Our original product, which we’re retroactively calling Pyston-full, is a fork of the entire CPython codebase. Having users install a fully-custom version of Python lets us make changes across the Python implementation, leading to the most optimizations and largest speedups.
但這種方式的安裝與維護都需要另外搞，而且因為 ABI 不相容的問題，遇到一些套件可能會需要自己編 (甚至自己改？)，不能直接用編好的 binary：
The flip side is that it is fairly intensive to set up. While we believe Pyston-full is one of the most highly-compatible alternative Python implementations available, it can be difficult to switch Python implementations regardless of the ease of use of either implementation. Compounded on this, we decided to break the ABI which requires users to recompile extension modules. In theory this is not a big deal, but in practice the lack of available binary packages is a significant disincentive to use an alternative implementation.
The sum of all of this was that while we were very happy to achieve a 30% speedup with Pyston-full, it was very difficult to get people to start using it. We decided to try a different form factor: a pip-installable extension module called Pyston-lite.
但效能的提昇就不像 Pyston-full 這麼高，Pyston-lite 只剩下 10% 了：
So while it’s a bit difficult to accept that we are now providing a 10% speedup instead of 30%, we’ve decided that it’s much more important to provide something that people are willing to use.
In the longer-term future we are planning to submit our JIT upstream as well, but we expect retargeting it to 3.11 to be significantly more work than the other versions due to the extensive amount of changes that were made to the interpreter in that version.
In this latest release, PostgreSQL improves on its in-memory and on-disk sorting algorithms, with benchmarks showing speedups of 25% - 400% based on which data types are sorted.
PostgreSQL 15 adds support for LZ4 and Zstandard (zstd) compression to write-ahead log (WAL) files, which can have both space and performance benefits for certain workloads.
What is surprising is that a 14kB page can load much faster than a 15kB page — maybe 612ms faster — while the difference between a 15kB and a 16kB page is trivial.
Sending multiple packets into the network without any delay between them creates a packet burst that might cause short-term congestion and losses. Implementations MUST either use pacing or limit such bursts to the initial congestion window, which is recommended to be the minimum of 10 * max_datagram_size and max(2* max_datagram_size, 14720)), where max_datagram_size is the current maximum size of a datagram for the connection, not including UDP or IP overhead.
It turns out that Chrome actively throttles requests, including those to cached resources, to reduce I/O contention. This generally improves performance, but will mean that pages with a large number of cached resources will see a slower retrieval time for each resource.
首先是在 -O3 的情況下 (也就是作者使用的參數)，可以看到類似的結果：(我桌機的 CPU 是定速，沒有跑動態調整)
$ repeat 10 ./a
[-] Took: 248830 ns.
[-] Took: 249150 ns.
[-] Took: 248760 ns.
[-] Took: 248730 ns.
[-] Took: 248770 ns.
[-] Took: 248861 ns.
[-] Took: 248760 ns.
[-] Took: 253050 ns.
[-] Took: 248640 ns.
[-] Took: 249211 ns.
$ repeat 10 ./b
[-] Took: 686660 ns.
[-] Took: 696090 ns.
[-] Took: 696310 ns.
[-] Took: 694431 ns.
[-] Took: 691971 ns.
[-] Took: 697690 ns.
[-] Took: 693241 ns.
[-] Took: 692900 ns.
[-] Took: 654751 ns.
[-] Took: 679101 ns.
從版本 A 的 objdump -d -S -M intel a 可以看到作者 screenshot 內也有看的 unroll 與 SSE2 指令集：
$ repeat 10 ./a
[-] Took: 571140 ns.
[-] Took: 570280 ns.
[-] Took: 571271 ns.
[-] Took: 573971 ns.
[-] Took: 571981 ns.
[-] Took: 569650 ns.
[-] Took: 566361 ns.
[-] Took: 571600 ns.
[-] Took: 571330 ns.
[-] Took: 571030 ns.
$ repeat 10 ./b
[-] Took: 697521 ns.
[-] Took: 696961 ns.
[-] Took: 696201 ns.
[-] Took: 694921 ns.
[-] Took: 696930 ns.
[-] Took: 695001 ns.
[-] Took: 701661 ns.
[-] Took: 698100 ns.
[-] Took: 702430 ns.
[-] Took: 702641 ns.
從 objdump 可以看到版本 A 的變化，退化成一次只處理一個，但把所有的數字都用 xmmN 存放計算：
$ repeat 10 ./a
[-] Took: 1097091 ns.
[-] Took: 1092941 ns.
[-] Took: 1092501 ns.
[-] Took: 1091991 ns.
[-] Took: 1092441 ns.
[-] Took: 1093970 ns.
[-] Took: 1091341 ns.
[-] Took: 1093931 ns.
[-] Took: 1094111 ns.
[-] Took: 1092231 ns.
$ repeat 10 ./b
[-] Took: 2703282 ns.
[-] Took: 2705933 ns.
[-] Took: 2703582 ns.
[-] Took: 2702622 ns.
[-] Took: 2703043 ns.
[-] Took: 2702262 ns.
[-] Took: 2703352 ns.
[-] Took: 2703532 ns.
[-] Took: 2703112 ns.
[-] Took: 2702533 ns.
When taking the geometric mean of all the Python benchmarks I carried out for this article on the AMD Ryzen 9 5950X, Python 3.11 Beta was about 41% faster overall than the current Python 3.10.4 stable release or 45% over the aging Python 3.8 series.
Our next generation, Graviton3 processors, deliver up to 25 percent higher performance, up to 2x higher floating-point performance, and 50 percent faster memory access based on leading-edge DDR5 memory technology compared with Graviton2 processors.
