  Integrating GPUs into serverless computing platforms is crucial for improving
efficiency. However, existing solutions for GPU-enabled serverless computing
platforms face two significant problems due to coarse-grained GPU management:
long setup time and low function throughput.
  To address these issues, we propose SAGE, a GPU serverless framework with
fast setup and high throughput. First, based on the data knowability of GPU
function ahead of actual execution, SAGE first devises the parallelized
function setup mechanism, which parallelizes the data preparation and context
creation. In this way, SAGE achieves fast setup of GPU function
invocations.Second, SAGE further proposes the sharing-based memory management
mechanism, which shares the read-only memory and context memory across multiple
invocations of the same function. The memory sharing mechanism avoids repeated
data preparation and then unnecessary data-loading contention. As a
consequence, the function throughput could be improved. Our experimental
results show that SAGE reduces function duration by 11.3X and improves function
density by 1.22X compared to the state-of-the-art serverless platform.
