From da93727472bb7ba381e4b0bbeb0b0e9712bd0518 Mon Sep 17 00:00:00 2001 From: JT Olio Date: Wed, 26 Jun 2024 14:54:43 -0400 Subject: [PATCH 1/5] npm audit fix --- package-lock.json | 575 +++++++++++++++++++++++++++++++--------------- 1 file changed, 391 insertions(+), 184 deletions(-) diff --git a/package-lock.json b/package-lock.json index e54b0f40d..11e22f9c7 100644 --- a/package-lock.json +++ b/package-lock.json @@ -438,9 +438,9 @@ } }, "node_modules/@next/env": { - "version": "14.0.2", - "resolved": "https://registry.npmjs.org/@next/env/-/env-14.0.2.tgz", - "integrity": "sha512-HAW1sljizEaduEOes/m84oUqeIDAUYBR1CDwu2tobNlNDFP3cSm9d6QsOsGeNlIppU1p/p1+bWbYCbvwjFiceA==" + "version": "14.2.4", + "resolved": "https://registry.npmjs.org/@next/env/-/env-14.2.4.tgz", + "integrity": "sha512-3EtkY5VDkuV2+lNmKlbkibIJxcO4oIHEhBWne6PaAp+76J9KoSsGvNikp6ivzAT8dhhBMYrm6op2pS1ApG0Hzg==" }, "node_modules/@next/eslint-plugin-next": { "version": "14.0.2", @@ -452,9 +452,9 @@ } }, 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bytes .../page.md | 80 ++ .../c1245dac8cff160d.jpeg | Bin 0 -> 173355 bytes .../finding-goroutine-leaks-in-tests/page.md | 528 ++++---- .../1597845bc69c4f76.png | Bin 0 -> 247596 bytes .../441ae738c61703c8.png | Bin 0 -> 113072 bytes .../71499072b0db295f.jpeg | Bin 0 -> 1044219 bytes .../79fa81cab0393039.png | Bin 0 -> 179971 bytes .../page.md | 44 + .../2aaa5a3adcf49612.jpeg | Bin 0 -> 239579 bytes .../page.md | 76 ++ .../26c69fe77df6e712.png | Bin 0 -> 54543 bytes .../e2c929baac38fe20.png | Bin 0 -> 29296 bytes .../page.md | 148 +++ app/(blog)/blog/lensm/0254dd9cb3223a7d.png | Bin 0 -> 467655 bytes app/(blog)/blog/lensm/0b52083cb5116034.png | Bin 0 -> 26224 bytes app/(blog)/blog/lensm/32e9beff555091f9.png | Bin 0 -> 240926 bytes app/(blog)/blog/lensm/41b000780c0ec8d1.png | Bin 0 -> 478082 bytes app/(blog)/blog/lensm/5513fe38f6ea01e0.png | Bin 0 -> 122132 bytes app/(blog)/blog/lensm/5c968a28c01d3c08.png | Bin 0 -> 625212 bytes app/(blog)/blog/lensm/6246319ebda3c85f.png | Bin 0 -> 323132 bytes app/(blog)/blog/lensm/71bd197bec7fd5d9.png | Bin 0 -> 86353 bytes app/(blog)/blog/lensm/88a2dddc651c9576.png | Bin 0 -> 35337 bytes app/(blog)/blog/lensm/9cfb54d7e51f9d77.png | Bin 0 -> 52101 bytes app/(blog)/blog/lensm/a4cb43cd5163de6e.png | Bin 0 -> 991919 bytes app/(blog)/blog/lensm/b50757548a5c14fe.png | Bin 0 -> 51072 bytes app/(blog)/blog/lensm/c59c0fe3df775270.png | Bin 0 -> 1007687 bytes app/(blog)/blog/lensm/d20ef641424803bf.png | Bin 0 -> 597342 bytes app/(blog)/blog/lensm/db316e6fe6d98305.png | Bin 0 -> 307473 bytes app/(blog)/blog/lensm/e1ce73a8dfceabfd.jpeg | Bin 0 -> 134617 bytes app/(blog)/blog/lensm/page.md | 116 ++ .../20487c1035ee937f.jpeg | Bin 0 -> 41052 bytes .../page.md | 84 ++ .../14f72bac7573e10c.png | Bin 0 -> 1613131 bytes .../page.md | 151 +++ .../df0a3a1834547b61.jpeg | Bin 0 -> 205678 bytes .../blog/production-concurrency/page.md | 1090 +++++++++++++++++ .../10fb6f750e522e1c.png | Bin 0 -> 143477 bytes .../page.md | 67 + .../69c628262ae22ee5.png | Bin 0 -> 143338 bytes .../75072fcbffc78ed1.png | Bin 0 -> 293888 bytes .../page.md | 56 + .../06a4d2d7af413c6a.png | Bin 0 -> 24913 bytes .../344a9e476aa2b1ea.png | Bin 0 -> 133161 bytes .../4b79f2e770056a31.png | Bin 0 -> 26601 bytes .../6de1c07a519802ed.png | Bin 0 -> 6270 bytes .../943dcd25f919a98b.png | Bin 0 -> 30061 bytes .../c911680feffbe653.png | Bin 0 -> 55376 bytes .../e76c6da3bc2247e7.png | Bin 0 -> 182312 bytes .../page.md | 125 ++ .../1da95eeb5ef706b6.png | Bin 0 -> 53456 bytes .../334785fee7e124be.png | Bin 0 -> 41990 bytes .../87361ca4a0a843f4.png | Bin 0 -> 31280 bytes .../9ccdc75d22b6993e.png | Bin 0 -> 75805 bytes .../9fae43e73a66cba0.png | Bin 0 -> 235959 bytes .../page.md | 164 +++ .../29e62398803ba277.png | Bin 0 -> 252670 bytes .../47f263c59b58c3b7.jpeg | Bin 0 -> 22452 bytes .../6371747a59800613.jpeg | Bin 0 -> 87782 bytes .../page.md | 50 + utils/import-blogs.py | 221 ++++ 87 files changed, 3525 insertions(+), 316 deletions(-) create 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B-);Z^ literal 0 HcmV?d00001 diff --git a/app/(blog)/blog/automatically-store-your-tesla-sentry-mode-and-dashcam-videos-on-the-decentralized-cloud/page.md b/app/(blog)/blog/automatically-store-your-tesla-sentry-mode-and-dashcam-videos-on-the-decentralized-cloud/page.md new file mode 100644 index 000000000..4fb347f57 --- /dev/null +++ b/app/(blog)/blog/automatically-store-your-tesla-sentry-mode-and-dashcam-videos-on-the-decentralized-cloud/page.md @@ -0,0 +1,100 @@ +--- +author: + name: Krista Spriggs +date: '2021-06-15 00:00:00' +heroimage: ./94ca9aa0a874a87b.png +layout: blog +metadata: + description: You can automatically transfer Sentry Mode and Dashcam video clips + over WiFi to cloud storage and make room for more videos the next day. We used + a Raspberry Pi (a small, low cost, low power computer about the size of an Altoids + tin) plugged into the USB port in the dashboard to store the video files. When + the Tesla pulls into the garage at night, the Raspberry Pi connects via WiFi and + uploads all the videos to Storj DCS cloud storage, then clears off the drive for + use the next day. This will also work for videos recorded in Track Mode if you + have one of the performance models, making it easy to share any of the videos + with your friends. + title: Automatically Store Your Tesla Sentry Mode and Dashcam Videos on the Decentralized + Cloud +title: Automatically Store Your Tesla Sentry Mode and Dashcam Videos on the Decentralized + Cloud + +--- + +I have a 2019 Tesla M3 and I love the built in features to capture Dashcam and Sentry Mode footage for review later. The Sentry Mode feature captures 10 minutes of video when someone or something approaches your parked vehicle. Dashcam captures video when you're driving. It continuously stores the most recent hour of video and will also save the most recent 10 minutes of video when you press the Dashcam button or when you honk your horn. Where do those videos get saved? As it turns out, they get saved to a flash drive, if you have one plugged into one of the USB ports in the front console. + + +When you get home, you have to pull the flash drive out of the car and copy the video files to your computer to watch them or store them long term. It’s a fairly low-tech way to manage your data. If you don’t free up space on the flash drive, Sentry Mode will eventually fill up the storage device and your Dashcam feature will stop saving video. What if there was an easy way to save all your Sentry Mode and Dashcam videos automatically when you pull into your garage, and ensure you always have space for new videos? As it turns out, [this is a solved problem](https://github.com/marcone/teslausb)\* if you just want to copy the videos to a computer on your home network. We’ve taken this open source project maintained by GitHub user marcone and created by Reddit user drfrank, and connected it to store the videos on Storj DCS, a decentralized cloud storage service that is secure, private, and extremely affordable.  + +### TL;DR + +You can automatically transfer Sentry Mode and Dashcam video clips over WiFi to cloud storage and make room for more videos the next day. We used a Raspberry Pi (a small, low cost, low power computer about the size of an Altoids tin) plugged into the USB port in the dashboard to store the video files. When the Tesla pulls into the garage at night, the Raspberry Pi connects via WiFi and uploads all the videos to Storj DCS cloud storage, then clears off the drive for use the next day. This will also work for videos recorded in Track Mode if you have one of the performance models, making it easy to share any of the videos with your friends. + + +### So, how hard is it? + +Ok, so most Tesla owners tend to be pretty technical, so, if that describes you, this is a piece of cake, sorry, Pi. Here’s what you’ll need: + +* [Raspberry Pi Zero W : ID 3400 : $10.00](https://www.adafruit.com/product/3400) - we used a different model, but this is better. +* [Adafruit Raspberry Pi Zero Case : ID 3252 : $4.75](https://www.adafruit.com/product/3252) - it should look good - you can 3d print your own for extra credit. +* Y [Video microSDXC Card](https://www.amazon.com/SanDisk-Endurance-microSDXC-Adapter-Monitoring/dp/B07P4HBRMV) $37 - it’s very important to have high quality storage with high write endurance. This gives you room for a few days in case you don’t connect to WiFi and won't wear out too quickly. +* [USB A to Micro-B - 3 foot long](https://www.adafruit.com/product/592) - A USB Cable to plug into the car. [‍](https://www.storj.io/) +* [Storj DCS cloud storage](https://www.storj.io/) - Storj provides 25 GB for free and it’s only $0.004 per GB after that! Secure, private, and decentralized. +* Optional Items for easier setup - A [Mini HDMI to HDMI Cable - 5 feet : ID 2775 : $5.95](https://www.adafruit.com/product/2775) will make it easier to set everything up by connecting the Pi to a monitor. + +All in, you’re looking at right around $60 to get going. I’ll wait while you get everything ordered... + + +### Okay, what’s next? + +Assuming you have everything you need, we’ve published the [detailed, step-by-step instructions in this tutorial](https://docs.storj.io/dcs/how-tos/tesla-sentry-mode-teslausb). In general, there are four main steps which we share in a brief overview below: + + +* Create your Storj DCS account +* Set up your Raspberry Pi +* Enable Sentry Mode and Dashcam Mode +* Drive around and honk your horn + +After that, you can just go home and park your car. Your Raspberry Pi will connect to your home WiFi and do the rest. You’ll be able to view and share your videos from Storj DCS. + +### Create your Storj DCS account + +Storj DCS is a decentralized cloud object storage service. Storj DCS is like the Airbnb for hard drives—thousands of people with extra hard drive space share that space with Storj, then Storj makes that space available to use for storing things like Tesla videos. Every file stored on Storj DCS is encrypted, encoded for redundancy, and divided into at least 80 pieces— each stored on a different hard drive all over the world, provided by people who share storage space and bandwidth in exchange for STORJ tokens. Of those 80 pieces, you only need any 29 to download your file. That’s a bit of an oversimplification, and it’s even better than it sounds, but you get the idea; cloud storage for your videos. We just released some new features and pretty amazing pricing on April 20 and now you can try it. + + +First, go to Storj.io and create an account. You get 25 GB of storage and bandwidth a month for free with your new account. After that, you’ll need to create an Access Grant and generate the S3-compatible gateway credentials for use in the application that uploads your data to Storj DCS. Follow the steps in the tutorial and save your gateway credentials for the next step. + + +### Set up your Raspberry Pi + +A Raspberry Pi is a mini computer. Setting one up is relatively straightforward. Once you assemble the parts, you can connect to it remotely or plug it in to a monitor keyboard and mouse. To get it up and running, you download an OS like Raspian, flash it to the SD card and boot it up. From there, it’s a matter of installing a few components, including Rclone, which is the application that will upload your videos to Storj DCS. Configure Rclone with the gateway credentials you created on your Storj DCS account. Once you have the Raspberry Pi working, shut it down, unplug everything and head out to your Tesla with the Raspberry Pi and the USB cable. + + +### Enable Sentry Mode and Dashcam Mode + +Once you’re in your car, plug the Raspberry Pi into one of the USB ports in the front console. (The ones in the back don’t work for this project.) The Raspberry Pi will store video files through and is also powered by the USB port, so it needs to stay plugged in. Now, you’ll need to enable Sentry Mode and Dashcam Mode. These features are not enabled by default. Follow the steps in the tutorial to enable those two features on your Tesla. Once the Raspberry Pi is plugged in and the features are enabled, you’re ready to see it in action. + + +### Drive around and honk your horn + +The easiest way to capture some video clips is to drive around and honk your horn. Of course, if you worked on this until late into the night, your neighbors may or may not be as excited to test it as you are, so honk responsibly. As an alternative, drive around and click the Dashcam button to save a clip. Really, it’s up to you, but just get some video footage. All of the videos generated by Sentry Mode and Dashcam Mode will be saved to the SD card in the Raspberry Pi. + + +Once you’ve got some video, it’s time for the real magic—go home. When you pull into your garage and your Raspberry Pi connects to your home WiFi, it will upload the trip’s videos to Storj DCS.  + + +As you drive around, honk your horn, capture Dashcam videos and accumulate Sentry Mode video. Upon return, the videos will be uploaded to your Storj DCS account. Every one of those videos will be encrypted, erasure coded and stored in pieces distributed across our network of 13,000 Storage Nodes (and growing). You can view, download or share those videos with your friends. We’ve shared a sample video from a Tesla belonging to a Storj team member. When you share a file though Storj DCS,  the link lets you see all the Storage Nodes storing pieces of your file and stream the file directly from the network. The tutorial also has the steps to share a file (hint: click the share button to create a secure and private link to share). + + +### That’s all there is to it + +If you’ve followed along and followed the steps in your tutorial, your Tesla will store your Sentry and Dashcam videos in the decentralized cloud for as long as you want or need them.  + + +Overall this was a really fun project to put together, and shows off yet another way that you can integrate with Storj DCS easily and quickly! + + + +*\* The code used in this tutorial is open source and uses, among other things,* [*RClone*](https://github.com/rclone/rclone) *which includes native support for Storj DCS. The GitHub Repository for the code is available at:* [*https://github.com/marcone/teslausb*](https://github.com/marcone/teslausb) *and the project was originally described on the* [*/r/teslamotors*](https://www.reddit.com/r/teslamotors/comments/9m9gyk/build_a_smart_usb_drive_for_your_tesla_dash_cam/) *subreddit.* + + diff --git a/app/(blog)/blog/demystifying-technical-debt/09f5df2707586cc9.png b/app/(blog)/blog/demystifying-technical-debt/09f5df2707586cc9.png new file mode 100644 index 0000000000000000000000000000000000000000..84349cdfc8480406b6ed4b13c7c1bf9978ddba93 GIT binary patch literal 64095 zcmce-^;cC-^f!Jk-7Vd%NC?s`NQa1ogmg)RbX`D7xy_h-Ii|6&a-+*%+#}he+DgA7k={YEeMyz(=zkm4c8QCfod_aAA+R^#x z$D@$m5Vq9FAN?5}yGjDtBod@&?_H#qZ8g>3T=cK1gh;CV&Rr(Z)&Dr3Zj^b^t)xAO zJvex{>dLaVA+fHUS=6B-YntlGCSuZq@y9ZV@CD9jZyX%?0%Mee?`Ko9S=f!-mN%MzbGh~S=+!en?9tnJy zJHA!O`@1n(oYqgR?y$)ln7cUuQ7bF;+t0W;={|nHFY?1pq^-cEzf}ziAr>b%4JfWJ6yIQgk1vLKH~`QCin7vgJTrIZ 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+metadata: + description: It was the best of times, it was the worst of times. That's when I + hit a performance mystery that sent me down a multi-day rabbit hole of adventure. + I was writing some code to take some entries, append them into a fixed size in-memory + buffer, and then flush that buffer to disk when it was full. + title: A Tale of Two Copies +title: A Tale of Two Copies + +--- + +It was the best of times, it was the worst of times. That's when I hit a performance mystery that sent me down a multi-day rabbit hole of adventure. I was writing some code to take some entries, append them into a fixed size in-memory buffer, and then flush that buffer to disk when it was full. The main bit of code looked a little something like this: + + +``` +type Buffer struct { + fh *os.File + n uint + buf [numEntries]Entry +} + +func (b *Buffer) Append(ent Entry) error { + if b.n < numEntries-1 { + b.buf[b.n] = ent + b.n++ + return nil + } + return b.appendSlow(ent) +} + +``` + + +with the idea being that when there's space in the buffer, we just insert the entry and increment a counter, and when we're full, it falls back to the slower path that writes to disk. Easy, right? Easy... + +## The Benchmark + +I had a question about what size the entries should be. The minimum size I could pack them into was 28 bytes, but that's not a nice power of 2 for alignment and stuff, so I wanted to compare it to 32 bytes. Rather than just relying on my intuition, I decided to write a benchmark. The benchmark would Append a fixed number of entries per iteration (100,000) and the only thing changing would be if the entry size was 28 or 32 bytes. + +Even if I'm not relying on my intuition, I find it fun and useful to try to predict what will happen anyway. And so, I thought to myself: + + +> Everyone knows that I/O is usually dominating over small CPU potential inefficiencies. The 28 byte version writes less data and does less flushes to disk than the 32 byte version. Even if it's somehow slower filling the memory buffer, which I doubt, that will be more than made up for by the extra writes that happen. + +Maybe you thought something similar, or maybe something completely different. Or maybe you didn't sign up to do thinking right now and just want me to get on with it. And so, I ran the following benchmark: + + +``` +func BenchmarkBuffer(b *testing.B) { + fh := tempFile(b) + defer fh.Close() + + buf := &Buffer{fh: fh} + now := time.Now() + ent := Entry{} + + b.ResetTimer() + + for i := 0; i < b.N; i++ { + fh.Seek(0, io.SeekStart) + + for i := 0; i < 1e5; i++ { + _ = buf.Append(ent) + } + _ = buf.Flush() + } + + b.ReportMetric(float64(time.Since(now).Nanoseconds())/float64(b.N)/1e5, "ns/key") + b.ReportMetric(float64(buf.flushes)/float64(b.N), "flushes") +} + +``` + + +## Confusion + +And here are the results: + + +``` +BenchmarkBuffer/28 734286 ns/op 171.0 flushes 7.343 ns/key +BenchmarkBuffer/32 436220 ns/op 196.0 flushes 4.362 ns/key + +``` + + + +That's right, a nearly 2x difference in performance where the benchmark writing to disk MORE is FASTER! + +![](./51fb41f548601419.png) + +*Me too, Nick Young. Me, too.* + +And so began my journey. The following is my best effort in remembering the long, strange trip I took diagnosing what I thought was happening. Spoiler alert: I was wrong a lot, and for a long time. + +# The Journey + +## CPU Profiles + +CPU profiles have a huge power to weight ratio. To collect them from a Go benchmark, all you have to do is specify `-cpuprofile=` + on the command line and that's it. So of course this is the first thing I reached for. + +One thing to keep in mind, though, is that Go benchmarks by default will try to run for a fixed amount of time, and if one benchmark takes longer to do its job vs another, you get less iterations of it. Since I wanted to compare the results more directly, I made sure to also pass a fixed number of iterations to the command with `-benchtime=2000x` +. + +So let's take a look at these profiles. First, the 32 byte version: + + +``` + . . 24:func (b *Buffer) Append(ent Entry) error { + 30ms 30ms 25: if b.n < numEntries-1 { + 110ms 110ms 26: b.buf[b.n] = ent + 90ms 90ms 27: b.n++ + . . 28: return nil + . . 29: } + 10ms 520ms 30: return b.appendSlow(ent) + . . 31:} + +``` + + +The first column shows the amount of time spent on that line just in the context of the shown function, and the second column is the amount of time spent on that line including any functions it may have called. + +From that, we can see that, as expected, most of the time is spent flushing to disk in appendSlow compared to writing to the in memory buffer. + +And now here's the 28 byte version: + + +``` + . . 24:func (b *Buffer) Append(ent Entry) error { + 20ms 20ms 25: if b.n < numEntries-1 { + 840ms 840ms 26: b.buf[b.n] = ent + 20ms 20ms 27: b.n++ + . . 28: return nil + . . 29: } + . 470ms 30: return b.appendSlow(ent) + . . 31:} + +``` + + + +A couple of things stand out to me here. First of all, WHAT? Second of all, it spends less time flushing to disk compared to the 32 byte version. That's at least expected because it does that less often (171 vs 196 times). And finally, WHAT? + +Maybe the penalty for writing unaligned memory was worse than I thought. Let's take a look at the assembly to see what instruction it's stuck on. + +## The Assembly + +Here's the section of code responsible for the 840ms on line 26 in the above profile: + + +``` + . . 515129: IMULQ $0x1c, CX, CX (1) + 90ms 90ms 51512d: LEAQ 0xc0(SP)(CX*1), CX (2) + . . 515135: MOVUPS 0x7c(SP), X0 (3) + 670ms 670ms 51513a: MOVUPS X0, 0(CX) (4) + 80ms 80ms 51513d: MOVUPS 0x88(SP), X0 (5) + . . 515145: MOVUPS X0, 0xc(CX) (6) + +``` + + +If you've never read assembly before, this may be a bit daunting, so I've numbered the lines and will provide a brief explanation. The most important bits to know are that `CX` +, `SP` + and `X0` + are registers, and the syntax `0x18(CX)` + means the value at address `CX + 0x18` +. Armed with that knowledge, we can understand the lines: + +1. Multiply the `CX` + register by `0x1c` + and store it into `CX` +. `0x1c` + is the hex encoding of the decimal value 28. +2. This computes the address we'll be storing the entry into. It computes `0xc0 + SP + (CX*1)` + and stores it into `CX` +. From this, we deduce that the start of the entry array is at `0xc0(SP)` +. +3. This loads 16 bytes starting at `0x7c(SP)` + and stores it into `X0` +. +4. This stores the 16 bytes we just loaded into `0(CX)` +. +5. This loads 16 bytes starting at `0x88(SP)` + and stores it into `X0` +. +6. This stores the 16 bytes we just loaded into `0xc(CX)` +. + +I don't know about you, but I saw no reason why line 4 should have so much weight compared to the other lines. So, I compared it to the 32 byte version to see if the generated code was different: + + +``` + 40ms 40ms 515129: SHLQ $0x5, CX + 10ms 10ms 51512d: LEAQ 0xc8(SP)(CX*1), CX + . . 515135: MOVUPS 0x80(SP), X0 + 10ms 10ms 51513d: MOVUPS X0, 0(CX) + 40ms 40ms 515140: MOVUPS 0x90(SP), X0 + 10ms 10ms 515148: MOVUPS X0, 0x10(CX) + +``` + + + +It looks like the only difference, aside from almost no time at all being spent in these instructions, is the SHLQ vs the IMULQ. The former is doing a "left shift" of 5, which effectivly multiplies by 2 to the 5th power, or 32, and the latter, as we previously saw, multiplies by 28. Could this possibly be the performance difference? + +## Pipelines and Ports + +Modern CPUs are complex beasts. Maybe you have the mental model that your CPU reads instructions in and executes them one at a time as I once did. That couldn't be further from the truth. Instead, they execute multiple instructions at once, possibly out of order, in a [pipeline](https://en.wikipedia.org/wiki/Instruction_pipelining). But it gets even better: they have limits on how many of each kind of instruction can be run simultaneously. This is done by the CPU having multiple "ports", and certain instructions require and can run on different subsets of these ports. + +So what does that have to do with IMULQ vs SHLQ? Well, you may have noticed that the LEAQ following the IMULQ/SHLQ has a multiply in it (`CX*1` +). But, because there aren't infinite ports, there must be a limited number of ports able to do multiplies. + +The LLVM project has lots of tools to help you understand what computers do, and one of them is a tool called [`llvm-mca`](https://www.llvm.org/docs/CommandGuide/llvm-mca.html#how-llvm-mca-works). Indeed, if we run the two first instructions of the 32 and 28 byte versions through `llvm-mca` +, it gives us an idea of what ports will be used when they are executed: + + +``` +Resource pressure by instruction (32 byte version): +[2] [3] [7] [8] Instructions: +0.50 - - 0.50 shlq $5, %rcx + - 0.50 0.50 - leaq 200(%rsp,%rcx), %rcx + +Resource pressure by instruction (28 byte version): +[2] [3] [7] [8] Instructions: + - 1.00 - - imulq $28, %rcx, %rcx + - - 1.00 - leaq 192(%rsp,%rcx), %rcx + +``` + + + +The numbers are what percent of the time each instruction ran on the port (here, numbered 2, 3, 7 and 8) when executed in a loop. + +So that's saying that in the 32 byte version, the SHLQ ran on port 2 half the time and port 8 the other half, and the LEAQ ran on port 3 half the time and port 7 the other half. This is implying that it can have 2 parallel executions at once. For example, on one iteration, it can use ports 2 and 3, and on the next iteration it can use ports 7 and 8, even if ports 2 and 3 are still being used. However, for the 28 byte version, the IMULQ must happen solely on port 3 due to the way the processor is built, which in turn limits the maximum throughput. + +And for a while, this is what I thought was happening. In fact, a first draft of this very blog post had that as the conclusion, but the more I thought about it, the less good of an explanation it seemed. + +![](./eb4b808e55daa547.png) + +*My first draft attempt* + +## Trouble in Paradise + +Here are some thoughts that you may be having: + +1. In the worst case, that can only be a 2x speed difference. +2. Aren't there other instructions in the loop? That has to make it so that it's much less than 2x in practice. +3. The 32 byte version spends 230ms in the memory section and the 28 byte version spends 880ms. +4. That is much bigger than 2x bigger. +5. Oh no. + +Well, maybe that last one was just me. With those doubts firmly in my mind, I tried to figure out how I could test to see if it was because of the IMULQ and SHLQ. Enter `perf` +. + +## Perf + +[`perf`](https://perf.wiki.kernel.org/index.php/Main_Page) is a tool that runs on linux that allows you to execute programs and expose some detailed counters that CPUs keep about how they executed instructions (and more!). Now, I had no idea if there was a counter that would let me see something like "the pipeline stalled because insufficient ports or whatever", but I did know that it had counters for like, everything. + +If this were a movie, this would be the part where the main character is shown trudging through a barren desert, sun blazing, heat rising from the earth, with no end in sight. They'd see a mirage oasis and jump in, gulping down water, and suddenly realize it was sand. + +A quick estimate shows that perf knows how to read over 700 different counters on my machine, and I feel like I looked at most of them. Take a look at [this huge table](https://perfmon-events.intel.com/skylake.html) if you're interested. I couldn't find any counters that could seem to explain the large difference in speed, and I was starting to get desparate. + +![](./9b285294e5712e47.jpeg) + +*A picture of me wading through all of the perf counters* + +## Binary Editing for Fun and Profit + +At this point, I had no idea what the problem was, but it sure seemed like it wasn't port contention like I thought. One of the only other things that I thought it could be was alignment. CPUs tend to like to have memory accessed at nice multiples of powers of 2, and 28 is not one of those, and so I wanted to change the benchmark to write 28 byte entries but at 32 byte offsets. + +Unfortunately, this wasn't as easy as I hoped. The code under test is very delicately balanced with respect to the Go compiler's inliner. Basically any changes to Append cause it to go over the threshold and stop it from being inlined, which really changes what's being executed. + +Enter binary patching. It turns out that in our case, the IMULQ instruction encodes to the same number of bytes as the SHLQ. Indeed, the IMULQ encodes as `486bc91c` +, and the SLHQ as `48c1e105` +. So it's just a simple matter of replacing those bytes and running the benchmark. I'll (for once) spare you the details of how I edited it (Ok, I lied: I hackily used `dd` +). The results sure did surprise me: + + +``` +BenchmarkBuffer/28@32 813529 ns/op 171.0 flushes 8.135 ns/key + +``` + + + +I saw the results and felt defeated. It wasn't the IMULQ making the benchmark go slow. That benchmark has no IMULQ in it. It wasn't due to unaligned writes. The slowest instruction was written with the same alignment as in the 32 byte version as we can see from the profiled assembly: + + +``` + . . 515129: SHLQ $0x5, CX + 60ms 60ms 51512d: LEAQ 0xc0(SP)(CX*1), CX + . . 515135: MOVUPS 0x7c(SP), X0 + 850ms 850ms 51513a: MOVUPS X0, 0(CX) + 120ms 120ms 51513d: MOVUPS 0x88(SP), X0 + . . 515145: MOVUPS X0, 0xc(CX) + +``` + + +What was left to try? + +# A Small Change + +Sometimes when I have no idea why something is slow, I try writing the same code but in a different way. That may tickle the compiler just right to cause it to change which optimizations it can or can't apply, giving some clues as to what's going on. So in that spirit I changed the benchmark to this: + + +``` +func BenchmarkBuffer(b *testing.B) { + // ... setup code + + for i := 0; i < b.N; i++ { + fh.Seek(0, io.SeekStart) + + for i := 0; i < 1e5; i++ { + _ = buf.Append(Entry{}) + } + _ = buf.Flush() + } + + // .. teardown code +} + +``` + + + +It's hard to spot the difference, but it changed to passing a new entry value every time instead of passing the `ent` + variable manually hoisted out of the loop. I ran the benchmarks again. + + +``` +BenchmarkBuffer/28 407500 ns/op 171.0 flushes 4.075 ns/key +BenchmarkBuffer/32 446158 ns/op 196.0 flushes 4.462 ns/key + +``` + + + +IT DID SOMETHING? How could that change possibly cause that performance difference? It's finally running faster than the 32 byte version! As usual, time to look at the assembly. + + +``` + 50ms 50ms 515109: IMULQ $0x1c, CX, CX + . . 51510d: LEAQ 0xa8(SP)(CX*1), CX + . . 515115: MOVUPS X0, 0(CX) + 130ms 130ms 515118: MOVUPS X0, 0xc(CX) + +``` + + + +It's no longer loading the value from the stack to store it into the array, and instead just storing directly into the array from the already zeroed register. But we know from all the pipeline analysis done earlier that the extra loads should effectively be free, and the 32 byte version confirms that. It didn't get any faster even though it also is no longer loading from the stack. + +So what's going on? + +## Overlapping Writes + +In order to explain this idea, it's important to show the assembly of the full inner loop instead of just the code that writes the entry to the in-memory buffer. Here's a cleaned up and annotated version of the slow 28 byte benchmark inner loop: + + +``` +loop: + INCQ AX (1) + CMPQ $0x186a0, AX + JGE exit + + MOVUPS 0x60(SP), X0 (2) + MOVUPS X0, 0x7c(SP) + MOVUPS 0x6c(SP), X0 + MOVUPS X0, 0x88(SP) + + MOVQ 0xb8(SP), CX (3) + CMPQ $0x248, CX + JAE slow + + IMULQ $0x1c, CX, CX (4) + LEAQ 0xc0(SP)(CX*1), CX + MOVUPS 0x7c(SP), X0 (5) + MOVUPS X0, 0(CX) + MOVUPS 0x88(SP), X0 + MOVUPS X0, 0xc(CX) + + INCQ 0xb8(SP) (6) + JMP loop + +slow: + // ... slow path goes here ... + +exit: + +``` + + +1. Increment `AX` + and compare it to 100,000 exiting if it's larger. +2. Copy 28 bytes on the stack from offsets `[0x60, 0x7c]` + to offsets `[0x7c, 0x98]` +. +3. Load the memory counter and see if we have room in the memory buffer +4. Compute where the entry will be written to in the in-memory buffer. +5. Copy 28 bytes on the stack at offsets `[0x7c, 0x98]` + into the in-memory buffer. +6. Increment the memory counter and loop again. + +Steps 4 and 5 are what we've been looking at up to now. + +If step 2 seems silly and redundant, that's because it is. There's no reason to copy a value on the stack to another location on the stack and then load from that copy on the stack into the in-memory buffer. Step 5 could have just used offsets `[0x60, 0x7c]` + instead and step 2 could have been eliminated. The Go compiler could be doing a better job here. + +But that shouldn't be why it's slow, right? The 32 byte code does almost the exact same silly thing and it goes fast, because of pipelines or pixie dust or something. What gives? + +There's one crucial difference: the writes in the 28 byte case overlap. The MOVUPS instruction writes 16 bytes at a time, and as everyone knows, 16 + 16 is usually more than 28. So step 2 writes to bytes `[0x7c, 0x8c]` + and then writes to bytes `[0x88, 0x98]` +. This means the range `[0x88, 0x8c]` + was written to twice. Here's a helpful ASCII diagram: + + +``` +0x7c 0x8c +├────────────────┤ +│ Write 1 (16b) │ +└───────────┬────┴──────────┐ + │ Write 2 (16b) │ + ├───────────────┤ + 0x88 0x98 + +``` + + +## Store Forwarding + +Remember how CPUs are complex beasts? Well it gets even better. An optimization that some CPUs do is they have something called a ["write buffer"](https://en.wikipedia.org/wiki/Write_buffer). You see, memory access is often the slowest part of what CPUs do. Instead of, you know, actually writing the memory when the instruction executes, CPUs place the writes into a buffer first. I think the idea is to coalesce a bunch of small writes into larger sizes before flushing out to the slower memory subsystem. Sound familiar? + +So now it has this write buffer buffering all of the writes. What happens if a read comes in for one of those writes? It would slow everything down if had to wait for that write to actually happen before reading it back out, so instead it tries to service the read from the write buffer directly if possible, and no one is the wiser. You clever little CPU. This optimization is called [store forwarding](https://easyperf.net/blog/2018/03/09/Store-forwarding). + +![](./07035c56befc8dc1.png) + +*My CPU buffering and reorganizing all of the writes* + +But what if those writes overlap? It turns out that, on my CPU at least, this inhibits that "store forwarding" optimization. There's even a perf counter that keeps track of when this happens: [ld\_blocks.store\_forward](https://perfmon-events.intel.com/index.html?pltfrm=skylake.html&evnt=LD_BLOCKS.STORE_FORWARD). + +Indeed, the documentation about that counter says + + +> Counts the number of times where store forwarding was prevented for a load operation. The most common case is a load blocked due to the address of memory access (partially) overlapping with a preceding uncompleted store. + +Here's how often that counter hits for the different benchmarks so far where "Slow" means that the entry is constructed outside of the loop, and "Fast" means that the entry is constructed inside of the loop on every iteration: + + +``` +BenchmarkBuffer/28-Slow 7.292 ns/key 1,006,025,599 ld_blocks.store_forward +BenchmarkBuffer/32-Slow 4.394 ns/key 1,973,930 ld_blocks.store_forward +BenchmarkBuffer/28-Fast 4.078 ns/key 4,433,624 ld_blocks.store_forward +BenchmarkBuffer/32-Fast 4.369 ns/key 1,974,915 ld_blocks.store_forward + +``` + + +Well, a billion is usually bigger than a million. Break out the champagne. + +# Conclusion + +After all of that, I have a couple of thoughts. + +Benchmarking is hard. People often say this, but maybe the only thing harder than benchmarking is adequately conveying how hard benchmarking is. Like, this was closer to the micro-benchmark than macro-benchmark side of things but still included performing millions of operations including disk flushes and actually measured a real effect. But at the same time, this would almost never be a problem in practice. It required the compiler to spill a constant value to the stack unnecessarily very closely to the subsequent read in a tight inner loop to notice. Doing any amount of real work to create the entries would cause this effect to vanish. + +A recurring theme as I learn more about how CPUs work is that the closer you get to the "core" of what it does, the leakier and more full of edge cases and hazards it becomes. Store forwarding not working if there was a partially overlapping write is one example. Another is that the caches aren't [fully associative](https://en.wikipedia.org/wiki/CPU_cache#Associativity), so you can only have so many things cached based on their memory address. Like, even if you have 1000 slots available, if all your memory accesses are multiples of some factor, they may not be able to use those slots. [This blog post](https://danluu.com/3c-conflict/) has a great discussion. Totally speculating, but maybe this is because you have less "room" to solve those edge cases when under ever tighter physical constraints. + +Before now, I've never been able to concretely observe the CPU slowing down from port exhaustion issues in an actual non-contrived setting. I still haven't. I've heard the adage that you can imagine every CPU instruction taking 0 cycles except for the ones that touch memory. As a first approximation, it seems pretty true. + +I've put up the full code sample in [a gist](https://gist.github.com/zeebo/4c9e28ac277c74ae450ad1bff8068f93) for your viewing/downloading/running/inspecting pleasure. + +Often, things are more about the journey than the destination, and I think that's true here, too. If you made it this far, thanks for coming along on the journey with me, and I hope you enjoyed it. 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