LLC-Guided Data Migration in Hybrid Memory Systems

2019 ◽  
Author(s):  
Evangelos Vasilakis ◽  
Vassilis Papaefstathiou ◽  
Pedro Trancoso ◽  
Ioannis Sourdis
Keyword(s):  
Memory Systems ◽  
Data Migration ◽  
Hybrid Memory

Software Hint-Driven Data Management for Hybrid Memory in Mobile Systems

2022 ◽  
Vol 21 (1) ◽  
pp. 1-18
Author(s):  
Fei Wen ◽  
Mian Qin ◽  
Paul Gratz ◽  
Narasimha Reddy
Keyword(s):  
Time Window ◽  
Memory Systems ◽  
Memory Device ◽  
Memory System ◽  
Mobile Systems ◽  
Data Migration ◽  
Time Data ◽  
Cooperative Approach ◽  
Hybrid Memory ◽  
Data Objects

Hybrid memory systems, comprised of emerging non-volatile memory (NVM) and DRAM, have been proposed to address the growing memory demand of current mobile applications. Recently emerging NVM technologies, such as phase-change memories (PCM), memristor, and 3D XPoint, have higher capacity density, minimal static power consumption and lower cost per GB. However, NVM has longer access latency and limited write endurance as opposed to DRAM. The different characteristics of distinct memory classes render a new challenge for memory system design. Ideally, pages should be placed or migrated between the two types of memories according to the data objects’ access properties. Prior system software approaches exploit the program information from OS but at the cost of high software latency incurred by related kernel processes. Hardware approaches can avoid these latencies, however, hardware’s vision is constrained to a short time window of recent memory requests, due to the limited on-chip resources. In this work, we propose OpenMem: a hardware-software cooperative approach that combines the execution time advantages of pure hardware approaches with the data object properties in a global scope. First, we built a hardware-based memory manager unit (HMMU) that can learn the short-term access patterns by online profiling, and execute data migration efficiently. Then, we built a heap memory manager for the heterogeneous memory systems that allows the programmer to directly customize each data object’s allocation to a favorable memory device within the presumed object life cycle. With the programmer’s hints guiding the data placement at allocation time, data objects with similar properties will be congregated to reduce unnecessary page migrations. We implemented the whole system on the FPGA board with embedded ARM processors. In testing under a set of benchmark applications from SPEC 2017 and PARSEC, experimental results show that OpenMem reduces 44.6% energy consumption with only a 16% performance degradation compared to the all-DRAM memory system. The amount of writes to the NVM is reduced by 14% versus the HMMU-only, extending the NVM device lifetime.

scholarly journals Supporting Superpages and Lightweight Page Migration in Hybrid Memory Systems

2019 ◽  
Vol 16 (2) ◽  
pp. 1-26 ◽  
Author(s):  
Xiaoyuan Wang ◽  
Haikun Liu ◽  
Xiaofei Liao ◽  
Ji Chen ◽  
Hai Jin ◽  
...  
Keyword(s):  
Memory Systems ◽  
Hybrid Memory ◽  
Page Migration

MemBrain: Automated Application Guidance for Hybrid Memory Systems

2018 ◽  
Author(s):  
M. Ben Olson ◽  
Tong Zhou ◽  
Michael R. Jantz ◽  
Kshitij A. Doshi ◽  
M. Graham Lopez ◽  
...  
Keyword(s):  
Memory Systems ◽  
Hybrid Memory

scholarly journals NGraph: Parallel Graph Processing in Hybrid Memory Systems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 103517-103529
Author(s):  
Wei Liu ◽  
Haikun Liu ◽  
Xiaofei Liao ◽  
Hai Jin ◽  
Yu Zhang
Keyword(s):  
Memory Systems ◽  
Graph Processing ◽  
Hybrid Memory ◽  
Parallel Graph

Big high-dimension data cube designs for hybrid memory systems

2020 ◽  
Vol 62 (12) ◽  
pp. 4717-4746
Author(s):  
Rodrigo Rocha Silva ◽  
Celso Massaki Hirata ◽  
Joubert de Castro Lima
Keyword(s):  
High Dimension ◽  
Memory Systems ◽  
Data Cube ◽  
Hybrid Memory ◽  
High Dimension Data
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