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简介：IBM Research最近在Big Data领域有很多工作，例如我们组在4月份在10台采用POWER7处理器的P730服务器上成功地用14分钟跑完了1TB数据的排序(7月份又在10台Power7R2上用8分44秒跑完了1TB排序)，这项工作已经发表为一篇IBM Research Report，欢迎大家围观，并提出宝贵意见，谢谢。
The use of Big Data underpins critical activities in all sectors of our society. Achieving the full transformative potential of Big Data in this increasingly digital world requires both new data analysis algorithms and a new class of systems to handle the dramatic data growth, the demand to integrate structured and unstructured data analytics, and the increasing computing needs of massive-scale analytics. In this paper, we discuss several Big Data research activities at IBM Research: (1) Big Data benchmarking and methodology; (2) workload optimized systems for Big Data; (3) case study of Big Data workloads on IBM Power systems. In (3), we show that preliminary infrastructure tuning results in sorting 1TB data in 14 minutes on 10 Power 730 machines running IBM InfoSphere BigInsights. Further improvement is expected, among other factors, on the new IBM PowerLinuxTM 7R2 systems.
By: Anne E. Gattiker, Fadi H. Gebara, Ahmed Gheith, H. Peter Hofstee, Damir A. Jamsek, Jian Li, Evan Speight, Ju Wei Shi, Guan Cheng Chen, Peter W. Wong
Published in: RC25281 in 2012
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2. High write throughput（高写吞吐量）
3. Efficient and low-latency strong consistency semantics within a data center（单个data center内高性能、低延迟的强一致性）
4. Efficient random reads from disk（disk的高性能随机读）
5. High Availability and Disaster Recovery（高可靠性、灾后恢复能力）
6. Fault Isolation（错误隔离）
7. Atomic read-modify-write primitives（read-modify-write原子操作）
8. Range Scans（范围扫描）
1. File Appends
2. Name Node的高可靠性优化 (AvatarNode)