File-Based Memory Management for Non-volatile Main Memory

2013 ◽  
Author(s):  
Shuichi Oikawa ◽  
Satoshi Miki
Keyword(s):  
Memory Management ◽  
Main Memory

scholarly journals Optimization Techniques for a Distributed In-Memory Computing Platform by Leveraging SSD

2021 ◽  
Vol 11 (18) ◽  
pp. 8476
Author(s):  
June Choi ◽  
Jaehyun Lee ◽  
Jik-Soo Kim ◽  
Jaehwan Lee
Keyword(s):  
Memory Management ◽  
Computing System ◽  
Optimization Techniques ◽  
Main Memory ◽  
Apache Spark ◽  
Computing Platform ◽  
Intermediate Data ◽  
Management Capability ◽  
Overall Performance ◽  
Optimization Methodology

In this paper, we present several optimization strategies that can improve the overall performance of the distributed in-memory computing system, “Apache Spark”. Despite its distributed memory management capability for iterative jobs and intermediate data, Spark has a significant performance degradation problem when the available amount of main memory (DRAM, typically used for data caching) is limited. To address this problem, we leverage an SSD (solid-state drive) to supplement the lack of main memory bandwidth. Specifically, we present an effective optimization methodology for Apache Spark by collectively investigating the effects of changing the capacity fraction ratios of the shuffle and storage spaces in the “Spark JVM Heap Configuration” and applying different “RDD Caching Policies” (e.g., SSD-backed memory caching). Our extensive experimental results show that by utilizing the proposed optimization techniques, we can improve the overall performance by up to 42%.

Using DRAM as Cache for Non-Volatile Main Memory Swapping

2016 ◽  
Vol 4 (1) ◽  
pp. 61-71
Author(s):  
Hirotaka Kawata ◽  
Gaku Nakagawa ◽  
Shuichi Oikawa
Keyword(s):  
Power Consumption ◽  
Power Management ◽  
Mobile Devices ◽  
Memory Management ◽  
High Performance ◽  
Reducing Power ◽  
Memory Systems ◽  
Main Memory ◽  
Dynamic Power Management ◽  
Memory Space

The performance of mobile devices such as smartphones and tablets has been rapidly improving in recent years. However, these improvements have been seriously affecting power consumption. One of the greatest challenges is to achieve efficient power management for battery-equipped mobile devices. To solve this problem, the authors focus on the emerging non-volatile memory (NVM), which has been receiving increasing attention in recent years. Since its performance is comparable with that of DRAM, it is possible to replace the main memory with NVM, thereby reducing power consumption. However, the price and capacity of NVM are problematic. Therefore, the authors provide a large memory space without performance degradation by combining NVM with other memory devices. In this study, they propose a design for non-volatile main memory systems that use DRAM as a swap space. This enables both high performance and energy efficient memory management through dynamic power management in NVM and DRAM.

Tnvmalloc: A Thread-Level-Based Wear-Aware Allocator for Nonvolatile Main Memory

2021 ◽  
Author(s):  
Chunhua Xiao ◽  
Lin Zhang ◽  
Mingliang Zhou
Keyword(s):  
Data Structure ◽  
Open Problem ◽  
Memory Management ◽  
Main Memory ◽  
Wear Leveling ◽  
Large Memory

The nonvolatile main memory (NVMM) has the advantages of near-DRAM speed, byte-addressability, and persistence, and presents limitations in write durability. The memory allocator, a fundamental data structure of memory management, can effectively mitigate the wear speed, thereby prolonging the NVMM lifetime. Nevertheless, balancing the performance and writing reliability in single and multi-thread scenarios is still an open problem for NVMM allocators. In this paper, we propose a thread-level wear-aware allocator (Tnvmalloc) that divides the NVMM space into multiple management granularities and then dynamically selects the optimal blocks using a wear-leveling strategy based on allocation requests and wear records. Experiments show that the proposed Tnvmalloc provides more than 10 times improvement in wear-leveling than typical allocators Glibc malloc, NVMalloc, and nvm_malloc, which becomes obvious especially in multi-threaded scenarios. Moreover, when allocating large memory blocks, Tnvmalloc achieves three times faster than that of NVMalloc.

Sign in / Sign up

Export Citation Format

Share Document