性增加了满足截止日期的可能性，使得大规模计算具备了适应性和透明度。