HEP applications perform an excessive amount of allocations/deallocations within short time intervals which results in memory churn, poor locality and performance degradation. These issues are already known for a decade, but due to the complexity of software frameworks and billions of allocations for a single job, up until recently no efficient mechanism has been available to correlate these issues with source code lines. However, with the advent of the Big Data era, many tools and platforms are now available to do large scale memory profiling. This paper presents, a prototype program developed to track and identify each single (de-)allocation. The CERN IT Hadoop cluster is used to compute memory key metrics, like locality, variation, lifetime and density of allocations. The prototype further provides a web based visualization back-end that allows the user to explore the results generated on the Hadoop cluster. Plotting these metrics for every single allocation over time gives a new insight into application's memory handling. For instance, it shows which algorithms cause which kind of memory allocation patterns, which function flow causes how many short-lived objects, what are the most commonly allocated sizes etc. The paper will give an insight into the prototype and will show profiling examples for the LHC reconstruction, digitization and simulation jobs.
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