Serverless computing, with its ease of management, auto-scaling, and cost-effectiveness, is widely adopted by deep learning (DL) applications. DL workloads, especially with large language models, require substantial GPU resources to ensure QoS. However, it is prone to produce GPU fragments (e.g., 15%-94%) in serverless DL systems due to the dynamicity of workloads and coarse-grained static GPU allocation mechanisms, gradually eroding the profits offered by serverless elasticity. Different from classical serverless systems that only scale horizontally, we present introspective elasticity (IE), a fine-grained and adaptive two-dimensional co-scaling mechanism to support GPU resourcing-on-demand for serverless DL tasks. Based on this insight, we build Dilu, a cross-layer and GPU-based serverless DL system with IE support. First, Dilu provides multi-factor profiling for DL tasks with efficient pruning search methods. Second, Dilu adheres to the resourcing-complementary principles in scheduling to improve GPU utilization with QoS guarantees. Third, Dilu adopts an adaptive 2D co-scaling method to enhance the elasticity of GPU provisioning in real time. Evaluations show that it can dynamically adjust the resourcing of various DL functions with low GPU fragmentation (10%-46% GPU defragmentation), high throughput (up to 1.8× inference and 1.1× training throughput increment) and QoS guarantees (11%-71% violation rate reduction), compared to the SOTA baselines.

Serverless workflows are characterized as multi-stage computing, while downstream functions require accessing intermediate states or the output of upstream functions for running. The workflow's performance can be easily affected due to the inefficiency of data access. Studies accelerate data access with various policies, such as direct and indirect methods. However, these methods may fail due to various limitations such as resource availability.

Running HPC in the cloud has gained more and more practice. As a new cloud paradigm, serverless is highly attractive for HPC service providers due to its distinctive benefits such as scalability. However, it is difficult for serverless to meet the demands of MPI programming and running, resulting in that MPI programs cannot scale with serverless functions. We introduce a serverless parallel function model to solve the problems. It divides parallelism at function and worker levels to bridge gaps in programming and running between serverless and MPI. Then we present the SMPI framework atop the model. For programming, SMPI redefines the function generation pipeline for parallel functions to prepare metadata for MPI parallel functions. For running, SMPI employs the parallel function gateway and scheduler to realize parallel function invocation and instantiation for MPI parallel functions. It is implemented and evaluated with OpenFaaS. Experiments show that SMPI supports MPI programming and running in a complete serverless manner. Compared to server-centric methods, it reduces efforts on cluster maintenance, provides scalable serverless MPI computing with competitive performance (0.559–1.048s slower of start-up time, and 0.145s–0.945s slower of computing time than best-behaved baseline), and is potential to scale on multiple clusters for higher scalability.