Project description
Overcoming performance issues in data science and machine learning
Data science and machine learning have attained an important position in scientific fields and studies of the modern world. This became possible with a nearly constant advancement of platforms, increased data availability, and improved algorithms and computational performance. Unfortunately, despite these ongoing improvements, computational performance is expected to slow down, having a negative effect on the other three parameters. The EU-funded ScaleML project aims to find a solution by introducing a novel methodology for machine learning and data science which could overcome these inefficiencies. To that end, it will develop techniques to allow elastic coordination between singular and network-based machines, which should help overcome performance issues.
Objective
Machine learning and data science are areas of tremendous progress over the last decade, leading to exciting research developments, and significant practical impact. Broadly, progress in this area has been enabled by the rapidly increasing availability of data, by better algorithms, and by large-scale platforms enabling efficient computation on immense datasets. While it is reasonable to expect that the first two trends will continue for the foreseeable future, the same cannot be said of the third trend, of continually increasing computational performance. Increasing computational demands place immense pressure on algorithms and systems to scale, while the performance limits of traditional computing paradigms are becoming increasingly apparent. Thus, the question of building algorithms and systems for scalable machine learning is extremely pressing. The project will take a decisive step to answer this challenge, developing new abstractions, algorithms and system support for scalable machine learning. In a nutshell, the line of approach is elastic coordination: allowing machine learning algorithms to approximate and/or randomize their synchronization and communication semantics, in a structured, controlled fashion, to achieve scalability. The project exploits the insight that many such algorithms are inherently stochastic, and hence robust to inconsistencies. My thesis is that elastic coordination can lead to significant, consistent performance improvements across a wide range of applications, while guaranteeing provably correct answers. ScaleML will apply elastic coordination to two specific relevant scenarios: scalability inside a single multi-threaded machine, and scalability across networks of machines.
Conceptually, the project’s impact is in providing a set of new design principles and algorithms for scalable computation. It will develop these insights into a set of tools and working examples for scalable distributed machine learning.
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
-
H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)
MAIN PROGRAMME
See all projects funded under this programme
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
ERC-STG - Starting Grant
See all projects funded under this funding scheme
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2018-STG
See all projects funded under this callHost institution
Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
3400 KLOSTERNEUBURG
Austria
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.