Hi-EST SIGNED
Holistic Integration of Emerging Supercomputing Technologies
Total Cost €
0EC-Contrib. €
0Partnership
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0Hi-EST project word cloud
Explore the words cloud of the Hi-EST project. It provides you a very rough idea of what is the project "Hi-EST" about.
Project "Hi-EST" data sheet
The following table provides information about the project.
| Coordinator |
BARCELONA SUPERCOMPUTING CENTER - CENTRO NACIONAL DE SUPERCOMPUTACION
Organization address contact info |
| Coordinator Country | Spain [ES] |
| Project website | http://hiest.bsc.es |
| Total cost | 1˙467˙783 € |
| EC max contribution | 1˙467˙783 € (100%) |
| Programme |
1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC)) |
| Code Call | ERC-2014-STG |
| Funding Scheme | ERC-STG |
| Starting year | 2015 |
| Duration (year-month-day) | from 2015-05-01 to 2020-04-30 |
Partnership
Take a look of project's partnership.
| # | ||||
|---|---|---|---|---|
| 1 | BARCELONA SUPERCOMPUTING CENTER - CENTRO NACIONAL DE SUPERCOMPUTACION | ES (BARCELONA) | coordinator | 1˙467˙783.00 |
Map
Project objective
Hi-EST aims to address a new class of placement problem, a challenge for computational sciences that consists in mapping workloads on top of hardware resources with the goal to maximise the performance of workloads and the utilization of resources. The objective of the placement problem is to perform a more efficient management of the computing infrastructure by continuously adjusting the number and type of resources allocated to each workload.
Placement, in this context, is well known for being NP-hard, and resembles the multi-dimensional knapsack problem. Heuristics have been used in the past for different domains, providing vertical solutions that cannot be generalised. When the workload mix is heterogeneous and the infrastructure hybrid, the problem becomes even more challenging. This is the problem that Hi-EST plans to address. The approach followed will build on top of four research pillars: supervised learning of the placement properties, placement algorithms for tasks, placement algorithms for data, and software defined environments for placement enforcement.
Hi-EST plans to advance research frontiers in four different areas: 1) Adaptive Learning Algorithms: by proposing the first known use of Deep Learning techniques for guiding task and data placement decisions; 2) Task Placement: by proposing the first known algorithm to map heterogeneous sets of tasks on top of systems enabled with Active Storage capabilities, and by extending unifying performance models for heterogeneous workloads to cover and unprecedented number of workload types; 3) Data Placement: by proposing the first known algorithm used to map data on top of heterogeneous sets of key/value stores connected to Active Storage technologies; and 4) Software Defined Environments (SDE): by extending SDE description languages with a still inexistent vocabulary to describe Supercomputing workloads that will be leveraged to combine data and task placement into one single decision-making process.
Publications
| year | authors and title | journal | last update |
|---|---|---|---|
| 2019 |
Shuja-Ur-Rehman Baig, Waheed Iqbal, Josep Lluis Berral, Abdelkarim Erradi, David Carrera Adaptive Prediction Models for Data Center Resources Utilization Estimation published pages: 1681-1693, ISSN: 1932-4537, DOI: 10.1109/tnsm.2019.2932840 |
IEEE Transactions on Network and Service Management 16/4 | 2020-01-29 |
| 2019 |
Shuja-ur-Rehman Baig, Waheed Iqbal, Josep Lluis Berral, Abdelkarim Erradi, David Carrera Real-Time Data Center\'s Telemetry Reduction and Reconstruction Using Markov Chain Models published pages: 4039-4050, ISSN: 1932-8184, DOI: 10.1109/jsyst.2019.2918430 |
IEEE Systems Journal 13/4 | 2020-01-29 |
| 2019 |
Alvaro Villalba, Josep Lluis Berral, David Carrera Constant-Time Sliding Window Framework with Reduced Memory Footprint and Efficient Bulk Evictions published pages: 486-500, ISSN: 1045-9219, DOI: 10.1109/TPDS.2018.2868960 |
IEEE Transactions on Parallel and Distributed Systems 30/3 | 2020-01-29 |
| 2019 |
Nicola Cadenelli, Zoran Jaks̆ić, Jordà Polo, David Carrera Considerations in using OpenCL on GPUs and FPGAs for throughput-oriented genomics workloads published pages: 148-159, ISSN: 0167-739X, DOI: 10.1016/j.future.2018.11.028 |
Future Generation Computer Systems 94 | 2020-01-29 |
| 2018 |
Juan Luis Pérez, Alberto Gutierrez-Torre, Josep Ll. Berral, David Carrera A resilient and distributed near real-time traffic forecasting application for Fog computing environments published pages: 198-212, ISSN: 0167-739X, DOI: 10.1016/j.future.2018.05.013 |
Future Generation Computer Systems 87 | 2019-05-29 |
| 2017 |
Josep Lluis Berral, Nicolas Poggi, David Carrera, Aaron Call, Rob Reinauer, Daron Green ALOJA: A Framework for Benchmarking and Predictive Analytics in Hadoop Deployments published pages: 480-493, ISSN: 2168-6750, DOI: 10.1109/TETC.2015.2496504 |
IEEE Transactions on Emerging Topics in Computing 5/4 | 2019-05-29 |
| 2018 |
David Buchaca Prats, Josep Lluis Berral, David Carrera Automatic Generation of Workload Profiles using Unsupervised Learning Pipelines published pages: 1-1, ISSN: 1932-4537, DOI: 10.1109/TNSM.2017.2786047 |
IEEE Transactions on Network and Service Management 4 | 2019-05-27 |
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