Introduction to Multiprocessor Architecture

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Concurrency and Parallelism: Visibility

Explores visibility in concurrency, memory models, shared memory, and hardware optimizations.

Cache Coherence: Concepts and Protocols

Explores cache coherence in multiprocessor systems, including protocols and challenges.

Efficient GPU Join Optimization

Covers efficient GPU-accelerated join optimization for complex queries, focusing on improving optimization times and heuristic plan quality.

Computer Science Evolution: Moore's Law to Multicores

Covers the evolution of computer science, from Moore's Law to multicores, research on parallelizing Lisp code, experiences at UC Berkeley and Microsoft Research, and insights on cloud computing and faculty management.

GPUs: Architecture and Programming

Explores GPUs' architecture, CUDA programming, image processing, and their significance in modern computing, emphasizing early start and correctness in GPU programming.

Cache Coherence

Covers cache coherence, sharing data in multiprocessor caches, directories, and coherence protocols.

Transitioning to Multithreaded Programming: Key Concepts

Covers the transition from single-threaded to multithreaded programming, focusing on memory models and consistency issues.

GPUs: Multithreading and Architecture

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

Principles of Parallelism

Covers the basics of parallelism, including physical examples, historical context, multicore era, and parallel collections in Scala.

Challenge of even bigger models

Explores simulating large-scale neural network models and optimizing memory efficiency in neural simulations using NEURON and CoreNEURON.