In order to enable an iCal export link, your account needs to have an API key created. This key enables other applications to access data from within Indico even when you are neither using nor logged into the Indico system yourself with the link provided. Once created, you can manage your key at any time by going to 'My Profile' and looking under the tab entitled 'HTTP API'. Further information about HTTP API keys can be found in the Indico documentation.
Additionally to having an API key associated with your account, exporting private event information requires the usage of a persistent signature. This enables API URLs which do not expire after a few minutes so while the setting is active, anyone in possession of the link provided can access the information. Due to this, it is extremely important that you keep these links private and for your use only. If you think someone else may have acquired access to a link using this key in the future, you must immediately create a new key pair on the 'My Profile' page under the 'HTTP API' and update the iCalendar links afterwards.
Permanent link for public information only:
Permanent link for all public and protected information:
The Computational Science Initiative at Brookhaven National Laboratory, in partnership with the HEP Center for Computational Excellence and the SOLLVE Exascale Software Technology project, will host the first KNL Hackathon on February 26–March 2, 2018. Currently there are several large-scale computing systems in the US that are based on Intel's KNL processors, including Cori-2 at NERSC, Theta at Argonne Leadership Computing Facility and Stampede-2 at Texas Advanced Computing Center. The KNL Hackathon will offer current and potential users of these systems the opportunity to have 5-day intense hands-on mentoring with computing experts from industry, national labs, universities and OpenMP standardization committee. The goal of the hackathon is to help the participants port or optimize their codes to take advantage of different levels of parallelism and memory hierarchies, which will not only allow them to run more efficiently on KNL-based systems, but also potentially improve the performance of their codes on other CPU-based systems.