Profile-guided scope-based data allocation method

The complexity of High Performance Computing nodes memory system increases in order to challenge application growing memory usage and increasing gap between computation and memory access speeds. As these technologies are just being introduced in HPC supercomputers no one knows if it is better to manage them with hardware or software solutions. Thus both are being studied in parallel. For both solutions, the problem consists in choosing which data to store on which memory at any time. In this paper we present a linear formulation of the data allocation problem. Moreover, we propose a new profile-guided scope-based approach which reduces the data allocation problem complexity, thus enhancing the precision of state of the art analyzes. Finally we have implemented our method in a framework made of GCC plugins, dynamic libraries and python scripts, allowing to test the method on several benchmarks. We have evaluated our method on an INTEL Knight's Landing processor. To this aim we have run LULESH, HydroMM, two hydrodynamic codes, and MiniFE, a finite element mini application. We have compared our framework performance over these codes to several straight-forward solutions: MCDRAM as a cache, in hybrid mode, in flat mode using numactl command and existing AutoHBW dynamic library.