DeepAI AI Chat
Log In Sign Up

Acceleration-as-a-μService: A Cloud-native Monte-Carlo Option Pricing Engine on CPUs, GPUs and Disaggregated FPGAs

by   Dionysios Diamantopoulos, et al.

The evolution of cloud applications into loosely-coupled microservices opens new opportunities for hardware accelerators to improve workload performance. Existing accelerator techniques for cloud sacrifice the consolidation benefits of microservices. This paper presents CloudiFi, a framework to deploy and compare accelerators as a cloud service. We evaluate our framework in the context of a financial workload and present early results indicating up to 485x gains in microservice response time.


page 2

page 3


Address Translation Design Tradeoffs for Heterogeneous Systems

This paper presents a broad, pathfinding design space exploration of mem...

Accelerator Virtualization in Fog Computing: Moving From the Cloud to the Edge

Hardware accelerators are available on the Cloud for enhanced analytics....

Hardless: A Generalized Serverless Compute Architecture for Hardware Processing Accelerators

The increasing use of hardware processing accelerators tailored for spec...

Multi-Objective Hardware-Mapping Co-Optimisation for Multi-Tenant DNN Accelerators

To meet the ever-increasing computation demand from emerging workloads, ...

Analysis and Improvement of Heterogeneous Hardware Support in Docker Images

Docker images are used to distribute and deploy cloud-native application...

GX-Plug: a Middleware for Plugging Accelerators to Distributed Graph Processing

Recently, research communities highlight the necessity of formulating a ...

A Case for Quantifying Statistical Robustness of Specialized Probabilistic AI Accelerators

Statistical machine learning often uses probabilistic algorithms, such a...