RackMem: A Tailored Caching Layer for Rack Scale Computing

High-performance computing (HPC) clusters suffer from an overall low memory utilization that is caused by the node-centric memory allocation combined with the variable memory requirements of HPC workloads. The recent provisioning of nodes with terabytes of memory to accommodate workloads with extreme peak memory requirements further exacerbates the problem. Memory disaggregation is viewed as a promising remedy to increase overall resource utilization and enable cost-effective up-scaling and efficient operation of HPC clusters, however, the overhead of demand paging in virtual memory management has so far hindered performant implementations. To overcome these limitations, this work presents RackMem, an efficient implementation of disaggregated memory for rack scale computing. RackMem addresses the shortcomings of Linux's demand paging algorithm and automatically adapts to the memory access patterns of individual processes to minimize the inherent overhead of remote memory accesses. Evaluated on a cluster with an InfiniBand interconnect, RackMem outperforms the state-of-the-art RDMA implementation and Linux's virtual memory paging by a significant margin. RackMem's custom demand paging implementation achieves a tail latency that is two orders of magnitude better than that of the Linux kernel. Compared to the state-of-the-art remote paging solution, RackMem achieves a 28% higher throughput and a 44% lower tail latency for a wide variety of real-world workloads.