Accelerated Data Management Systems

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Related lectures (32)

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Data Management Challenges: Hardware and Query Optimization

Explores hardware changes, query optimization, workload distribution, and effective strategies for academia and work-life balance.

GPUs: Multithreading and Architecture

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

Real-time Intelligence: Data Challenges and Hardware Evolution

Explores data challenges and hardware evolution for real-time intelligence in the era of big data.

Accelerator-centric OS Architecture

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

GPUs: Architecture and Programming

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

Multiprocessors: Overview and Challenges

Covers the evolution and challenges of multiprocessors, emphasizing energy efficiency, parallel programming, cache coherence, and the role of GPUs.

GPU Introduction: Performance and Programming

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

Fast Interconnects: Scalable Co-processing with GPUs

Explores the use of fast interconnects for scalable co-processing with GPUs in databases, emphasizing the importance of overcoming the transfer bottleneck and reevaluating assumptions for performance improvements.

Topology Imperfections: Overcoming Hybrid CPU-GPU Analytics

Explores overcoming topology imperfections in analytics through hybrid CPU-GPU architectures and load balancing.

GPUs: Introduction to CUDA

Introduces the basics of GPUs, CUDA programming, and thread synchronization for parallel computing applications.