• IEEE.org
  • IEEE CS Standards
  • Career Center
  • About Us
  • Subscribe to Newsletter

0

IEEE
CS Logo
  • MEMBERSHIP
  • CONFERENCES
  • PUBLICATIONS
  • EDUCATION & CAREER
  • VOLUNTEER
  • ABOUT
  • Join Us
CS Logo

0

IEEE Computer Society Logo
Sign up for our newsletter
FacebookTwitterLinkedInInstagramYoutube
IEEE COMPUTER SOCIETY
About UsBoard of GovernorsNewslettersPress RoomIEEE Support CenterContact Us
COMPUTING RESOURCES
Career CenterCourses & CertificationsWebinarsPodcastsTech NewsMembership
BUSINESS SOLUTIONS
Corporate PartnershipsConference Sponsorships & ExhibitsAdvertisingRecruitingDigital Library Institutional Subscriptions
DIGITAL LIBRARY
MagazinesJournalsConference ProceedingsVideo LibraryLibrarian Resources
COMMUNITY RESOURCES
GovernanceConference OrganizersAuthorsChaptersCommunities
POLICIES
PrivacyAccessibility StatementIEEE Nondiscrimination PolicyIEEE Ethics ReportingXML Sitemap

Copyright 2025 IEEE - All rights reserved. A public charity, IEEE is the world’s largest technical professional organization dedicated to advancing technology for the benefit of humanity.

  • Home
  • /Profiles
  • Home
  • /Profiles

Jesús Labarta

Award Recipient

Featured ImageFeatured ImageJesús Labarta received a B.S. in Telecommunications Engineering from the Technical University of Catalunya (UPC) in 1981 and his Ph.D. in Telecommunications Engineering also from UPC in 1983. He is full professor of Computer Architecture at UPC since 1990 and was Director of CEPBA-European Center of Parallelism at Barcelona from 1996 to2005. Since its creation in 2005, he has been the Director of the Computer Sciences Research Department within the Barcelona Supercomputing Center (BSC). During his 35-year academic career, Prof. Labarta has made significant contributions in programming models and performance analysis tools for parallel, multicore and accelerated systems, with the sole objective of helping application programmers to improve their understanding of their applications performance and to improve programming productivity in the transition towards very large-scale systems. Under his supervision, his research team has been developing performance analysis and prediction tools (Paraver and Dimemas) and pioneering research on how to increase the intelligence embedded in these performance tools. He has also been a driving force behind the task-based StarSs programming model, which gives runtime systems the required intelligence to dynamically exploit the potential parallelism and resources available. His team has influenced the evolution of the OpenMP standard with the OmpSs instantiation of StarSs, and, in particular, its tasking model. Prof. Labarta has authored a large number of publications in peer-reviewed conferences and journals and has advised dozens of PhD students. He has constantly tried to incorporate his vision and ideas into industrial collaborations. These include projects partially funded by the European Commission, or with HPC companies. Currently Prof. Labarta is the leader of the Performance Optimization and Productivity (POP) EU Center of Excellence where more than 100 users (both academic and SMEs) from a very wide range of application sectors receive performance assessments and suggestions for code refactoring efforts.
LATEST NEWS
Platform Engineering: Bridging the Developer Experience Gap in Enterprise Software Development
Platform Engineering: Bridging the Developer Experience Gap in Enterprise Software Development
IEEE Std 3158.1-2025 — Verifying Trust in Data Sharing: Standard for Testing and Performance of a Trusted Data Matrix System
IEEE Std 3158.1-2025 — Verifying Trust in Data Sharing: Standard for Testing and Performance of a Trusted Data Matrix System
IEEE Std 3220.01-2025: Standard for Consensus Framework for Blockchain System
IEEE Std 3220.01-2025: Standard for Consensus Framework for Blockchain System
Mapping the $85B AI Processor Landscape: Global Startup Surge, Market Consolidation Coming?
Mapping the $85B AI Processor Landscape: Global Startup Surge, Market Consolidation Coming?
AI Agentic Mesh – A Foundational Architecture for Enterprise Autonomy
AI Agentic Mesh – A Foundational Architecture for Enterprise Autonomy
Read Next

Platform Engineering: Bridging the Developer Experience Gap in Enterprise Software Development

IEEE Std 3158.1-2025 — Verifying Trust in Data Sharing: Standard for Testing and Performance of a Trusted Data Matrix System

IEEE Std 3220.01-2025: Standard for Consensus Framework for Blockchain System

Mapping the $85B AI Processor Landscape: Global Startup Surge, Market Consolidation Coming?

AI Agentic Mesh – A Foundational Architecture for Enterprise Autonomy

IEEE O.C A.I “DEVHACK” Hackathon 2025 Winner Celebration

Broadening Participation Winners 2026

IEEE Publications Serve as Accessible Industry Standard for Authors

Get the latest news and technology trends for computing professionals with ComputingEdge
Sign up for our newsletter