This chapter explores the foundational concepts of GPUs, focusing on parallelization in graphics hardware and machine learning. It discusses how GPUs excel at parallel computation through efficient matrix operations, contrasting with the sequential nature of CPUs. The conversation also touches on challenges like workload distribution, software patterns for parallel programming, and the complexities of memory management in GPU programming.

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