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:
Cette deuxième journée (sous forme de deux 1/2 journées: 5 et 10/12) organisée par le laboratoire IMath fait suite à la journée programée en Décembre 2012. Il s'agit de présenter à la communauté universitaire des retours d'expérience en industrie et dans le monde de la recherche sur l'utilisation du calcul haute performance.
Un retour d'expérience sur le développement d'un code de recherche dans l'environnement PETSc1h
PETSc is a suite of data structures and routines for the scalable (parallel) solution of scientific applications modeled by partial differential equations. It supports MPI, shared memory pthreads, and NVIDIA GPUs, as well as hybrid MPI-shared memory pthreads or MPI-GPU parallelism.
Linear Algebra for the Discrete Logarithm Problem using GPUs1h
In the context of cryptanalysis, computing discrete logarithms in large cyclic groups using index-calculus-based methods, such as the number field sieve (NFS-DL) or the function field sieve (FFS), requires solving large sparse systems of linear equations modulo the group order. Most of the fast algorithms used to solve such systems -- e.g., the conjugate gradient or the Lanczos and Wiedemann algorithms -- iterate a product of the corresponding sparse matrix with a vector (SpMV). We investigate accelerating this central operation on NVIDIA GPUs. In this talk, we present a matrix format suitable to the sparsity and the specific computing model and how we use Residue Number System (RNS) arithmetic to accelerate modular operations.