TY - GEN
T1 - The VINEYARD Framework for Heterogeneous Cloud Applications
T2 - 12th Conference on Design and Architectures for Signal and Image Processing, DASIP 2018
AU - Sidiropoulos, Harry
AU - Chatzikonstantis, George
AU - Soudris, Dimitrios
AU - Strydis, Christos
PY - 2018/12/31
Y1 - 2018/12/31
N2 - 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.
AB - 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.
KW - cloud computing
KW - FPGAs
KW - hardware accelerators
KW - neurocomputing
KW - reconfigurable computing
UR - http://www.scopus.com/inward/record.url?scp=85061347381&partnerID=8YFLogxK
U2 - 10.1109/DASIP.2018.8597119
DO - 10.1109/DASIP.2018.8597119
M3 - Conference contribution
AN - SCOPUS:85061347381
T3 - Conference on Design and Architectures for Signal and Image Processing, DASIP
SP - 70
EP - 75
BT - 2018 Conference on Design and Architectures for Signal and Image Processing, DASIP 2018
PB - IEEE
Y2 - 10 October 2018 through 12 October 2018
ER -