基于WINCE系统软件开发的内存研究

WINCE系统内存配置较小,嵌入式软件如果出现内存泄露问题,将会导致系统的可用内存不足,甚至出现系统崩溃。为此针对WINCE操作系统内存的管理和应用,通过引入一个在实时更新动态图的过程中出现内存泄露的案例,阐述了关于内存管理和内存应用的重要知识点,包括内存模型和内存分配方式,分析了在该案例中出现内存泄露的原因,提出了与案例中出现的内存泄露相似问题的解决方法,并针对一般情况下如何防止内存泄露作了简单介绍,最后详细阐述了案例中为了优化内存所做的工作,对编程者有很好的指导作用。     

高性能多媒体SoC分组访存调度算法

根据多媒体处理单元的访存特点,提出一种面向高性能多媒体SoC的分组访存调度算法.该算法将访存请求按照访存ID和页地址分组,以访存组为单位进行乱序调度,并通过维护相同ID访存请求之间的顺序保证访存的正确性:综合考虑访存单元的访存效率和服务质量要求,在每个访存单元独立的调度周期内提供最低带宽保障服务.将该分组访存调度算法应用于访存调度装置,实际应用仿真结果表明,与已有基于带宽分配的访存调度算法相比,文中算法在保障访存单元带宽需求的同时降低了访存延迟,并将平均带宽利用率提高了15%.     

实时操作系统SACOS的内存管理

研究了实时操作系统的内存管理,总结了静态内存分配和动态内存分配的优缺点。介绍了实时操作系统SACOS的特点、体系结构和目标机系统的内存映射。描述了SACOS的动态内存管理,并给出了实现SACOS动态内存管理的基础-堆的分配与回收的详细过程。SACOS的动态内存管理方法可以有效地减少内存管理开销和内存碎片,提高内存的利用率。     

事务内存机制在系统安全中的应用:现状与展望

为了提高并行程序中共享内存数据的读写访问性能,事务内存机制于1993年被提出。因为事务内存机制直接涉及内存数据的读写控制,所以也得到了系统安全研究人员的极大关注。2013年,Intel公司开始支持TSX（Transactional Synchronizatione Xtension）特性,第一次在广泛使用的计算机硬件中支持事务内存机制。利用事务内存机制的内存访问跟踪、内存访问信号触发和内存操作回滚,以及Intel TSX特性的用户态事务回滚处理、在Cache中执行所有操作和硬件实现高效率,研究人员完成了各种的系统安全研究成果,包括：授权策略实施、虚拟机自省、密钥安全、控制流完整性、错误恢复和侧信道攻防等。本文先介绍了各种基于事务内存机制的研究成果;然后分析了现有各种系统安全研究成果与事务内存机制特性之间的关系,主要涉及了3个角度：内存访问的控制、事务回滚处理、和在Cache中执行所有操作。我们将已有的研究成果的技术方案从3个角度进行分解,与原有的、不基于事务内存机制的解决方案比较,解释了引入事务内存机制带来的技术优势。最后,我们总结展望了将来的研究,包括：硬件事务内存机制的实现改进,事务内存机制（尤其是硬件事务内存机制）在系统安全研究中的应用潜力。     

一种适用嵌入式系统的自适应动态内存管理方案

实时性、可靠性、高效性的要求,使得许多嵌入式应用使用自己的内存管理方案。任何内存碎片的产生无疑都是对大块内存频繁分割造成的,适当减少对大块内存的分割,就会减少内存碎片的产生,但在减少分割内存块的同时又如何才能满足系统对内存的需求呢?文中在对当今最常用的两种内存分配算法分析的基础上提出一种新的适用于嵌入式系统的内存管理算法——自适应动态内存分配算法,重点就如何减少内存碎片,提高内存利用率,提出了新的构想与实现。望其成为嵌入式系统中内存管理算法的模板。     

开源RTOS内存管理机制分析和改进

针对开源RTOS(FreeRTOS)内存分配时间不确定及内存利用率低、不能很好支持动态内存分配等不足,研究FreeRTOS的内存管理机制并比较几种典型动态内存管理算法的优缺点。移植修改过的TLSF算法对管理机制进行改进,较小的内存分成固定大小的内存块,用一级位图索引组织,较大的内存用二级间隔表组织。实验结果表明该方法能较好地提高内存分配速度和利用率。     

大型3D场景漫游系统内存管理

在大型3D场景漫游系统中,单个资源(如模型、纹理)所需内存较大且分配和释放频繁,为了防止内存碎片的产生并提高内存分配速度,提出了一种新型内存管理方法.根据程序需求首先划分出一块或多块大的虚拟内存区域,然后基于所划分的内存区域进行内存分配和回收管理.在该管理方法中,对于程序中的小资源,使用内存池;对于大的资源,则使用伙伴系统内存管理方法.实验结果表明,该内存管理方法高效且稳定.     

面向Android系统的动态内存管理策略

对Linux内存和Android系统的PMEM（physical memory）管理机制进行了分析,提出了在Linux内存管理中增加PMEM管理区,将大块连续物理内存划分为不同的PMEM内存块进行管理并实现PMEM内存块的回收机制。实验结果表明,采用内存优化方案后,系统管理的总内存和空闲内存均大幅提升,系统整体性能明显提升。     

Magnetic flux memory effect using a magnetostrictive material-shape memory piezoelectric actuator composite

A magnetostrictive-shape memory piezoelectric material composite is proposed to realize a magnet field memory effect. An imprint electrical field enables a shape memory piezoelectric actuator, and the shape memory effect maintains a certain permeability of the magnetostrictive materials. A new magnetic flux memory effect is generated using a composite of the magnetostrictive-shape memory piezoelectric actuator and a permanent magnet. This magnetic flux memory effect can be operated with a pulsed voltage to the shape memory piezoelectric actuator, so that no energy is consumed to maintain a certain magnetic effect.     

Generalizations of the Hamming Associative Memory

This Letter reviews four models of associative memory which generalize the operation of the Hamming associative memory: the grounded Hamming memory, the cellular Hamming memory, the decoupled Hamming memory, and the two-level decoupled Hamming memory. These memory models offer high performance and allow for a more practical hardware realization than the Hamming net and other fully interconnected neural net architectures.     

改进的Spark Shuffle内存分配算法

Shuffle性能是影响大数据集群性能的重要指标,Spark自身的Shuffle内存分配算法试图为内存池中的每一个Task平均分配内存,但是在实验中发现,由于各Task对于内存需求的不均衡导致了内存的浪费和运行效率较低的问题。针对上述问题,提出一种改进的Spark Shuffle内存分配算法。该算法根据Task的内存申请量和历史运行数据将Task按内存需求分为大小两类,对小内存需求型Task作"分割化"处理,对大内存需求型Task基于Task溢出次数和溢出后等待时间分配内存。该算法充分利用内存池的空闲内存,可以在数据倾斜导致的Task内存需求不均衡的情况下进行Task内存分配的自适应调节。实验结果表明,改进后算法较原算法降低了Task的溢出率,减少了Task的周转时间,提高了集群的运行性能。     

基于线段树的高效内存管理算法及其空间优化

现有的内存管理的工作多集中在内存分配的效率上,实时性较好,但易产生内存碎片。为此,提出基于线段树的高效内存管理方法。该方法将内存地址空间划分为内存段,建立内存管理线段树,基于所建立的内存管理线段树,进行高效灵活的内存分配和回收管理,减少了内存碎片的产生。另外,针对线段树空间开销大的问题,提出了线段树空间优化的方法。实验结果表明,所提出的内存管理方法,具有效率高、产生的内存碎片少、内存管理空间开销小等优势。     

Huge Page Friendly Virtualized Memory Management

With the rapid increase of memory consumption by applications running on cloud data centers,we need more efficient memory management in a virtualized environment.Exploiting huge pages becomes more critical for a virtual machine's performance when it runs large working set size programs.Programs with large working set sizes are more sensitive to memory allocation,which requires us to quickly adjust the virtual machine's memory to accommodate memory phase changes.It would be much more efficient if we could adjust virtual machines'memory at the granularity of huge pages.However,existing virtual machine memory reallocation techniques,such as ballooning,do not support huge pages.In addition,in order to drive effective memory reallocation,we need to predict the actual memory demand of a virtual machine.We find that traditional memory demand estimation methods designed for regular pages cannot be simply ported to a system adopting huge pages.How to adjust the memory of virtual machines timely and effectively according to the periodic change of memory demand is another challenge we face.This paper proposes a dynamic huge page based memory balancing system(HPMBS)for efficient memory management in a virtualized environment.We first rebuild the ballooning mechanism in order to dispatch memory in the granularity of huge pages.We then design and implement a huge page working set size estimation mechanism which can accurately estimate a virtual machine's memory demand in huge pages environments.Combining these two mechanisms,we finally use an algorithm based on dynamic programming to achieve dynamic memory balancing.Experiments show that our system saves memory and improves overall system performance with low overhead.     

Artificial memory optimization

To solve some complicated optimization problems, an artificial memory optimization (AMO) is constructed based on the human memory mechanism. In AMO, a memory cell is used to trace an alternative solution of a problem to be solved; memorizing and forgetting rules of the human memory mechanism are used to control state transition of each memory cell; the state of a memory cell consists of two components, one is the solution state which associates with an alternative solution being traced; another is the memory state which associates with the memory information resulting from tracing results, where the memory residual value (MRV) is stored; the states of memory cells are divided into three types: instantaneous, short- and long-term memory state, each of which can be strengthened or weakened by accepted stimulus strength. If the solution state of a memory cell has transferred to a good position, its MRV will increase, and then the memory cell is not easily to be forgotten; when the solution state of a memory cell is at sticky state, its MRV will decrease until the memory cell is forgotten; this will effectively prevent invalid iteration. In the course of evolution, a memory cell may strive to evolve from the instantaneous, short-term memory state to long-term memory state, it makes search to be various. Because AMO has 6 operators at the curent version, it has wider adaptability to solve different types of optimization problems. Besides, these operators are automatically dispatched according to their executing efficiency. Results show that AMO possesses of strong search capability and high convergence speed when solving some complicated function optimization problems.     

A survey of memory management techniques in virtualized systems

Virtualization technology allows multiple operating systems to share hardware resources of a computer system in an isolated manner. Traditionally, memory is shared by an operating system using segmentation and paging techniques. With virtualization, memory partitioning and management has several new challenges. For isolated and safe execution, hypervisors do not provide direct access to hardware resources. Lack of direct access to the memory management hardware like page tables disqualifies direct usage of virtual memory solutions used on native (non-virtualized) setups. Further, aspects of dual control of the memory resource (by the guest OS and the hypervisor) and lack of semantics regarding memory usage in virtual machines present additional challenges for memory management. This paper surveys different techniques of memory partitioning and management across multiple guest OSs in a virtualized environment.An important goal of virtualization is to increase the physical machine utilization in order to save costs. With varying application demand for memory and diverse memory management policies of the guest OSs, ensuring optimal usage of memory is non-trivial. In this survey, challenges of memory management in virtualized systems, different memory management techniques with their implications, and optimizations to increase memory utilization are discussed in detail.     

The intelligent memory allocator selector

Memory fragmentation is a serious obstacle preventing efficient memory usage. Garbage collectors may solve the problem; however, they cause serious performance impact, memory and energy consumption. Therefore, various memory allocators have been developed. Software developers must test memory allocators, and find an efficient one for their programs. Instead of this cumbersome method, we propose a novel approach for dynamically deciding the best memory allocator for every application. The proposed solution tests each process with various memory allocators. After the testing, it selects an efficient memory allocator according to condition of operating system (OS). If OS runs out of memory, then it selects the most memory efficient allocator for new processes. If most of the CPU power was occupied, then it selects the fastest allocator. Otherwise, the balanced allocator is selected. According to test results, the proposed solution offers up to 58% less fragmented memory, and 90% faster memory operations. In average of 107 processes, it offers 7.16±2.53% less fragmented memory, and 1.79±7.32% faster memory operations. The test results also prove the proposed approach is unbeatable by any memory allocator. In conclusion, the proposed method is a dynamic and efficient solution to the memory fragmentation problem.     

一个基于孙子定理的素数存储系统方案

基于孙子定理,本提出一个素数存储系统方案。该方案既不浪费存储空间,且为实本系统仅需计算“ｄｍｏｄｐ”,而无需计算商。因此,本系统是一高效存储方案。     

轻量级安全内存：RISC-V嵌入式微处理器安全增强

近年来,针对嵌入式设备中硬件的新型攻击不断出现,严重威胁嵌入式设备的安全.特别是随着非易失性存储器开始被配备到嵌入式设备中,就需要考虑如何保护配备非易失性存储器的嵌入式设备的安全.安全内存,就是这样一种通过保护内存来增强嵌入式设备安全性的有效手段.通过设计一种安全内存加密引擎来实现安全内存.在保证该安全内存加密引擎足够轻量、开销低的同时,将其集成到RISC-V嵌入式微处理器中,并通过FPGA对该安全内存加密引擎进行了评估.评估结果表明,安全内存加密引擎能够在提升RISC-V嵌入式微处理器安全性的同时,保证其合理的访存性能以及较小的面积开销.研究结果具有良好的参考价值和应用前景.     

A Study on Modeling and Optimization of Memory Systems

Accesses Per Cycle(APC),Concurrent Average Memory Access Time(C-AMAT),and Layered Performance Matching(LPM)are three memory performance models that consider both data locality and memory assess concurrency.The APC model measures the throughput of a memory architecture and therefore reflects the quality of service(QoS)of a memory system.The C-AMAT model provides a recursive expression for the memory access delay and therefore can be used for identifying the potential bottlenecks in a memory hierarchy.The LPM method transforms a global memory system optimization into localized optimizations at each memory layer by matching the data access demands of the applications with the underlying memory system design.These three models have been proposed separately through prior efforts.This paper reexamines the three models under one coherent mathematical framework.More specifically,we present a new memory-centric view of data accesses.We divide the memory cycles at each memory layer into four distinct categories and use them to recursively define the memory access latency and concurrency along the memory hierarchy.This new perspective offers new insights with a clear formulation of the memory performance considering both locality and concurrency.Consequently,the performance model can be easily understood and applied in engineering practices.As such,the memory-centric approach helps establish a unified mathematical foundation for model-driven performance analysis and optimization of contemporary and future memory systems.     

Buddy算法的μC/OS-Ⅱ高可靠内存管理方案

针对现有μC/OS-Ⅱ内存管理方案分配内存不灵活、可靠性不高的特点,提出一种适用于μC/OS-Ⅱ增强内存管理可靠性的方案.该方案借鉴Buddy算法的思想,将可用内存划分为一系列2的幂次方规模大小的内存块,申请小块内存得不到分配时可以将大块内存块平分后得到满足.回收内存块时,地址连续的相同大小的内存块可以合并成大内存块,...