The VINEYARD Framework for Heterogeneous Cloud Applications

Abstract

Emerging cloud applications like machine learning, AI, big data analytics and scientific computing require highperformance computing systems that can sustain the increased amount of data processing without consuming excessive power. To this end, many cloud operators have started deploying hardware accelerators, like GPUs and FPGAs, to increase the performance of computationally intensive tasks. However, increased performance, comes at a higher cost of increased programming complexity for utilizing these accelerators. VINEYARD has developed a versatile framework that allows the seamless deployment and utilization of heterogeneous accelerators in the cloud without increasing the programming complexity while offering the flexibility of software packages. This paper presents the main components that have been developed in the VINEYARD framework and focuses on BrainFrame, the neurocomputing case that demonstrates the new framework's value. BrainFrame not only accelerates neuronal simulations but also has an architecture that allows easy access to neuroscientists, hiding the system complexity, and enabling a modular integration of new accelerated simulators.