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 Risks of data loss and corruption 2 Data consistency Checksums Block checksums 3 Data safety Redundancy Parity Erasure coding 4 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）Optimizing existing large codebase-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）Preserving data-pres

1 Storage devices Existing devices Hierarchical storage 2 Distributed storage Data distribution Data federation 3 Parallelizing files’ storage Striping Introduction to Map/Reduce 4 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）Many ways to store data-pres

1 Data format Row vs Column 2 Compressing data Compression algorithms Efficiency and use cases 3 Data addressing Hierarchical namespaces Limitations Flat namespaces 4 Stateful interfaces POSIX Limitations Stateless interfaces 5 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）Structuring data for efficient I/O-pres