E191-04-Research Unit of Parallel Computing

The research group for Parallel Computing is concerned with means and methods for efficiently utilizing real (compute clusters, supercomputers) and idealized (PRAM, communication networks) parallel computing architectures for solving challenging computational problems.

Some concrete themes that we are pursuing are:

Parallel programming interfaces for High Performance Computing (HPC) and their efficient algorithmic support and implementation. The Message-Passing Interface (MPI) is an important paradigm that poses interesting design and implementation problems.
Some specific topics are the quality and performance portability of such interfaces and algorithms for collective communication operations.
Shared-memory parallel computing support: programming models, interfaces (OpenMP), frameworks, algorithms and (concurrent, lock- and wait-free) data structures, and scheduling support.

Address

TU Wien
Faculty of Informatics
Institute of Computer Engineering
Research Group Parallel Computing

Treitlstr. 1 - 3 / 191-04, DG
1040 Vienna
Austria

Phone: +43 1 58801 191 411
Email: secretariat@par.tuwien.ac.at

Address

TU Wien
Faculty of Informatics
Institute of Computer Engineering
Research Group Parallel Computing

Treitlstr. 1 - 3 / 191-04, DG
1040 Vienna
Austria

Phone: +43 1 58801 191 411
Email: secretariat@par.tuwien.ac.at

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SimpleSAMLAuthToken	This is needed to distinguish between the sessions of the logged-in users.	session	HTTP	Login TU Wien
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test_cookie	Is set as a test to check whether the browser allows cookies to be set. Does not contain any identification features.	15 minutes	HTTP	Google
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sb	Is needed to store browser details and security information of the Facebook account.	2 years	HTTP	Meta (Facebook)
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pl	Required for Facebook Pixel.	2 years	HTTP	Meta (Facebook)
lu	Required for Facebook Pixel.	2 years	HTTP	Meta (Facebook)
c_user	Required for Facebook Pixel.	3 months	HTTP	Meta (Facebook)
bcookie	Is needed to store browser data (marketing/tracking).	2 years	HTTP	LinkedIn
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lidc	Is needed to store performed actions on the website (marketing/tracking).	1 day	HTTP	LinkedIn
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lms_analytics	Is needed to identify LinkedIn members for analytics purposes.	30 days	HTTP	LinkedIn
li_fat_id	Required for indirect member identification used for conversion tracking, retargeting and analytics.	30 days	HTTP	LinkedIn
U	Is needed to identify the browser.	3 months	HTTP	LinkedIn
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