The document discusses performance characterization of in-memory data analytics using Apache Spark on a scale-up server. It identifies problems like poor multicore scalability, thread load imbalance, I/O wait times, and GC overhead. Solutions proposed include NUMA awareness, hyperthreading, disabling next-line prefetchers, using parallel scavenge GC, multiple small executors, and a future node architecture based on a hybrid in-storage processing and 2D processing-in-memory design. The work aims to improve node-level performance through architecture support for emerging big data workloads.