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Concurrency vs Parallelism

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Alex Xu

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40,109 görüntüleme • 10 ay önce •via X (Twitter)

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•one for the parallelism in #meemsemohabbat [ishq hai- ft. talha and roshi]
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•one for the parallelism in #meemsemohabbat [ishq hai- ft. talha and roshi]

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🔥 Outstanding interactive visualizer from Alex which demonstrates Optimistic vs Pessimistic Concurrency Control! Rialo supports both modes giving developers maximum flexibility based on their use case! This interactive demo will be live soon for anyone to play with!
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🔥 Outstanding interactive visualizer from Alex which demonstrates Optimistic vs Pessimistic Concurrency Control! Rialo supports both modes giving developers maximum flexibility based on their use case! This interactive demo will be live soon for anyone to play with!

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Day 3 of learning Go after office hours Studied about Concurrency in Go: - how Go handles concurrency - Locks and Mutex - Goroutines - Channels (sender & receiver pattern) It's very intuitive in Go coming from some experience in Java. Will deep dive more in the coming days!
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Day 3 of learning Go after office hours Studied about Concurrency in Go: - how Go handles concurrency - Locks and Mutex - Goroutines - Channels (sender & receiver pattern) It's very intuitive in Go coming from some experience in Java. Will deep dive more in the coming days!

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PWR Chain brings internet-like scalability to blockchain. 🌐 Here’s how 👇 • Others: Parallelism at smart contract level within one VM • PWR: Parallelism at VM and software application level - Supports any VM or applications - Similar to parallel servers on the internet - Efficient inter-application communication

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How Fast is Gemma 4 on a MacBook Pro M4? Benchmarking Google's new MoE (26B-A4B) > Model size: 26.1 GiB > Load time: ~4.2s Comparing single request VS > concurrent requests performance > 32k total context, 4 parallel slots single request behavior > TTFT: 5.68s > prompt: 3,701 tokens @ 652 tok/s > decode: 40.08 tok/s sequential (1 request at a time): > avg duration: 20.5s > p99: 22.1s > throughput: 40.11 tok/s > clean finishes: 100% concurrent (4 parallel requests): > aggregate throughput: 47.25 tok/s > total system throughput: 262.27 tok/s > avg duration: 65.1s > p95 latency: 68.8s > req/sec: 0.058 Head-to-Head: Sequential vs Concurrent throughput: > 40.11 tok/s → 47.25 tok/s (+17.8%) > small gain despite 4x parallelism latency per request: > 20.5s → 65.1s (~3.2x slower) > you pay heavily for concurrency system throughput (true utilization): > ~40 tok/s → 262 tok/s (~6.5x total output) > this is where concurrency wins tokens per second (decode ceiling): > ~40 tok/s steady in both modes > hardware-bound, not scheduler-bound TTFT impact: > ~5.7s baseline → buried under queueing in concurrent > “headers waittime” becomes the bottleneck What this actually means? - You don’t get linear scaling from parallel slots - You trade latency for total output - Mac Unified Memory setup is clearly saturating - Bandwidth + Scheduling overhead show up immediately This is exactly why GPUs dominate here Concurrency without killing latency

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Speed up sequential async/await code with promise concurrency But be aware of a couple critical gotchas! Read more in my latest article:

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the parallelism that there is between these two in live, #taekook there is so much subtlety and there are still pple who doubt how close they are?
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the parallelism that there is between these two in live, #taekook there is so much subtlety and there are still pple who doubt how close they are?

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this takedown ft mdzs edit is ICONIC. the cinematic parallelism of it all— jgy's deceptive sweetness, nhs dropping the hammer and wwx sweeping in for the ultimate takedown. goosebumps
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