風扇的出風孔的遮罩,不同形狀產生噪音的差異

前幾天在 Hacker News Daily 上看的文章:「Effects of grill patterns on fan performance/noise (2011) (pugetsystems.com)」,原文在「Effects of Grill Patterns on Fan Performance/Noise」。文章在講電腦風扇的出風口擋板的樣式對噪音的影響。這是一篇 2011 的老文章,但看了一下好像沒有什麼新資料...

出風口的遮罩在有些地區似乎是法律要求,主要是怕小朋友手指戳進去,以及成年人不小心碰到,所以就會有專門的測試項目在跑:

I remember back in 2000 ish I worked in R&D of a PC manufacturer and had to check the new PSUs and cases with a 'Test Finger' to make sure all the holes were small enough. The test finger was really expensive IIRC.

但回到這篇的重點,噪音與風量的差異,所以基準值是拔掉遮罩的部份先測試 (所以可以知道最低的噪音與最大的風量),然後上各種遮罩測差異。

Mesh 增加的噪音是最少,但風量有點差:

Wire 增加的噪音也很少,風量也達到最高:

從倒數的方向來看,Turbine 是最吵風量也最少的:

其實意外發現比較差的遮罩增加的噪音不算少,把很多風的動能變成聲音的能量了...

CPU Core 之間溝通的時間成本

Hacker News 上看到「Measuring CPU core-to-core latency (github.com/nviennot)」這篇,專案在「Measuring CPU core-to-core latency」這裡,看起來是個有趣的研究,測試許多不同 CPU 內,跨 core 之間溝通的時間花費。

依照專案的說明,測試的方式是利用 cache coherence 來來量測:

We measure the latency it takes for a CPU to send a message to another CPU via its cache coherence protocol.

By pinning two threads on two different CPU cores, we can get them to do a bunch of compare-exchange operation, and measure the latency.

裡面已經測了很多不同的 CPU,然後可以看到一些有趣的結果。

像是第一張圖片的「Intel Core i9-12900K @ 8+8 Cores (Alder Lake, 12th gen) 2021-Q4」這組,大家還蠻好奇 CPU #8 到底是怎麼一回事,跨 core 溝通的 latency 特別低,還特別找了 CPU 的 die 圖片看看:

另外一個是 AWS 上的 c6a.metal,機種是「AMD EPYC 7R13 @ 48 Cores (Milan, 3rd gen) 2021-Q1」,可以看到被分成了六個區塊:

接下來在 ARM 平台,在更多 CPU core 的 c7g.16xlarge 上,機種「AWS Graviton3 @ 64 Cores (Arm Neoverse, 3rd gen) 2021-Q4」,會看到更多不平均的現象:

早一點的 c6gd.metal 雖然也還是 ARM 的 64 cores 機種「AWS Graviton2 @ 64 Cores (Arm Neoverse, 2nd gen) 2020-Q1」,但可以看到很不一樣的 latency pattern:

大致上可以感覺到當 core 數愈多就會有很多技術上的瓶頸,導致不同 core 之間的溝通成本不一樣... 這個感覺跟當初學到 NUMA 的情況有點像。

網頁大小 14KB 與 15KB 的速度差異

Hacker News 上看到「Why your website should be under 14kB in size」這篇,對應的討論在「A 14kb page can load much faster than a 15kb page (endtimes.dev)」,在講網頁大小 14KB/15KB 的速度差異比 15KB/16KB 大很多的問題:

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.

原因是 TCP slow start 造成的:

This is because of the TCP slow start algorithm.

而網頁這邊 TCP slow start 目前大多數的實做都是 10 packets 後發動:

Most web servers TCP slow start algorithm starts by sending 10 TCP packets.

然後再組合 1500 bytes/packet 以及 overhead,就差不多是 14KB 了:

The maximum size of a TCP packet is 1500 bytes.

This this maximum is not set by the TCP specification, it comes from the ethernet standard

Each TCP packet uses 40 bytes in its header — 16 bytes for IP and an additional 24 bytes for TCP

That leaves 1460 bytes per TCP packet. 10 x 1460 = 14600 bytes or roughly 14kB!

然後 HTTP/3 也可以看到類似的設計 (出自「QUIC Loss Detection and Congestion Control」:

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.

算是一個小知識... 但對於現在肥滋滋的網頁效果來說就沒辦法了,而且考慮到大一點的網站會在一個 TCP 連線裡面可能會傳很多 request,其實早就超過 TCP slow start 的門檻了。

CloudFront 支援 HTTP/3

雖然 HTTP/3 還沒有進到 Standard Track,但看到 CloudFront 宣佈支援 HTTP/3 了:「New – HTTP/3 Support for Amazon CloudFront」。

只要在 CloudFront 的 console 上勾選起來就可以了:

看了看 RFC 9114: HTTP/3 文件裡的描述,client 可以試著建立 UDP 版本的 QUIC 連線,但要有機制在失敗時回去用 TCPHTTP/2 或是 HTTP/1.1

A client MAY attempt access to a resource with an "https" URI by resolving the host identifier to an IP address, establishing a QUIC connection to that address on the indicated port (including validation of the server certificate as described above), and sending an HTTP/3 request message targeting the URI to the server over that secured connection. Unless some other mechanism is used to select HTTP/3, the token "h3" is used in the Application-Layer Protocol Negotiation (ALPN; see [RFC7301]) extension during the TLS handshake.

Connectivity problems (e.g., blocking UDP) can result in a failure to establish a QUIC connection; clients SHOULD attempt to use TCP-based versions of HTTP in this case.

另外一條路是在 TCP 連線時透過 HTTP header 告訴瀏覽器升級:

An HTTP origin can advertise the availability of an equivalent HTTP/3 endpoint via the Alt-Svc HTTP response header field or the HTTP/2 ALTSVC frame ([ALTSVC]) using the "h3" ALPN token.

像是這樣:

Alt-Svc: h3=":50781"

然後 client 就可以跑上 HTTP/3:

On receipt of an Alt-Svc record indicating HTTP/3 support, a client MAY attempt to establish a QUIC connection to the indicated host and port; if this connection is successful, the client can send HTTP requests using the mapping described in this document.

另外在 FAQ 裡面有提到啟用 HTTP/3 是不另外計費的,就照著本來的 request 費用算:

Q. Is there a separate charge for enabling HTTP/3?

No, there is no separate charge for enabling HTTP/3 on Amazon CloudFront distributions. HTTP/3 requests will be charged at the request pricing rates as per your pricing plan.

先開起來玩看看...

幾個其他 Teams 的替代方案 (但還是連到 Teams 伺服器)

這邊講的替代方案不是換掉 Teams,而是找其他的方法連上 Teams 伺服器,畢竟用 Teams 的人大多都沒得選...

在「Teams is killing my Mac every day (microsoft.com)」這邊看到的一些資料可以嘗試,裡面有很多抱怨 Teams 的問題,但還是有些人有給出一些 workaround。

大家主要遇到的問題除了 CPU 吃很兇以外,另外就是記憶體這塊。

一種方法是是用 Edge 瀏覽器的 extension 來跑,我本來想看看 Linux 上的 Brave 能不能裝,但沒有看到對應的安裝連結,大概是 Edge 限定:

If you don't want to use the Microsoft Teams app (which uses a lot of resources), you can:

1. Install the Microsoft Edge Web browser on your Mac

2. Log into https://teams.microsoft.com

3. Click ... > Apps > Install this site as an app

This will create an Edge app for Teams that uses almost no resources but has feature parity with the regular Microsoft Teams app.

We tell all of our students to do this, and it has solved all Microsoft Teams performance issues on student Macs (both Intel and Apple Silicon).

另外有人提到其實官方是有放 M1 的 preview 版本的,雖然不是正式版,但總是比 Intel 版本會好一些:

If you're running an Apple Silicon Mac you can get an early build of Teams osx-arm64 from the exploration build link listed here.[0]

I've been running a daily build for a few weeks and it's noticeably better than the Intel build on an M1 Pro. It launches in half the time and feels far more responsive (probably due to not needing to use the Rosetta JIT for Electron). That said it's still a daily "exploration" build so YMMV.

[0] https://raw.githubusercontent.com/ItzLevvie/MicrosoftTeams-msinternal/master/defconfig

據說會少吃一點點記憶體,就真的大概一點點:

Can confirm it is snappier on a M1 Macbook Pro and using *less* RAM, maybe about 10% less.

但據說這個 preview 版本在自我更新時會跳到 Intel 版本,還要再找一下 workaround 關掉自動更新:

How do you prevent it from automatically updating to the Intel version? I keep downloading the preview builds and they keep getting updated.

後面還有看到有人說他直接實體隔離,把這些肥滋滋的 app 跑在另外一台 Mac 上,然後透過 Universal Control 使用,大多數的情況下都夠用,真的有需要分享畫面時再跑在自己機器上,用完就可以關掉:

Thanks for the tip. I'll give this a try!

For work, I have to run Microsoft Teams, Slack, and Discord. Of those 3, Slack surprisingly uses the least amount of memory (~700 MB), and Teams uses the most (~1.5 GB). I dusted off an old Intel Mac (literally) and interact with it using Universal Control. It only runs those 3 chat apps + mail. It's turned out to be a great way to offload resource hogs and as an added benefit, it minimizes distractions. I'll occasionally glance at the dock to see if there are any notification badges, whereas on my main Mac, I'd feel compelled to deal with notifications immediately.

When I have to share my screen or focus on a conversation, I'll fire up one of those 3 apps on my main (M1) Mac and quit it when I'm done.

Universal Control still feels rough around the edges, but it has saved me from ditching my Macbook Air and shelling out for an M1 Macbook Pro. Sometimes there are issues with reconnecting to the Intel Mac, but it seems to resolve itself if I wait a bit or turn off/on wifi.

大家都在找方法 XDDD

Firefox 的 RCWN (Race Cache With Network)

前幾天 Hacker News 上看到「When network is faster than browser cache (2020) (simonhearne.com)」這則 2020 的文章,原文在「When Network is Faster than Cache」這邊,講 Firefox 在 2017 年導入了一個特別的設計,除了會在 cache 裡面抓資料以外,也會到網路上拉看看,有機會從網路上抓到的資料會比 cache 先得到,這個功能叫做 RCWN (Race Cache With Network):「Enable RCWN」。

開頭就先提到了有人回報 Firefox 上的 RCWN 似乎沒有明顯效果:「Tune RCWN racing parameters (and make them pref-able)」。

On my OSX box I'm seeing us race more than we probably need to:

Total network request count: 5574
Cache won count 938
Net won count 13

That's racing almost 16% of the time, but only winning 1.3% of the time. We should probably back off on racing a bit in this case, at least.

16% 的 request 決定 RCWN 兩邊打,但裡面只有 1.3% 是 network 比 cache 快。

不過作者決定試著再多找看看有沒有什麼方向可以確認,但測了很多項目都找不到哪個因素跟 cache retrieval time 有直接相關,反而在看看 Chromium 時發現 Chromium 是透過限制連線數量,降低 I/O 造成的問題:

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.

看起來就是個簡單粗暴的 workaround...

Git 2.37.0 對巨大 Monorepo 的加速功能 FSMonitor

這邊用 GitHub 寫的說明好了:「Improve Git monorepo performance with a file system monitor」。

從 2.37.0 開始,Windows 與 Mac 版的使用者可以透過 FSMonitor 的功能記錄檔案系統的變化,大幅減少需要 scan 整個 repository 的時間,可以看到啟用後對於像是 chromium 這種大型專案的 status 時間就大幅下降了:

不過 Linux 還沒支援,目前我的環境都是 Linux,就沒辦法用了...

翻一下 Linux container 的各種 overhead

想要查一下 Linux 下跑 container 的 overhead,發現大多都是 2014~2016 左右的文章,而且基本上都是 Docker,好像沒什麼新資料,但還是整理整理...

首先是「What is the runtime performance cost of a Docker container?」這篇,裡面的答案有提到 CPU、Memory 以及 I/O 看起來 overhead 都不高,主要是網路的 latency 增加不少:

看起來大約是 40µs 的增加 (0.04ms),這個量級雖然看起來很小,但對於本來就是透過 Ethernet 溝通的的應用來說,平常可能都是 <1ms 了,0.04ms 的增加可能還是有影響 (像是 TCP 的 3-way handshake)。

另外一篇是 Percona 的「Measuring Percona Server Docker CPU/network overhead」,不過這邊是測 CPU bound 的方式,沒有碰到 heavy I/O:

可以看到網路層的變化造成 tps 的變化,也符合在 Stack Overflow 上面找到的文章。

Oracle 官方的「MySQL with Docker - Performance characteristics」這篇則是測到 I/O bound 的應用,畢竟資料庫軟體會用到很多一般 I/O 測試不會用到的 flag,像是 InnoDB 大家通常都會啟用 O_DIRECT

For these tests, we used a custom configuration file. We first deliberately set the buffer pool size to around 10% of the total database size in order to increase I/O-bound load. The database size was 2358MB, so we set our buffer pool size to 256MB. We then increased the buffer size to 16384MB to see what happens when Docker isn’t bound by I/O load.

文章後面有列出數字,可以看到 I/O bound 的應用似乎沒有什麼影響,而 network bound 的時候可以看到效能的下降。

不過得注意這些資料都是六年前的資料了,沒有什麼新資料可以看做應該是沒什麼改變,但畢竟不是 100% 確定的事情...

從簡單的 C 語言函式來看現代 Compiler 使用 SIMD 的威力

兩個禮拜前在 Hacker News Daily 上看到這篇很精彩的問題與分析,裡面展現出了現代 compiler 最佳化的能力,大量使用了 SIMD 來衝效能:「Why does this code execute more slowly after strength-reducing multiplications? (stackoverflow.com)」,原文在 Stack Overflow 上:「Why does this code execute more slowly after strength-reducing multiplications to loop-carried additions?」。

這篇會很長,除了本來 Stack Overflow 上的討論以外,我另外自己測 GCC 9.4.0 不加上 -O、加上 -O-O3,發現這次 Stack Overflow 給的範例剛剛好把這幾個常見的最佳化等級都練出不同結果,算是蠻厲害的題目。

作者一開始是寫了一個很簡單的版本 A,會透過 loop (對 i 進行) 計算 A*i^2 + B*i + C 的值,把結果放到 array 裡面:

double data[LEN];

void compute()
{
    const double A = 1.1, B = 2.2, C = 3.3;

    int i;
    for(i=0; i<LEN; i++) {
        data[i] = A*i*i + B*i + C;
    }
}

透過一些紙本公式計算可以知道,每次遞增的值雖然不是固定值,但也是有規律的:

所以可以改寫成一堆加號的版本 B:

void compute()
{
    const double A = 1.1, B = 2.2, C = 3.3;
    const double A2 = A+A;
    double Z = A+B;
    double Y = C;

    int i;
    for(i=0; i<LEN; i++) {
        data[i] = Y;
        Y += Z;
        Z += A2;
    }
}

理想上版本 A 在 loop 內用到三個乘法與兩個加法,而版本 B 只用到了三個加法,預期版本 B 應該會快不少,但實際上跑出來的結果剛好反過來:版本 B 慢了許多。

作者實際用 objdump 拉出來看,粗粗看下來也會發現版本 A 的指令多很多:

而版本 B 的指令簡單很多:

在討論下面已經有人給出解釋,主要的原因包括了兩個。

首先是現代 CPU 靠著暴力電路解決,乘法速度跟加法其實不像以前差那麼多,可以從 Instruction tables 這邊看到 MUL 類的指令速度雖然不能跟加法相比,但其實不算慢了,反倒是 DIV 整數除法類的指令比較痛。

另外一個原因,如果仔細看作者貼的 screenshot 分析會發現,在版本 A 裡面,一個 loop 其實做了四次 i 的運算 (add rax, 0x20),而版本 B 只做了一個 i 的運算 (add rax, 0x8),這邊 compiler 幫你 unroll 最佳化改用 SIMD 處理掉了。

在 Stack Overflow 的回答裡面,有人給了一段不錯的 code 示意,提到版本 A 其實先被展成像是這樣的程式碼:

int i;
for (i = 0; i < LEN; i += 4) {
    data[i+0] = A*(i+0)*(i+0) + B*(i+0) + C;
    data[i+1] = A*(i+1)*(i+1) + B*(i+1) + C;
    data[i+2] = A*(i+2)*(i+2) + B*(i+2) + C;
    data[i+3] = A*(i+3)*(i+3) + B*(i+3) + C;
}

然後被 SIMD 包起來處理掉了。

我把作者的 code (他有貼在 GitHub Gist 上) 拿下來編,用不同的 -O-O3 測試,然後去讀 assmebly 的部份也可以看到很多有趣的東西...

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:

13a0:       66 0f 6f c2             movdqa xmm0,xmm2
13a4:       48 83 c0 20             add    rax,0x20
13a8:       66 0f fe d6             paddd  xmm2,xmm6
13ac:       f3 0f e6 f8             cvtdq2pd xmm7,xmm0
13b0:       66 0f 28 cf             movapd xmm1,xmm7
13b4:       66 0f 70 c0 ee          pshufd xmm0,xmm0,0xee
13b9:       66 0f 59 cd             mulpd  xmm1,xmm5
13bd:       f3 0f e6 c0             cvtdq2pd xmm0,xmm0
13c1:       66 0f 59 cf             mulpd  xmm1,xmm7
13c5:       66 0f 59 fc             mulpd  xmm7,xmm4
13c9:       66 0f 58 cf             addpd  xmm1,xmm7
13cd:       66 0f 58 cb             addpd  xmm1,xmm3
13d1:       0f 29 48 e0             movaps XMMWORD PTR [rax-0x20],xmm1
13d5:       66 0f 28 c8             movapd xmm1,xmm0
13d9:       66 0f 59 cd             mulpd  xmm1,xmm5
13dd:       66 0f 59 c8             mulpd  xmm1,xmm0
13e1:       66 0f 59 c4             mulpd  xmm0,xmm4
13e5:       66 0f 58 c1             addpd  xmm0,xmm1
13e9:       66 0f 58 c3             addpd  xmm0,xmm3
13ed:       0f 29 40 f0             movaps XMMWORD PTR [rax-0x10],xmm0
13f1:       48 39 c2                cmp    rdx,rax
13f4:       75 aa                   jne    13a0 <compute+0x40>

而版本 B 的 objdump -d -S -M intel b 也符合作者提到的現象:

1340:       f2 0f 11 08             movsd  QWORD PTR [rax],xmm1
1344:       48 83 c0 08             add    rax,0x8
1348:       f2 0f 58 c8             addsd  xmm1,xmm0
134c:       f2 0f 58 c2             addsd  xmm0,xmm2
1350:       48 39 d0                cmp    rax,rdx
1353:       75 eb                   jne    1340 <compute+0x30>

但把 gcc 改成 -O 後,可以看到版本 A 的速度慢很多,但還是稍微比版本 B 快一些:

$ 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:

11b1:       66 0f ef c9             pxor   xmm1,xmm1
11b5:       f2 0f 2a c8             cvtsi2sd xmm1,eax
11b9:       66 0f 28 c1             movapd xmm0,xmm1
11bd:       f2 0f 59 c4             mulsd  xmm0,xmm4
11c1:       f2 0f 59 c1             mulsd  xmm0,xmm1
11c5:       f2 0f 59 cb             mulsd  xmm1,xmm3
11c9:       f2 0f 58 c1             addsd  xmm0,xmm1
11cd:       f2 0f 58 c2             addsd  xmm0,xmm2
11d1:       f2 0f 11 04 c2          movsd  QWORD PTR [rdx+rax*8],xmm0
11d6:       48 83 c0 01             add    rax,0x1
11da:       48 3d 40 42 0f 00       cmp    rax,0xf4240
11e0:       75 cf                   jne    11b1 <compute+0x28>

而版本 B 在 -O 的情況下基本上是一樣的東西 (所以速度上差不多):

11b3:       f2 0f 11 08             movsd  QWORD PTR [rax],xmm1
11b7:       f2 0f 58 c8             addsd  xmm1,xmm0
11bb:       f2 0f 58 c2             addsd  xmm0,xmm2
11bf:       48 83 c0 08             add    rax,0x8
11c3:       48 39 d0                cmp    rax,rdx
11c6:       75 eb                   jne    11b3 <compute+0x2a>

再來是拔掉 -O,都不加就會超慢:

$ 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.

看 objdump 就可以發現幾乎都是對記憶體操作,沒有放到 register 裡面,這是版本 A:

11c1:       f2 0f 2a 45 e4          cvtsi2sd xmm0,DWORD PTR [rbp-0x1c]
11c6:       66 0f 28 c8             movapd xmm1,xmm0
11ca:       f2 0f 59 4d e8          mulsd  xmm1,QWORD PTR [rbp-0x18]
11cf:       f2 0f 2a 45 e4          cvtsi2sd xmm0,DWORD PTR [rbp-0x1c]
11d4:       f2 0f 59 c8             mulsd  xmm1,xmm0
11d8:       f2 0f 2a 45 e4          cvtsi2sd xmm0,DWORD PTR [rbp-0x1c]
11dd:       f2 0f 59 45 f0          mulsd  xmm0,QWORD PTR [rbp-0x10]
11e2:       f2 0f 58 c1             addsd  xmm0,xmm1
11e6:       f2 0f 58 45 f8          addsd  xmm0,QWORD PTR [rbp-0x8]
11eb:       8b 45 e4                mov    eax,DWORD PTR [rbp-0x1c]
11ee:       48 98                   cdqe   
11f0:       48 8d 14 c5 00 00 00    lea    rdx,[rax*8+0x0]
11f7:       00 
11f8:       48 8d 05 41 2e 00 00    lea    rax,[rip+0x2e41]
11ff:       f2 0f 11 04 02          movsd  QWORD PTR [rdx+rax*1],xmm0
1204:       83 45 e4 01             add    DWORD PTR [rbp-0x1c],0x1
1208:       81 7d e4 3f 42 0f 00    cmp    DWORD PTR [rbp-0x1c],0xf423f
120f:       7e b0                   jle    11c1 <compute+0x38>

這是版本 B:

11e8:       8b 45 cc                mov    eax,DWORD PTR [rbp-0x34]
11eb:       48 98                   cdqe   
11ed:       48 8d 14 c5 00 00 00    lea    rdx,[rax*8+0x0]
11f4:       00 
11f5:       48 8d 05 44 2e 00 00    lea    rax,[rip+0x2e44]
11fc:       f2 0f 10 45 d8          movsd  xmm0,QWORD PTR [rbp-0x28]
1201:       f2 0f 11 04 02          movsd  QWORD PTR [rdx+rax*1],xmm0
1206:       f2 0f 10 45 d8          movsd  xmm0,QWORD PTR [rbp-0x28]
120b:       f2 0f 58 45 d0          addsd  xmm0,QWORD PTR [rbp-0x30]
1210:       f2 0f 11 45 d8          movsd  QWORD PTR [rbp-0x28],xmm0
1215:       f2 0f 10 45 d0          movsd  xmm0,QWORD PTR [rbp-0x30]
121a:       f2 0f 58 45 f8          addsd  xmm0,QWORD PTR [rbp-0x8]
121f:       f2 0f 11 45 d0          movsd  QWORD PTR [rbp-0x30],xmm0
1224:       83 45 cc 01             add    DWORD PTR [rbp-0x34],0x1
1228:       81 7d cc 3f 42 0f 00    cmp    DWORD PTR [rbp-0x34],0xf423f
122f:       7e b7                   jle    11e8 <compute+0x5f>

寫到這邊差不多了,作者拿的這個範例算是很有趣的例子,尤其是現代 compiler 幫我們做了超多事情後,很多自己以為的 optimization 其實未必比較好,還是要有個 profiling review 才準...

Python 3.11 (目前還是 beta) 的效能大幅進步

Hacker News 上看到「Python 3.11 Performance Benchmarks Are Looking Fantastic」這篇,提到目前還在 beta 的 Python 3.11 效能已經比 Python 3.10 有大幅進步了:

Python 3.11 is 10~60% faster than Python 3.10 according to the official figures and a 1.22x speed-up with their standard benchmark suite.

HN 上對應的討論在「Python 3.11 Performance Benchmarks Are Looking Fantastic (phoronix.com)」。

從比較簡單的 PyBench 到 Python 官方的 pyperformance 都有大幅進步。

像是 PyBench:

然後 pyperformance 的部份挑個我自己用到比較多的,Django 相關的東西:

整體分數跑幾何平均的話會是:

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.

在官方文件上「Faster CPython」這邊有提到做了哪些事情,可以看到大家分頭去改善超多東西,累積起來就很驚人...