The increase in memory capacity is substantially behind the increase in computing power in today's supercomputers. In order to alleviate the effect of this gap, diverse options such as NVM - non-volatile memory (less expensive but slow) and HBM - high bandwidth memory (fast but expensive) are being explored. In this paper, we present a common approach using parallel runtime techniques for utilizing NVM and HBM as extensions of the existing memory hierarchy. We evaluate our approach using matrix-matrix multiplication kernel implemented in CHARM++ and show that applications with memory requirement four times the HBM/DRAM capacity can be executed efficiently using significantly less total resources.

Many HPC applications require dynamic load balancing to achieve high performance and system utilization. Different applications have different characteristics and hence require different load balancing strategies. Invocation of a suboptimal load balancing strategy can lead to inefficient execution. We propose Meta-Balancer, a framework to automatically decide the best load balancing strategy. It employs randomized decision forests, a machine learning method, to learn a model for choosing the best load balancing strategy for an application represented by a set of features that capture the application characteristics.