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

中国科学院：CERN专题计算学校《T-CSC数据存储》课程教学资源（讲义）Data storage and preservation-pres

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 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

中国科学院：CERN专题计算学校《T-CSC数据存储》课程教学资源（讲义）Preserving data-pres

中国科学院：CERN专题计算学校《T-CSC数据存储》课程教学资源（讲义）Many ways to store data-pres

中国科学院：CERN专题计算学校《T-CSC数据存储》课程教学资源（讲义）Structuring data for efficient I/O-pres

中国科学院：CERN专题计算学校《T-CSC数据存储》课程教学资源（讲义）Optimizing existing large codebase-pres

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 Introduction 2 Measuring vectorization 3 Vectorization Prerequisite 4 Vectorizing techniques in C++ Autovectorization Inline assembly Intrinsics Compiler extensions Libraries 5 What to expect ?