Name
Abstract | ||
|---|---|---|
Many data-intensive applications need large memory to boost system performance. The expansion of DRAM is restricted by its high power consumption and price per bit. Flash as an existing technology of Non-Volatile Memory (NVM) can make up for the drawbacks of DRAM. In this paper, we propose a hybrid main memory architecture named SSDRAM that expands RAM with flash-based SSD. SSDRAM implements a runtime library to provide several transparent interfaces for applications. Unlike using SSD as system swap device which manages data at a page level, SSDRAM works at an application object granularity to boost the efficiency of accessing data on SSD. It provides a flexible memory partition and multi-mapping strategy to manage the physical memory by micropages. Experimental results with a number of data-intensive workloads show that SSDRAM can provide up to 3.3 times performance improvement over SSD-swap. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1587/transinf.2016EDL8105 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
data-intensive application, NVM, SSD, hybrid memory | Dram,Computer vision,Computer architecture,Computer science,Artificial intelligence,Computer hardware,Data management,Memory architecture | Journal |
Volume | Issue | ISSN |
E99D | 12 | 1745-1361 |
Citations | PageRank | References |
1 | 0.38 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liyu Wang | 1 | 36 | 8.25 |
| Qiang Wang | 2 | 1 | 0.72 |
| Lan Chen | 3 | 11 | 10.57 |
| Xiaoran Hao | 4 | 1 | 2.41 |