基于忆阻神经网络的联想记忆，曾志刚，温世平，本文研究了基于忆阻神经网络的联想记忆。根据忆阻器二极模型的阻值的可变性，提出一种带有非对称电压阈值一维电压控制模型。由于

生产者－消费者问题 一个生产者和n个消费者共享内存 在此模拟中，一个生产者将m个元素发送给n个消费者。 这些元素以一个元素的容量存储在共享内存段中。 每个元素都包含一个随机整数和一个时间戳。 在模拟结束时，每个使用者将其pid，m个元素及其运行平均时间写入共享的result.txt文件中。 注意：运行平均时间=收集所有m个项目所需的时间。 怎么运行的 信号量： full：[0，n]，初始值为0 空：[0，n]，初始值为0 互斥锁：0 || 1，初始值为1（对于共享的results.txt文件） 操作方式： semDown（semaphore）：将信号量的值减一 semOpN（semaphore，value）：设置信号量的值 semWaitUntilZero（semaphore）：阻塞过程，直到信号量的值为零 制片人： 将新元素发布到共享内存段，将（semOpN）已满和空信号量

OtterCTF的Memory Forensics内存取证文件，vmem格式，解压即用。 此题详细解题博客：

C++内存池完整代码memory_pool Makefile

DDR的标准协议，官方协议，全英文 DDR的标准协议，官方协议，全英文

8-port sram memory cell, with 8 write or 16 reads simutaneously

This article provides an overview of recent developments in mainmemory database systems. With growing memory sizes and memory prices dropping by a factor of 10 every 5 years, data having a “primary home” in memory is now a reality. Main-memory databases eschew many of the traditional architectural pillars of relational database systems that optimized for disk-resident data. The result of these memory-optimized designs are systems that feature several innovative approaches to fundamental issues (e.g., concurrency control, query processing) that achieve orders of magnitude performance improvements over traditional designs. Our survey covers five main issues and architectural choices that need to be made when building a high performance main-memory optimized database: data organization and storage, indexing, concurrency control, durability and recovery techniques, and query processing and compilation. We focus our survey on four commercial and research systems: H-Store/VoltDB, Hekaton, HyPer, and SAP HANA. These systems are diverse in their design choices and form a representative sample of the state of the art in main-memory database systems. We also cover other commercial and academic systems, along with current and future research trends.

操作系统Operating system: Three Easy pieces，内存虚拟化部分思维导图，Chapter13-22部分知识点总结

linux memory management study note

根据论文the missing memory found仿真生成v-i图像