Memorydriven

Memorydriven refers to a class of computing paradigms in which the capacity and speed of primary memory are central to system performance and data processing. In memorydriven architectures, data is stored and manipulated as close as possible to the processing units, and software is designed to maximize data locality and minimize movement between storage and compute.

Key techniques include large-scale memory pools, disaggregation of memory from CPUs, and the use of non-volatile memory as a fast, persistent layer. Some approaches advocate near-memory processing where simple compute units reside near memory to operate on data without moving it far. Programming models emphasize memory-centric data layouts, streaming processing, and workload-aware memory management.

Common application domains include big data analytics, in-memory databases, AI training and inference, graph processing, and real-time analytics, where performance scales with memory bandwidth and capacity rather than processor speed alone. Benefits cited include lower latency, higher throughput, and improved energy efficiency by reducing data movement.

Challenges involve the higher cost of large memory footprints, software redesign to exploit data locality, memory fragmentation, persistence and durability guarantees with non-volatile memories, and the need for new operating system and compiler support to orchestrate memory resources.

Memorydriven approaches share goals with related concepts such as in-memory computing, memory-centric architectures, near-memory and disaggregated computing, and memory-bound algorithm design.