Accelerating AI/ML: Specialization and Efficiency

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Related lectures (32)

Page 2 of 4

Covers relational and spatial databases, including storage, management systems, ACID properties, historical typologies, primary and foreign keys, and spatial functions.

General Introduction to Big Data

Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.

Relational Databases: Fundamentals and ACID Properties

Covers relational databases, transactions, and data consistency in the context of historical database typologies.

Micro-architectural Analysis of Database Workloads

Explores the micro-architectural analysis of modern DBMS, focusing on performance optimization and data cache stall mitigation.

Computing: From 1 and 0 to Databases

Introduces computing basics, from logic gates to databases, emphasizing data integrity.

GPU Introduction: Performance and Programming

Explores the motivation and advantages of using GPUs for computation, focusing on their performance and programming through CUDA.

Data formats and data wrangling with Hadoop

Explores Apache Hive for data warehousing, data formats, and partitioning, with practical exercises in querying and connecting to Hive.

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Transactions: Concepts and Implementations

Explores the elegance and challenges of transactions, including ACID properties and transactional memory benefits in concurrent programming.

Data Modeling: Concepts and Applications

Introduces data modeling concepts, SQL usage, and Pandas library applications for efficient data processing.