# Lecture 8: GPU Computing Intro. The CUDA Programming Model. CUDA Execution Configuration.

## Lecture Summary

* Flynn's Taxonomy
* Amdahl's Law
* Start GPU computing

## Flynn's Taxonomy

* SISD: Single Instruction/Single Data
* SIMD: Single Instruction/Multiple Data
* MISD: Multiple Instruction/Single Data
* MIMD: Multiple Instruction/Multiple Data

![](/files/-MTCW1eYJShRco27pP_X)

![](/files/-MTCW5CQ3ggl5t6KLqDG)

![](/files/-MTCWDPALIAWy9R5ur0E)

![](/files/-MTCWGj8vHZqL7dTzuRu)

## Amdahl's Law (Law of Diminishing Returns)

The overall speedup relies on the worst-performing sections the most (see more explanations [here](/earlier-readings-and-notes/index/raid-a-case-for-redundant-arrays-of-inexpensive-disks.md#background-and-motivation)).

## GPU Computing with CUDA

![](/files/-MTCY1N7G585nZMvP7TZ)

![](/files/-MTCY6bbnqKgosahC27h)

![Fermi](/files/-MTCZ2upP99L7gFQCBg_)

![Volta](/files/-MTCZBoe6MnKuow3-sXs)

![Ampere](/files/-MTCZGQ_nLoQKiSyANlj)

![](/files/-MTCZR6i2dI4UQGRRF0E)


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