GPUs: Architecture and Programming

Related lectures (32)

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Covers Advanced Multiprocessor Architecture, discussing course logistics, components, grading, and trends in modern computing systems.

Covers VLIW architectures and compilers, focusing on instruction level parallelism and optimization techniques.

Explores GPU memory hierarchy, optimization challenges, and algorithm efficiency.

Covers the principles of parallel computing and introduces OpenMP for creating concurrent code from serial code.

Explores data-parallel programming with vector processors and SIMD, and introduces MapReduce, Pregel, and TensorFlow.

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

Explores GPU memory hierarchy, CUDA processing flow, optimizations, and parallelism efficiency on GPUs.

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

Explores the principles of parallel computing, focusing on OpenMP as a tool for creating concurrent code from serial code.

Covers multiprocessor architecture, sustainable computing, AI model training impact, and parallel programming fundamentals.