Exploiting Data Similarity to Reduce Memory Footprints

Abstract

Memory size has long limited large-scale applications on high-performance computing (HPC) systems. Since compute nodes frequently do not have swap space, physical memory often limits problem sizes. Increasing core counts per chip and power density constraints, which limit the number of DIMMs per node, have exacerbated this problem. Further, DRAM constitutes a significant portion of overall HPC system cost. Therefore, instead of adding more DRAM to the nodes, mechanisms to manage memory usage more efficiently -- preferably transparently -- could increase effective DRAM capacity and thus the benefit of multicore nodes for HPC systems. MPI application processes often exhibit significant data similarity. These data regions occupy multiple physical locations across the individual rank processes within a multicore node and thus offer a potential savings in memory capacity. These regions, primarily residing in heap, are dynamic, which makes them difficult to manage statically. Our novel memory allocation library, {\it SBLLmallocShort}, automatically identifies identical memory blocks and merges them into a single copy. Our implementation is transparent to the application and does not require any kernel modifications. Overall, we demonstrate that {\it SBLLmalloc} reduces the memory footprint of a range of MPI applications by $32.03\%$ on average and up to $60.87\%$. Further, {\it SBLLmalloc} supports problem sizes for IRS over $21.36\%$ larger than using standard memory management techniques, thus significantly increasing effective system size. Similarly, {\it SBLLmalloc} requires $43.75\%$ fewer nodes than standard memory management techniques to solve an AMG problem.