Using Real World Workloads and Artificial Intelligence to Optimize NVMe SSD and Persistent Memory Performance

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Wednesday, January 22, 2020
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In this session, see how we evaluate NVMe SSDs (including low-latency NVMe SSDs) and Persistent Memory performance on real world workloads.  We also use real world application workload captures as training for AI optimization with Long Short Term Memory (LSTM) Recurrent Neural Network (RNN) Artificial Intelligence to optimize storage performance in NVMe SSD.  Calypso IOProfiler real world application captures are used to optimize workload IO Stream recognition and IO determinism in storage applications and AI optimized workloads.  DapuStor AI Machine Learning (ML) training and LSTM RNN technologies are used to improve SSD wear leveling, task scheduling, pre-fetching and caching, and to minimize write amplification and garbage collection.  We compare and analyze different performance data in Optane Persistent Memory (DCPMM), NVDIMM-N, XL Flash based NVMe SSD, Optane 3D XPoint NVMe SSD and 3D NAND NVMe SSD on a variety of real world application workloads.

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