Fast Interconnects: Scalable Co-processing with GPUs

Related lectures (30)

Page 1 of 3

Explores GPUs' architecture, multithreading, and their role in machine learning, discussing limitations and future trends.

By the instructor Mark Silberstein explores accelerator-centric OS architecture and the role of CPUs in modern computers.

Explores memory cache principles, emphasizing spatial locality, latency impact, and cache efficiency strategies.

Covers data wrangling techniques using Hadoop, focusing on row versus column-oriented databases, popular storage formats, and HBase-Hive integration.

Explores GPU architecture, multithreading, SIMD processors, and CUDA programming for parallel computing.

Covers the fundamentals of database systems, including data modeling, information processing, and the challenges of managing large volumes of data.

Explores cache memory design, hits, misses, and eviction policies in computer systems, emphasizing spatial and temporal locality.

Covers best practices and guidelines for big data, including data lakes, architecture, challenges, and technologies like Hadoop and Hive.

Explores advanced GPU memory management, CUDA programming, and data-parallel computing for optimizing performance.

Covers data wrangling techniques using Apache Hive for efficient big data management.