# 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

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

The overall speedup relies on the worst-performing sections the most (see more explanations [here](https://blog.ruipan.xyz/index/raid-a-case-for-redundant-arrays-of-inexpensive-disks#background-and-motivation)).

## GPU Computing with CUDA

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![Fermi](https://1313833672-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MMTslgmrrtRXvxD2lk9%2F-MTBxAGuam68wxVzc68S%2F-MTCZ2upP99L7gFQCBg_%2FScreen%20Shot%202021-02-10%20at%202.09.29%20PM.png?alt=media\&token=165eba52-b50c-48d5-9416-14ef0450c6f1)

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![Ampere](https://1313833672-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MMTslgmrrtRXvxD2lk9%2F-MTBxAGuam68wxVzc68S%2F-MTCZGQ_nLoQKiSyANlj%2FScreen%20Shot%202021-02-10%20at%202.10.27%20PM.png?alt=media\&token=4d921148-1ccf-4a7e-8305-c3b30b818a16)

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