C-AMAT: Concurrent Data Access Model for the Big Data Era

摘要

Scalable data management for big data applications is a challenging task. It puts even more pressure on the lasting memory-wall problem, which makes data access the prominent performance bottleneck for high-end computing. High-end computing is known for its massively parallel architectures. A natural way to improve memory performance is to increase and utilize memory concurrency to a level commensurate with that of high-end computing. We argue that substantial memory concurrency exists at each layer of current memory systems, but it has not been fully utilized. In this talk we reevaluate memory systems and introduce the novel C-AMAT model for system design analysis of concurrent data accesses. C-AMAT is a paradigm shift to support sustained data accessing from a data-centric view. The power of C-AMAT is that it has opened new directions to reduce data access delay. In an ideal parallel memory system, the system will explicitly express and utilize parallel data accesses. This awareness is largely missing from current memory systems. We will review the concurrency available in modern memory systems, present the concept of C-AMAT, and discuss the considerations and possibility of optimizing parallel data access for big data applications. We will also present some of our recent results which quantize and utilize parallel I/O following the parallel memory concept.