为了在数据密集型工作流下有效降低缓存碎片整理开销并提高缓存命中率,提出一种持久性分布式文件系统客户端缓存DFS-Cache(Distributed File System Cache)。DFS-Cache基于非易失性内存(NVM)设计实现,能够保证数据的持久性和崩溃一致性...为了在数据密集型工作流下有效降低缓存碎片整理开销并提高缓存命中率,提出一种持久性分布式文件系统客户端缓存DFS-Cache(Distributed File System Cache)。DFS-Cache基于非易失性内存(NVM)设计实现,能够保证数据的持久性和崩溃一致性,并大幅减少冷启动时间。DFS-Cache包括基于虚拟内存重映射的缓存碎片整理机制和基于生存时间(TTL)的缓存空间管理策略。前者基于NVM可被内存控制器直接寻址的特性,动态修改虚拟地址和物理地址之间的映射关系,实现零拷贝的内存碎片整理;后者是一种冷热分离的分组管理策略,借助重映射的缓存碎片整理机制,提升缓存空间的管理效率。实验采用真实的Intel傲腾持久性内存设备,对比商用的分布式文件系统MooseFS和GlusterFS,采用Fio和Filebench等标准测试程序,DFS-Cache最高能提升5.73倍和1.89倍的系统吞吐量。展开更多
新型非易失性存储器(non-volatile memory,NVM)技术日渐成熟,延迟越来越低,带宽越来越高,未来将不仅有可能取代以动态随机存储器(dynamic random access memory,DRAM)为代表的易失型存储设备在主存中的垄断地位,还有可能取代传统Flash...新型非易失性存储器(non-volatile memory,NVM)技术日渐成熟,延迟越来越低,带宽越来越高,未来将不仅有可能取代以动态随机存储器(dynamic random access memory,DRAM)为代表的易失型存储设备在主存中的垄断地位,还有可能取代传统Flash和机械硬盘作为外存服务未来的计算机系统.如何综合各类新型存储的特性,设计高能效的存储架构,实现可应对大数据、云计算所需求的新型主存系统已经成为工业界和学术界的研究热点.提出基于高性能SOC FPGA阵列的NVM验证架构,互联多级FPGA,利用多层次FPGA结构扩展链接多片NVM.依据所提出的验证架构,设计了基于多层次FPGA的主从式NVM控制器,并完成适用于该架构的硬件原型设计.该架构不仅可以实现测试同类型多片NVM协同工作,也可以进行混合NVM存储管理方案验证.展开更多
随着大数据应用的涌现,计算机系统需要更大容量的内存以满足大数据处理的高时效性需求.新型非易失性存储器(non-volatile memory,NVM)结合传统动态随机存储器(dynamic random access memory,DRAM)组成的混合内存系统具有内存容量大、功...随着大数据应用的涌现,计算机系统需要更大容量的内存以满足大数据处理的高时效性需求.新型非易失性存储器(non-volatile memory,NVM)结合传统动态随机存储器(dynamic random access memory,DRAM)组成的混合内存系统具有内存容量大、功耗低的优势,因而得到了广泛关注.大数据应用同时也面临着旁路转换缓冲器(translation lookaside buffer,TLB)缺失率过高的性能瓶颈.大页可以有效降低TLB缺失率,然而,在混合内存中支持大页面临着大页迁移开销过大的问题.因此,设计了一种支持大页和大容量缓存的层次化混合内存系统:DRAM和NVM分别使用4KB和2MB粒度的页面分别进行管理,同时在DRAM和NVM之间实现直接映射.设计了基于访存频率的DRAM缓存数据过滤机制,减轻了带宽压力.提出了基于内存实时信息的动态热度阈值调整策略,灵活适应应用访存特征的变化.实验显示:与使用大页的全NVM内存系统和缓存热页(caching hot page,CHOP)系统相比平均有69.9%和15.2%的性能提升,而与使用大页的全DRAM内存系统相比平均只有8.8%的性能差距.展开更多
Wearable devices become popular because they can help people observe health condition.The battery life is the critical problem for wearable devices. The non-volatile memory(NVM) attracts attention in recent years beca...Wearable devices become popular because they can help people observe health condition.The battery life is the critical problem for wearable devices. The non-volatile memory(NVM) attracts attention in recent years because of its fast reading and writing speed, high density, persistence, and especially low idle power. With its low idle power consumption,NVM can be applied in wearable devices to prolong the battery lifetime such as smart bracelet. However, NVM has higher write power consumption than dynamic random access memory(DRAM). In this paper, we assume to use hybrid random access memory(RAM)and NVM architecture for the smart bracelet system.This paper presents a data management algorithm named bracelet power-aware data management(BPADM) based on the architecture. The BPADM can estimate the power consumption according to the memory access, such as sampling rate of data, and then determine the data should be stored in NVM or DRAM in order to satisfy low power. The experimental results show BPADM can reduce power consumption effectively for bracelet in normal and sleeping modes.展开更多
Edge storage stores the data directly at the data collection point,and does not need to transmit the collected data to the storage central server through the network.It is a critical technology that supports applicati...Edge storage stores the data directly at the data collection point,and does not need to transmit the collected data to the storage central server through the network.It is a critical technology that supports applications such as edge computing and 5G network applications,with lower network communication overhead,lower interaction delay and lower bandwidth cost.However,with the explosion of data and higher real-time requirements,the traditional Internet of Things(IoT)storage architecture cannot meet the requirements of low latency and large capacity.Non-volatile memory(NVM)presents new possibilities regarding this aspect.This paper classifies the different storage architectures based on NVM and compares the system goals,architectures,features,and limitations to explore new research opportunities.Moreover,the existing solutions to reduce the write latency and energy consumption and increase the lifetime of NVM IoT storage devices are analyzed.Furthermore,we discuss the security and privacy issues of IoT devices and compare the mainstream solutions.Finally,we present the opportunities and challenges of building IoT storage systems based on NVM.展开更多
With the development of the nonvolatile memory(NVM),using NVM in the design of the cache and scratchpad memory(SPM)has been increased.This paper presents a data variable allocation(DVA)algorithm based on the genetic a...With the development of the nonvolatile memory(NVM),using NVM in the design of the cache and scratchpad memory(SPM)has been increased.This paper presents a data variable allocation(DVA)algorithm based on the genetic algorithm for NVM-based SPM to prolong the lifetime.The lifetime can be formulated indirectly as the write counts on each SPM address.Since the differences between global variables and stack variables,our optimization model has three constraints.The constraints of the central processing unit(CPU)utilization and size are used for all variables,while no-overlay constraint is only used for stack variables.To satisfy the constraints of the optimization model,we use the greedy strategy to generate the initial population which can determine whether data variables are allocated to SPM and distribute them evenly on SPM addresses.Finally,we use the Mälardalen worst case executive time(WCET)benchmark to evaluate our algorithm.The experimental results show that the DVA algorithm can not only obtain close-to-optimal solutions,but also prolong the lifetime by 9.17% on average compared with SRAM-based SPM.展开更多
文摘为了在数据密集型工作流下有效降低缓存碎片整理开销并提高缓存命中率,提出一种持久性分布式文件系统客户端缓存DFS-Cache(Distributed File System Cache)。DFS-Cache基于非易失性内存(NVM)设计实现,能够保证数据的持久性和崩溃一致性,并大幅减少冷启动时间。DFS-Cache包括基于虚拟内存重映射的缓存碎片整理机制和基于生存时间(TTL)的缓存空间管理策略。前者基于NVM可被内存控制器直接寻址的特性,动态修改虚拟地址和物理地址之间的映射关系,实现零拷贝的内存碎片整理;后者是一种冷热分离的分组管理策略,借助重映射的缓存碎片整理机制,提升缓存空间的管理效率。实验采用真实的Intel傲腾持久性内存设备,对比商用的分布式文件系统MooseFS和GlusterFS,采用Fio和Filebench等标准测试程序,DFS-Cache最高能提升5.73倍和1.89倍的系统吞吐量。
文摘随着大数据应用的涌现,计算机系统需要更大容量的内存以满足大数据处理的高时效性需求.新型非易失性存储器(non-volatile memory,NVM)结合传统动态随机存储器(dynamic random access memory,DRAM)组成的混合内存系统具有内存容量大、功耗低的优势,因而得到了广泛关注.大数据应用同时也面临着旁路转换缓冲器(translation lookaside buffer,TLB)缺失率过高的性能瓶颈.大页可以有效降低TLB缺失率,然而,在混合内存中支持大页面临着大页迁移开销过大的问题.因此,设计了一种支持大页和大容量缓存的层次化混合内存系统:DRAM和NVM分别使用4KB和2MB粒度的页面分别进行管理,同时在DRAM和NVM之间实现直接映射.设计了基于访存频率的DRAM缓存数据过滤机制,减轻了带宽压力.提出了基于内存实时信息的动态热度阈值调整策略,灵活适应应用访存特征的变化.实验显示:与使用大页的全NVM内存系统和缓存热页(caching hot page,CHOP)系统相比平均有69.9%和15.2%的性能提升,而与使用大页的全DRAM内存系统相比平均只有8.8%的性能差距.
基金supported by the Research Fund of National Key Laboratory of Computer Architecture under Grant No.CARCH201501the Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing under Grant No.2016A09
文摘Wearable devices become popular because they can help people observe health condition.The battery life is the critical problem for wearable devices. The non-volatile memory(NVM) attracts attention in recent years because of its fast reading and writing speed, high density, persistence, and especially low idle power. With its low idle power consumption,NVM can be applied in wearable devices to prolong the battery lifetime such as smart bracelet. However, NVM has higher write power consumption than dynamic random access memory(DRAM). In this paper, we assume to use hybrid random access memory(RAM)and NVM architecture for the smart bracelet system.This paper presents a data management algorithm named bracelet power-aware data management(BPADM) based on the architecture. The BPADM can estimate the power consumption according to the memory access, such as sampling rate of data, and then determine the data should be stored in NVM or DRAM in order to satisfy low power. The experimental results show BPADM can reduce power consumption effectively for bracelet in normal and sleeping modes.
基金This work is supported by National Key Research and Development Program of China NO.2018YFB0203904National Natural Science Foundation of China(61832020,61872392,U1611261,U1811461,61702569)the Pearl River S&T Nova Program of Guangzhou Province(201906010008)+1 种基金Natural Science Foundation of Guangdong Province(2018B030312002)Key Area Research and Development Program of Guang Dong Province(2019B010107001).
文摘Edge storage stores the data directly at the data collection point,and does not need to transmit the collected data to the storage central server through the network.It is a critical technology that supports applications such as edge computing and 5G network applications,with lower network communication overhead,lower interaction delay and lower bandwidth cost.However,with the explosion of data and higher real-time requirements,the traditional Internet of Things(IoT)storage architecture cannot meet the requirements of low latency and large capacity.Non-volatile memory(NVM)presents new possibilities regarding this aspect.This paper classifies the different storage architectures based on NVM and compares the system goals,architectures,features,and limitations to explore new research opportunities.Moreover,the existing solutions to reduce the write latency and energy consumption and increase the lifetime of NVM IoT storage devices are analyzed.Furthermore,we discuss the security and privacy issues of IoT devices and compare the mainstream solutions.Finally,we present the opportunities and challenges of building IoT storage systems based on NVM.
基金supported by the Research Fund of National Key Laboratory of Computer Architecture under Grant No.CARCH201501the Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing under Grant No.2016A09.
文摘With the development of the nonvolatile memory(NVM),using NVM in the design of the cache and scratchpad memory(SPM)has been increased.This paper presents a data variable allocation(DVA)algorithm based on the genetic algorithm for NVM-based SPM to prolong the lifetime.The lifetime can be formulated indirectly as the write counts on each SPM address.Since the differences between global variables and stack variables,our optimization model has three constraints.The constraints of the central processing unit(CPU)utilization and size are used for all variables,while no-overlay constraint is only used for stack variables.To satisfy the constraints of the optimization model,we use the greedy strategy to generate the initial population which can determine whether data variables are allocated to SPM and distribute them evenly on SPM addresses.Finally,we use the Mälardalen worst case executive time(WCET)benchmark to evaluate our algorithm.The experimental results show that the DVA algorithm can not only obtain close-to-optimal solutions,but also prolong the lifetime by 9.17% on average compared with SRAM-based SPM.
