THE VIDEO SHELF · cpu performance tuning
CppCon 2017: Chandler Carruth “Going Nowhere Faster”
Chandler Carruth shows how to squeeze more performance out of already-optimized C++ code, covering how the CPU executes it. The talk goes into pipelining, branch prediction and speculative execution using benchmarks.
Why HN readers recommended it
Recommended as the best presentation on how modern CPUs run code, and why speculative execution matters for performance.
This is a summary from the source dataset, not an endorsement by Hacker News or the video creator.
Video & recommendation details
- Creator / channel
- CppCon
- Topic
- cpu performance tuning
- Format
- talk
- Level
- advanced
- HN recommenders in dataset
- 2
- HN mentions in dataset
- 5
- Recommendation years in dataset
- 2017–2026
These counts and years describe the included Hacker News records; they are not YouTube views or the video’s publication date.
From the original HN conversations
Excerpts from the recorded comments. Follow each source for the full comment and surrounding discussion.
The best presentation I've seen about CPU performance related to pipelining, branch prediction, and speculative execution was Chandler Carruth's "Going Nowhere Faster" presentation at CppCon 2017 [0]. I do recommend watching the whole presentation, but if you watch nothing else then just watch the 5 …
inetknght on Hacker News · 2026-04
In Visualizing CPU Pipelining (2024)Disclaimer: I am not an expert and have not measured. This is armchair theory. But, I would argue two things. First, the former appears to have at least one unaligned arithmetic: > 400538: mov 0x200b01(%rip),%rdx # 601040 <counter> ...while the latter's equivalent instruction is 4-byte …
inetknght on Hacker News · 2017-12
In Intel i7 loop performance anomaly (2013)Chandler Carruth has a good talk with an example of why speculative execution is critical for performance. [VIDEO] It starts around 36m13s time frame.
pkaye on Hacker News · 2018-11
In Researchers discover seven new Meltdown and Spectre attacks
Summaries, classifications and recommendation records are from the HackerTube source dataset. Read about this library and its limitations.