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Information on didactic, research and institutional assignments on this page are certified by the University; more information, prepared by the professor, are available on the personal web page and in the curriculum vitae indicated on this webpage.
Information on professor
ProfessorPalermo Gianluca
QualificationAssociate professor full time
Belonging DepartmentDipartimento di Elettronica, Informazione e Bioingegneria
Scientific-Disciplinary SectorING-INF/05 - Information Processing Systems
Curriculum VitaeDownload CV (212.11Kb - 02/07/2019)
OrcIDhttps://orcid.org/0000-0001-7955-8012

Contacts
Professor's office hours
DepartmentFloorOfficeDayTimetableTelephoneFaxNotes
Elettronica, Informazione e Bioingegneria--124WednesdayFrom 17:00
To 19:00
0223993552--Via Ponzio 34/5 - Building 20 MSTeams Virtual Office: https://tinyurl.com/palermoVirtualOffice2020
E-mailgianluca.palermo@polimi.it
Professor's personal websitehttp://palermo.faculty.polimi.it

Data source: RE.PUBLIC@POLIMI - Research Publications at Politecnico di Milano

List of publications and reserach products for the year 2021 (Show all details | Hide all details)
Type Title of the Publicaiton/Product
Journal Articles
Tunable approximations to control time-to-solution in an HPC molecular docking Mini-App (Show >>)


List of publications and reserach products for the year 2020 (Show all details | Hide all details)
Type Title of the Publicaiton/Product
Conference proceedings
A Collaborative Filtering Approach for the Automatic Tuning of Compiler Optimisations (Show >>)
Journal Articles
Pegasus: Performance Engineering for Software Applications Targeting HPC Systems (Show >>)
Performance-Driven Analysis for an Adaptive Car-Navigation Service on HPC Systems (Show >>)


List of publications and reserach products for the year 2019 (Show all details | Hide all details)
Type Title of the Publicaiton/Product
Abstract in Atti di convegno
An Hybrid Approach to accelerate a Molecular Docking Application for Virtual Screening in Heterogeneous Nodes (Show >>)
Journal Articles
A survey on compiler autotuning using machine learning (Show >>)
An Efficient Monte Carlo-based Probabilistic Time-Dependent Routing Calculation Targeting a Server-Side Car Navigation System (Show >>)
Exploiting OpenMP and OpenACC to accelerate a geometric approach to molecular docking in heterogeneous HPC nodes (Show >>)
The ANTAREX domain specific language for high performance computing (Show >>)
Workload- and process-variation aware voltage/frequency tuning for energy efficient performance sustainability of NTC manycores (Show >>)
mARGOt: a Dynamic Autotuning Framework for Self-aware Approximate Computing (Show >>)


List of publications and reserach products for the year 2018 (Show all details | Hide all details)
Type Title of the Publicaiton/Product
Conference proceedings
Accelerating a Geometric Approach to Molecular Docking with OpenACC (Show >>)
Autotuning and adaptivity in energy efficient HPC systems: The ANTAREX toolbox (Show >>)
Design space pruning and computational workload splitting for autotuning OpenCL applications (Show >>)
SOCRATES - A seamless online compiler and system runtime autotuning framework for energy-aware applications (Show >>)
Scientific Books
Automatic Tuning of Compilers Using Machine Learning (Show >>)
Journal Articles
Guest Editorial: Special Issue on Computing Frontiers (Show >>)


List of publications and reserach products for the year 2017 (Show all details | Hide all details)
Type Title of the Publicaiton/Product
Conference proceedings
Early stage interference checking for automatic design space exploration of mixed critical systems (Show >>)
The ANTAREX tool flow for monitoring and autotuning energy efficient HPC systems (Show >>)
Journal Articles
CONTREX: Design of embedded mixed-criticality CONTRol systems under consideration of EXtra-functional properties (Show >>)
Exploring efficient hardware support for applications with irregular memory patterns on multinode manycore architectures (Show >>)
MiCOMP: Mitigating the Compiler Phase-Ordering Problem Using Optimization Sub-Sequences and Machine Learning (Show >>)
manifesti v. 3.4.13 / 3.4.13
Area Servizi ICT
27/07/2021