SAVE
Responsabile:
Ricerca UE FP7
Ruolo DEIB: Coordinatore
Data inizio: 31/08/2013
Durata: 26 mesi
Sommario
SAVE (Self-Adaptive Virtualization-Aware High-Performance/Low-Energy Heterogeneous System Architectures) is a project funded by the EU Seventh Framework Program FP7. The global ICT footprint is expected to more than triple by 2020, caused by data centers that provide information at our fingertips and the mobile devices we use to access it. Nowadays, high performance systems are designed to serve rather static workloads with high-performance requirements, although we are moving towards a highly flexible, on-demand computing scenario characterized by varying workloads, constituted by diverse applications with different performance requirements and criticality. This mismatch between demand and supply of computing power causes high energy dissipation. A promising approach to address the challenges posed by this scenario is to better exploit specialized computing resources integrated in a heterogeneous system architecture by taking advantage of their individual characteristics to optimize the performance/energy trade-off for the overall system. However, heterogeneity comes at the cost of greater complexity.
SAVE addresses these limitations by exploiting self-adaptivity and hardware-assisted virtualization to allow the system to autonomously decide which specialized computing resources are exploited to achieve a more efficient execution, based on user-defined optimization goals, such as performance, energy, reliability.
SAVE defines crosscutting SW/HW technologies for implementing self-adaptive systems exploiting GPUs and FPGA-based data flow engines (DFEs) that enhance heterogeneous architectures to cope with the increased variety and dynamics of high-performance and embedded computing workloads. Virtualization and self-adaptation are jointly exploited to obtain a new self-adaptive virtualization-aware Heterogeneous System Architecture.
To this end, SAVE will develop hw/sw technologies consisting in:
SAVE addresses these limitations by exploiting self-adaptivity and hardware-assisted virtualization to allow the system to autonomously decide which specialized computing resources are exploited to achieve a more efficient execution, based on user-defined optimization goals, such as performance, energy, reliability.
SAVE defines crosscutting SW/HW technologies for implementing self-adaptive systems exploiting GPUs and FPGA-based data flow engines (DFEs) that enhance heterogeneous architectures to cope with the increased variety and dynamics of high-performance and embedded computing workloads. Virtualization and self-adaptation are jointly exploited to obtain a new self-adaptive virtualization-aware Heterogeneous System Architecture.
To this end, SAVE will develop hw/sw technologies consisting in:
- Advanced run-time self-adaptiveness OS support layer;
- Hardware-assisted virtualization support for GPUs/DFEs;
- Hypervisor extensions.
Risultati del progetto ed eventuali pubblicazioni scientifiche/brevetti
SAVE project, closed in August 2016, challenged a team of engineers and researchers to explore how complex hardware systems can more efficiently execute data intensive applications.
Funded by the European Union, SAVE has led to a number of innovations in hardware, software and operating system (OS) components. When integrated together, they can reduce application deployment costs and maximize usage of heterogeneous system computing units, resulting in energy efficiency being improved by up to twenty per cent.
A range of complex electronic systems stands to benefit from these innovations, including computer data centers, consumer electronics, automotive products and complex industrial electronics.
The computing units can be on chip, for example central processing units (CPUs) ranging from small and low-power to high-end and efficient, graphics processing units (GPUs), and dedicated accelerators. Alternatively, the units can be off chip, such as racks of dedicated accelerators or field-programmable gate arrays (FPGAs).
The prototyped technologies will enable performance and energy-efficiency gains in high-performance computing (HPC) and embedded heterogeneous systems.
Key achievements include:
• Platform behavior monitoring and task dispatching hardware and software: the first toolset closely tracks the performance and use rate of the various computing units available in the heterogeneous systems. The second toolset decides which computing units are best suited for the job.
• Just-in-Time compilation technology: using SAVE technologies, at runtime, a single application-code representation is optimized to the many possible hardware targets of the platform: CPUs, GPUs, accelerators, FPGAs.
• Hardware and software virtualization technologies: these technologies efficiently expose the dedicated processing engines to the many virtual machines (VM) running on these systems. The teams successfully prototyped virtualized GPUs, virtualized FPGA-based data-flow engines (DFEs), and virtualized application-specific accelerators.
These innovations are the culmination of three years of collaborative research by a team of three academic and four industrial partners. The international team worked together to achieve dynamic optimization of workload assignments across multicore system computation units, operated simultaneously from several virtualized operating systems. These developments lay the foundations for industrial partners to further optimize ever more complex systems, including HPC systems for finance applications and automotive embedded systems.
Funded by the European Commission’s Seventh Framework Program (FP7), the project was launched on 01 September 2013, under the project name SAVE: ‘Self-Adaptive Virtualization-Aware High-Performance/Low-Energy Heterogeneous System Architectures.’
Funded by the European Union, SAVE has led to a number of innovations in hardware, software and operating system (OS) components. When integrated together, they can reduce application deployment costs and maximize usage of heterogeneous system computing units, resulting in energy efficiency being improved by up to twenty per cent.
A range of complex electronic systems stands to benefit from these innovations, including computer data centers, consumer electronics, automotive products and complex industrial electronics.
The computing units can be on chip, for example central processing units (CPUs) ranging from small and low-power to high-end and efficient, graphics processing units (GPUs), and dedicated accelerators. Alternatively, the units can be off chip, such as racks of dedicated accelerators or field-programmable gate arrays (FPGAs).
The prototyped technologies will enable performance and energy-efficiency gains in high-performance computing (HPC) and embedded heterogeneous systems.
Key achievements include:
• Platform behavior monitoring and task dispatching hardware and software: the first toolset closely tracks the performance and use rate of the various computing units available in the heterogeneous systems. The second toolset decides which computing units are best suited for the job.
• Just-in-Time compilation technology: using SAVE technologies, at runtime, a single application-code representation is optimized to the many possible hardware targets of the platform: CPUs, GPUs, accelerators, FPGAs.
• Hardware and software virtualization technologies: these technologies efficiently expose the dedicated processing engines to the many virtual machines (VM) running on these systems. The teams successfully prototyped virtualized GPUs, virtualized FPGA-based data-flow engines (DFEs), and virtualized application-specific accelerators.
These innovations are the culmination of three years of collaborative research by a team of three academic and four industrial partners. The international team worked together to achieve dynamic optimization of workload assignments across multicore system computation units, operated simultaneously from several virtualized operating systems. These developments lay the foundations for industrial partners to further optimize ever more complex systems, including HPC systems for finance applications and automotive embedded systems.
Funded by the European Commission’s Seventh Framework Program (FP7), the project was launched on 01 September 2013, under the project name SAVE: ‘Self-Adaptive Virtualization-Aware High-Performance/Low-Energy Heterogeneous System Architectures.’
