Swift + Metal: The future of scientific programming?
summary
Claim: Swift + Metal can outpace Python and R and sometimes Scala on large problems, but hurts on trivial data where Accelerate wins. Experiments reproduce NumPy in Swift with a grad student, showing small-data slowdown yet as data scales performance reaches Julia/Scala parity. Benchmarks and a GitHub repo exist (uploader will post), and the implementation exploits GPU parallelism—modern GPUs expose roughly 65k threads—so the concrete takeaway is measurable speed curves, code, and posted numbers, not hype.
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chapters (15)
- 0:00Intro
- 1:22About me
- 2:02What is Metal
- 4:00Quote
- 4:26Parallelism
- 5:26Why is this important
- 7:11What is parallel programming
- 7:25Why parallel programming
- 10:43What is parallel computing
- 14:42GPU advancement
- 15:29Why Swift Metal
- 21:27democratization
- 28:06comparing languages
- 32:24the challenges of Swift
- 35:24machine learning
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