Tracing JITs and Modern CPUs: Part 2

https://github.com/lukego/blog/issues/6

by lmh • 11 years ago

69 20 11 years ago

github.com

LForLambda• 11 years ago

Is it a thing to blog with github issues now?

Everlag• 11 years ago

In theory blogging on github issues could actually be quite convenient.

- Great(==standardized) markdown support.

- Easy image support.

- Uptime is someone else's problem.

- Sending someone your blog also directly links them to your ~entire public codebase.

- Builtin sane commenting system.

- Can keep code beside the blog. Its aggravating to find a living blog that links to a dead code host.

- Free with zero setup cost for everything above.

- Not having to deal with ruby dependencies for jekyll...

Edit: You know, I think I'm going to host my blog in this manner. When writing the above list I realized I'm sick of having to handle each of those.

golergka• 11 years ago

All of that works with hosting md files in github repo or in gists, but with them you also get a convenient version control system also known as git.

fche• 11 years ago

(Seems like an "embarrassingly serial" problem -- a smarter lua could turn it into a single "set c = 1e9" instruction.)

vidarh6• 11 years ago

It could, but then we're getting into hairy language definition issues: Does Lua specify at what point updates need to hit memory? And this example is perfect example of why a language ought to be explicit about such guarantees - in this case such a change would have observable effects. Changing observable effects in optimizations is dangerous territory even when it's well defined.

Dylan168076• 11 years ago

What's memory?

Lua's much more high level than that, and it makes no promises about timing at all.

kendalharland• 11 years ago

Forgive my ignorance, but when writing a sufficiently large program, why would looking into the generated assembly be of use to a programmer? Wouldn't this turn into an arbitrarily large amount of instructions to sift through just to measure performance?

tekacs• 11 years ago

You find the 'hottest' code (usually a loop, the code executed most) and examine very carefully the generated assembly for that and how it can be made more CPU/JIT/VM-friendly.

This is an optimisation you perform when a small amount of code is consuming the overwhelming majority of your software's execution time (code very commonly has this property, until it is profiled and optimised).

In addition, this case is for a high speed networking library, where every CPU cycle counts.

Tracing JITs and Modern CPUs: Part 2

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