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RRZE-HPC
OSACA
Jupyter Notebook2018–2020
RRZE-HPC / OSACA

OSACA

Open Source Architecture Code Analyzer

Explore on GitHubRead Hacker News discussions
3Distinct people
4Mentions
0Explicit recommenders
384GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Open Source Architecture Code Analyzer

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month4 dated mentions

Observed mentions: 2018-10-09 21:48:11 UTC to 2020-05-31 18:16:19 UTC.

0122018-10-01 00:00:00 UTC to 2018-11-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 commentsOct 20182018-11-01 00:00:00 UTC to 2018-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-12-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2019-01-01 00:00:00 UTC to 2019-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-02-01 00:00:00 UTC to 2019-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-03-01 00:00:00 UTC to 2019-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-04-01 00:00:00 UTC to 2019-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-05-01 00:00:00 UTC to 2019-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-06-01 00:00:00 UTC to 2019-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-07-01 00:00:00 UTC to 2019-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsJul 20192019-08-01 00:00:00 UTC to 2019-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-09-01 00:00:00 UTC to 2019-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-10-01 00:00:00 UTC to 2019-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-11-01 00:00:00 UTC to 2019-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-12-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-01-01 00:00:00 UTC to 2020-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-02-01 00:00:00 UTC to 2020-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-03-01 00:00:00 UTC to 2020-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-04-01 00:00:00 UTC to 2020-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-05-01 00:00:00 UTC to 2020-06-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsMay 2020

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Oct 20182018-10-01 00:00:00 UTC2018-11-01 00:00:00 UTC211
Nov 20182018-11-01 00:00:00 UTC2018-12-01 00:00:00 UTC000
Dec 20182018-12-01 00:00:00 UTC2019-01-01 00:00:00 UTC101
Jan 20192019-01-01 00:00:00 UTC2019-02-01 00:00:00 UTC000
Feb 20192019-02-01 00:00:00 UTC2019-03-01 00:00:00 UTC000
Mar 20192019-03-01 00:00:00 UTC2019-04-01 00:00:00 UTC000
Apr 20192019-04-01 00:00:00 UTC2019-05-01 00:00:00 UTC000
May 20192019-05-01 00:00:00 UTC2019-06-01 00:00:00 UTC000
Jun 20192019-06-01 00:00:00 UTC2019-07-01 00:00:00 UTC000
Jul 20192019-07-01 00:00:00 UTC2019-08-01 00:00:00 UTC000
Aug 20192019-08-01 00:00:00 UTC2019-09-01 00:00:00 UTC000
Sep 20192019-09-01 00:00:00 UTC2019-10-01 00:00:00 UTC000
Oct 20192019-10-01 00:00:00 UTC2019-11-01 00:00:00 UTC000
Nov 20192019-11-01 00:00:00 UTC2019-12-01 00:00:00 UTC000
Dec 20192019-12-01 00:00:00 UTC2020-01-01 00:00:00 UTC000
Jan 20202020-01-01 00:00:00 UTC2020-02-01 00:00:00 UTC000
Feb 20202020-02-01 00:00:00 UTC2020-03-01 00:00:00 UTC000
Mar 20202020-03-01 00:00:00 UTC2020-04-01 00:00:00 UTC000
Apr 20202020-04-01 00:00:00 UTC2020-05-01 00:00:00 UTC000
May 20202020-05-01 00:00:00 UTC2020-06-01 00:00:00 UTC101

What Hacker News says

4 sources
matt_d·May 31, 2020
FWIW, there are some interesting tools for microarchitectural performance analysis which are getting closer (although it's a work in progress): https://github.com/MattPD/cpplinks/blob/master/performance.t... One particularly interesting example in this context is OSACA (Open Source Architecture Code Analyzer), https://github.com/RRZE-HPC/osaca The related publications explain the approach used to model and perform critical path analysis relevant for the modern superscalar out-of-order processors: - Automatic Throughput and Critical Path Analysis of x86 and ARM Assembly Kernels (2019): https://arxiv.org/abs/1910.00214 - Cross-Architecture Automatic Critical Path Detection For In-Core Performance Analysis (2020): https://hpc.fau.de/files/2020/02/Masterarbeit_JL-_final.pdf There's also a broader line of research on performance modeling in t
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his vein (some pretty detailed, including microarchitectural details like branch misprediction penalties, ROB capacity, etc.): https://gist.github.com/MattPD/85aad98ee8b135e675d49c571b67f... More on modeling microarchitectural details (chronological order): - Tejas S. Karkhanis and James E. Smith. "A First-Order Superscalar Processor Model." (ISCA 2004) - http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.79..... - Stijn Eyerman, Lieven Eeckhout, Tejas Karkhanis, and James E. Smith. "A mechanistic performance model for superscalar out-of-order processors." ACM Trans. Comput. Syst. 27, 2 (2009) - http://www.elis.ugent.be/~leeckhou/papers/tocs09.pdf - Maximilien B. Breughe, Stijn Eyerman, and Lieven Eeckhout. "Mechanistic analytical modeling of superscalar in-order processor performance." ACM Trans. Architec. Code Optim. 11, 4, Article 50 (2014) - https://users.elis.ugent.be/~leeckhou/papers/taco2015-breugh.... - "Modeling Superscalar Processor Memory-Level Parallelism", Sam Van den Steen and Lieven Eeckhout, IEEE Computer Architecture Letters (CAL), Vol 17, No 1 (2018) - https://users.elis.ugent.be/~leeckhou/papers/cal2018-MLP.pdf
In Hoare’s Rebuttal and Bubble Sort’s Comeback
BeeOnRope·Dec 2, 2018
You are right, the vast majority of loops should be left without unrolling. In fact, the majority of code should be compiled for size, not for speed. For a typical application you probably have 99% of the code not being very performance sensitive, and the remaining 1% is where the time is spent. Still, people regularly compile for speed to good effect, because it's so important to compile that 1% for speed that you are better off just compiling everything that way if you aren't able/willing to profile and figure what code falls in the 1%. That's nothing specific to unrolling though: it applies to all optimizations which trade off size and speed. About dependency chains and ILP: of course the compiler is in the perfect places to be aware of all of this. They have detailed machine models updated carefully as new CPUs come out (in fact, some
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of the earliest details about new CPU models often comes from compiler commits from insiders where hardware details are necessarily leaked). A compiler could certainly statically analyze a loop using an approach similar to Intel IACA or OASCA [1], and then unroll the loop a few times and run the analysis again and see if it improves. So it doesn't need any kind of sophisticated analysis, just try-and-measure. Of course, compilers don't actually work like this. One of reasons it is not so easy is that optimizations is done in layers, against a machine independent IR, and you might not be able to carefully evaluate the impact of unrolling until some later time, possibly as late the machine-dependent instruction emission. This issue is pervasive across many compiler optimizations, and leads to many cases where a compiler generates bad code where a human wouldn't. --- [1] https://github.com/RRZE-HPC/OSACA
In Why is 2 * (i * i) faster than 2 * i * i in Java?

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