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Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.
Discusses scheduling internals, metrics, and policies in computer systems, emphasizing efficiency and the complexities of modern multi-core architectures.
Explores scalable synchronization mechanisms for many-core operating systems, focusing on the challenges of handling data growth and regressions in OS.
Explores challenges and solutions for data center processors, focusing on efficiency, cache issues, branch prediction, and architectural optimizations.
Discusses the challenges and future of neuromorphic computing, comparing digital computers and specialized hardware, such as SpiNNaker and NEST, while exploring the Human Brain Project's Neuromorphic Computing Platform.
Explores the impact of locality on system design, discussing strategies to optimize efficiency and performance through data layout, lock design, and data movement minimization.
