Near-data computing represents a paradigm shift in addressing challenges faced by data-intensive applications in modern computing systems. This comprehensive overview examines how computational bottlenecks arise from the fundamental separation of storage and processing in traditional architectures, necessitating extensive data movement that consumes significant energy and introduces substantial latency. Several innovative solutions are presented, including Computational Storage Drives (CSDs) that integrate processing capabilities directly within storage media, and Processing-in-Memory (PIM) technologies that incorporate computational elements into memory structures. Samsung's pioneering efforts in both domains demonstrate considerable improvements in performance, energy efficiency, and data transfer reduction across multiple application domains. The article explores memory-centric computing techniques leveraging technologies like Compute Express Link (CXL) and specialized architectures such as IMPICA for pointer chasing acceleration. Practical implementations, including RowClone for bulk data operations and ReRAM-based structures for neural networks, illustrate the transformative potential of near-data processing. The DAMOV benchmark suite provides a structured evaluation of processing-in-memory architectures through carefully designed case studies examining load balancing, accelerator performance, core models, and instruction offloading strategies.
Peethambaran et al. (Sun,) studied this question.