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How did Apple Silicon get 50% faster in three years?
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20 September
Computer science professor who works on fast data processing; co-author of the simdjson parser and a weekly blogger about software performance since 2004.
18 September
Computer science professor who works on fast data processing; co-author of the simdjson parser and a weekly blogger about software performance since 2004.
Faster JSON parsing with SVE2 on ARM processors ARM processors, like those in your phone, have instructions capable of processing several elements at once (SIMD). These instructions are called NEON. But many newer processors have a different SIMD extension called SVE…
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How did AMD Ryzen get 50% faster in two years? People still tell me that CPUs are boring. That nothing much happens anymore. Let us look at AMD Ryzen 7 processors from 2022 to 2024: the 5800X3D (Zen 3), the 7800X3D (Zen 4) and the 9800X3D (Zen 5). They are comparabl…
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15 September
Computer science professor who works on fast data processing; co-author of the simdjson parser and a weekly blogger about software performance since 2004.
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Thus it appears that on the latest AMD and ARM processors subnormals might not be a concern. But they remain very much a performance issue under Intel processors.
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lemire.me
14 September
Computer science professor who works on fast data processing; co-author of the simdjson parser and a weekly blogger about software performance since 2004.
Apple design its own chips for its phones. The latest CPU is the A20 Pro. Geekbench is a benchmark with a database of results; you can run your own benchmarks and share your numbers with the world. In the last few days, we finally got some A20 Pro results and the numbers are impressive... reaching 4000 in single-core (one thread) performance. For comparison, the best that you can do with an x64 AMD or Intel processor is something like the Ryzen 9 9950X3D2 at about 3,163 which would be somewhere between the A18 and A19. The A20 Pro is 25% faster on this benchmark than any AMD or Intel processo… benchmarks Related
11 September
Computer science professor who works on fast data processing; co-author of the simdjson parser and a weekly blogger about software performance since 2004.
What should be obvious is that inference, running a large language model, is the part that has to be cheap. Most of our hardware was not designed for that. LLM inference is limited by bandwidth. You do a great many matrix-vector multiplications against a huge weight matrix, and you barely reuse those weights. It is closer to streaming a video than to running a simulation or drawing a scene in a game. Graphics processors from Nvidia and others were built for something else. They pileed compute first, then bolted on high-bandwidth memory. It is expensive. An obvious answer is to invert the desi… LLMs Related
1 September
Management writer; co-author of In Search of Excellence (1982) and author of The Excellence Dividend.
This conversation on listening is 100 times/1000 times more important than ever in an age when the attention span has more or less dropped to … ZERO‼️ Related
16 August
Programmer and teacher; writes on software, learning and the web, and makes the Coding Blocks video course.
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Dark nights, lots of coffee… This was fun!
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rakhim.org
10 June
Writer and former Substack product manager; publishes essays and interviews on technology, culture and China at jasmi.news.
5 February
Pseudonymous writer; posts careful, funny investigations of everyday questions — air quality, seed oils, arguing, chess — usually by reading the studies.
3 February
Non-fiction writer, podcaster and occasional TV host. Author of thirteen books including The Ghost Map, Where Good Ideas Come From and Extra Life, host of the PBS series How We Got To Now, and author of the Adjacent Possible newsletter on innovation.
10 March 2025
Organizational psychologist and professor at the Wharton School. Author of Give and Take, Originals, Think Again and Hidden Potential, host of the Re:Thinking podcast, and writer of the GRANTED newsletter.
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