Summary
Three major trends put immense pressure on existing infrastructure. First, there is an increased demand for compute given the exponential growth of data volume we are collecting. Second, as most data- and compute-intensive systems move to the cloud, it is vital to make data-centers more efficient. Third, pressured by the waning laws of computing infrastructure (end of Dennard scaling and its impact on Moores' law) the hardware landscape has embarked on a major push towards specialization. Unsurprisingly, all of these have major implications on software development.
FDS addresses these challenges in a holistic fashion by establishing the foundations of a new software infrastructure. More specifically, I propose new system software abstractions that enable more efficient execution of data-intensive jobs on modern hardware; and new cost-models to support reasoning about the performance/resource efficiency trade-offs when scheduling the said workloads.
To enable development of future-proof data systems, I propose replacing the traditional monoliths of a logical-graph optimizer and execution engine, with a new flexible framework for optimization and compilation, and a runtime system that can exploit the features of modern hardware, but remains amenable to its evolving nature. Doing so requires innovation on many levels, and combines work from many layers of the system stack: from HW/SW co-design, to compilers, operating and runtime systems, databases and distributed systems.
It is a non-trivial undertaking, but it is a necessary one. And it will have big and lasting impact on academia, industry, and society. In fact, I strongly believe that the only way forward towards reducing the increasing gap between data- and compute-demand in resource-hungry data centers is a software-solution that boosts efficiency, embraces specialization, removes redundancy and optimizes for data movement. FDS will be the pioneering system leading the way.
FDS addresses these challenges in a holistic fashion by establishing the foundations of a new software infrastructure. More specifically, I propose new system software abstractions that enable more efficient execution of data-intensive jobs on modern hardware; and new cost-models to support reasoning about the performance/resource efficiency trade-offs when scheduling the said workloads.
To enable development of future-proof data systems, I propose replacing the traditional monoliths of a logical-graph optimizer and execution engine, with a new flexible framework for optimization and compilation, and a runtime system that can exploit the features of modern hardware, but remains amenable to its evolving nature. Doing so requires innovation on many levels, and combines work from many layers of the system stack: from HW/SW co-design, to compilers, operating and runtime systems, databases and distributed systems.
It is a non-trivial undertaking, but it is a necessary one. And it will have big and lasting impact on academia, industry, and society. In fact, I strongly believe that the only way forward towards reducing the increasing gap between data- and compute-demand in resource-hungry data centers is a software-solution that boosts efficiency, embraces specialization, removes redundancy and optimizes for data movement. FDS will be the pioneering system leading the way.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101164556 |
| Start date: | 01-01-2025 |
| End date: | 31-12-2029 |
| Total budget - Public funding: | 1 496 305,00 Euro - 1 496 305,00 Euro |
Cordis data
Original description
Three major trends put immense pressure on existing infrastructure. First, there is an increased demand for compute given the exponential growth of data volume we are collecting. Second, as most data- and compute-intensive systems move to the cloud, it is vital to make data-centers more efficient. Third, pressured by the waning laws of computing infrastructure (end of Dennard scaling and its impact on Moores' law) the hardware landscape has embarked on a major push towards specialization. Unsurprisingly, all of these have major implications on software development.FDS addresses these challenges in a holistic fashion by establishing the foundations of a new software infrastructure. More specifically, I propose new system software abstractions that enable more efficient execution of data-intensive jobs on modern hardware; and new cost-models to support reasoning about the performance/resource efficiency trade-offs when scheduling the said workloads.
To enable development of future-proof data systems, I propose replacing the traditional monoliths of a logical-graph optimizer and execution engine, with a new flexible framework for optimization and compilation, and a runtime system that can exploit the features of modern hardware, but remains amenable to its evolving nature. Doing so requires innovation on many levels, and combines work from many layers of the system stack: from HW/SW co-design, to compilers, operating and runtime systems, databases and distributed systems.
It is a non-trivial undertaking, but it is a necessary one. And it will have big and lasting impact on academia, industry, and society. In fact, I strongly believe that the only way forward towards reducing the increasing gap between data- and compute-demand in resource-hungry data centers is a software-solution that boosts efficiency, embraces specialization, removes redundancy and optimizes for data movement. FDS will be the pioneering system leading the way.
Status
SIGNEDCall topic
ERC-2024-STGUpdate Date
22-11-2024
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