GPUs: Architecture and Programming

Related lectures (32)

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Covers simultaneous multithreading, its implementation, and its impact on processor performance.

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

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

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

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

Discusses GPU memory hierarchy and optimization strategies for efficient memory access and execution.

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