第六章化学热力学初步 1解决反应热量问题 2判断变化的方向问题 6.1热力学第一定律 一、热力学中几个基本概念 1.体系和环境:热力学中研究的对象为体系称体系以外的其他部分为环境 2.状态和状态函数 3.过程和途径可逆过程 4.化学计量数和反应进度

薛定谔方程及其应用 微观粒子的基本属性不能用经典语言确切表达, “波粒二象性”借用经典语言进行互补性描述。 对微观客体的数学描述可以脱离日常生活经验,避免 借用经典语言引起的表观矛盾。 量子力学包含一套计算规则及对数学程式的物理解 释,是建立在基本假设之上的构造性理论,其正确 性由实践检验

Intro Threading Safety Solutions 1 Introduction and expectations Scope : what is parallelism ? Finding parallelism Expectations 2 Threading, theory and practice Processes and threads Python threading C ++threading and async 3 Thread-safety issues Data races Thread safety 4 Thread-safety solutions Avoid the problem Replicate Atomics Locking

1 Why python and C++ Pros and Cons of each language Respective usecases 2 C ++getting usable Language “simplifications” Making bad code harder to write 3 Performant C++ New performance related features Templates Avoiding virtuality when possible 4 Migrating from Python 2 to python 3 Tour of python 3 changes How to support both versions How to migrate 5 Conclusion

1 Measuring Performance What is performance ? Tools available Finding bottlenecks 2 Code modernization 3 Improving Memory Handling Context Containers and memory Container reservation Detecting offending code 4 The nightmare of thread safety Context and constraints Identifying problems Solving problems Thread contention 5 Low level optimizations Scope and target How to measure ? Improving 6 Conclusion

1 Asynchronous I/O Latency Asynchronous I/O interfaces Message queues 2 I/O optimizations Optimizing network transfers Optimizing local transfers 3 Influence of data structures on I/O Measuring I/O efficiency of algorithms 4 Caching Principles Policies Distributed Caches 5 Conclusion

1 Asynchronous I/O Latency Asynchronous I/O interfaces Message queues 2 I/O optimizations Optimizing network transfers Optimizing local transfers 3 Influence of data structures on I/O Measuring I/O efficiency of algorithms 4 Caching Principles Policies Distributed Caches 5 Conclusion

1 Storage devices Existing devices 2 Parallelizing files’ storage Striping Introduction to Map/Reduce 3 Risks of data loss and corruption 4 Data consistency Checksums Practical usage 5 Data safety Redundancy Parity Erasure coding 6 Conclusion

1 Measuring Performance What is performance ? Tools available Finding bottlenecks 2 Code modernization 3 Improving Memory Handling Context Containers and memory Container reservation Detecting offending code 4 The nightmare of thread safety Context and constraints Identifying problems Solving problems Thread contention 5 Low level optimizations Scope and target How to measure ? Improving Vectorization promises 6 Conclusion

Intro Threading Safety Solutions 1 Introduction and expectations Scope : what is parallelism ? Finding parallelism Expectations 2 Threading, theory and practice Processes and threads Python threading C ++threading and async 3 Thread-safety issues Data races Thread safety 4 Thread-safety solutions Avoid the problem Replicate Atomics Locking