Speaker
Description
With the increased data volumes expected to be delivered by the HL-LHC, it becomes critical for the ATLAS experiment to maximize the utilization of available computing resources ranging from conventional GRID clusters to supercomputers and cloud computing platforms. To be able to run its data processing applications on these resources, the ATLAS software framework must be capable of efficiently executing data processing tasks in heterogeneous distributed computing environments. Today with the use of Gaudi Avalanche Scheduler, a central component of the multithreaded Athena framework whose implementation is based on Intel TBB, we can efficiently schedule Athena algorithms to multiple threads within a single compute node. Our goal is to develop a new framework scheduler capable of supporting distributed heterogeneous environments, based on technologies like HPX and Ray. After the initial evaluation phase of these technologies, we began the actual development of prototype distributed task schedulers and their integration with the Athena framework. This contribution will describe these prototype schedulers , as well as the preliminary results of performance studies of these prototypes within ATLAS data processing applications.
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