On the Acceleration of FaaS Using Remote GPU Virtualization

Abstract

Serverless computing and, in particular, Function as a Service (FaaS) has introduced novel computational approaches with its highly-elastic capabilities, per-millisecond billing and scale-to-zero capacities, thus being of interest for the computing continuum. Services such as AWS Lambda allow efficient execution of event-driven short-lived bursty applications, even if there are limitations in terms of the amount of memory and the lack of GPU support for accelerated execution. To this aim, this paper analyses the suitability of including GPU support in AWS Lambda through the rCUDA middleware, which provides CUDA applications with remote GPU execution capabilities. A reference architecture for data-driven accelerated processing is introduced, based on elastic queues and event-driven object storage systems to manage resource contention and GPU scheduling. The benefits and limitations are assessed through a use case of sequence alignment. The results indicate that, for certain scenarios, the usage of remote GPUs in AWS Lambda represents a viable approach to reduce the execution time.