Nvidia 在 Linux 上安裝核心驅動程式時將建議使用開源版本

在「NVIDIA to install open Linux kernel modules by default」這邊看到的新聞,引用的連結是官方的討論區「Unix graphics feature deprecation schedule」這篇。

從 560 開始會議建議使用開源版本:

Starting in the release 560 series, it will be recommended to use the open flavor of NVIDIA Linux Kernel Modules 204 wherever possible (Turing or later GPUs, or Ada or later when using GPU virtualization).

點進去看「Open Linux Kernel Modules」這頁可以看到開源版本有一些專屬功能 (在「The following features will only work with the open kernel modules flavor of the driver」這段),蛋也有一些功能是開源版本沒有的 (在「The following features are not yet supported by the open kernel modules」這段)。

另外 Known Issues 這邊有提到些效能與功耗上的差異。

看起來是 porting 的差不多了?我覺得可以再等一兩個版本 XD

IntelAMD GPU 直接跑 CUDA 程式的 ZLUDA

先前提過「在 Intel 內顯上面直接跑 CUDA 程式的 ZLUDA」,結果後來事情大翻轉,AMD 跑去贊助專案,變成支援 AMD GPU 了:「AMD Quietly Funded A Drop-In CUDA Implementation Built On ROCm: It's Now Open-Source」,專案在 GitHubvosen/ZLUDA 這邊,而這包支援 AMD GPU 的 commit log 則是在 1b9ba2b2333746c5e2b05a2bf24fa6ec3828dcdf 這包巨大的 commit:

Nobody expects the Red Team

Too many changes to list, but broadly:
* Remove Intel GPU support from the compiler
* Add AMD GPU support to the compiler
* Remove Intel GPU host code
* Add AMD GPU host code
* More device instructions. From 40 to 68
* More host functions. From 48 to 184
* Add proof of concept implementation of OptiX framework
* Add minimal support of cuDNN, cuBLAS, cuSPARSE, cuFFT, NCCL, NVML
* Improve ZLUDA launcher for Windows

其中的轉折以及後續的故事其實還蠻不知道怎麼說的... 作者一開始在 Intel 上班,弄一弄 Intel 覺得這沒前景,然後 AMD 接觸後贊助這個專案,到後面也覺得沒前景,於是依照後來跟 AMD 的合約,如果 AMD 覺得沒前景,可以 open source 出來:

Why is this project suddenly back after 3 years? What happened to Intel GPU support?

In 2021 I was contacted by Intel about the development od ZLUDA. I was an Intel employee at the time. While we were building a case for ZLUDA internally, I was asked for a far-reaching discretion: not to advertise the fact that Intel was evaluating ZLUDA and definitely not to make any commits to the public ZLUDA repo. After some deliberation, Intel decided that there is no business case for running CUDA applications on Intel GPUs.

Shortly thereafter I got in contact with AMD and in early 2022 I have left Intel and signed a ZLUDA development contract with AMD. Once again I was asked for a far-reaching discretion: not to advertise the fact that AMD is evaluating ZLUDA and definitely not to make any commits to the public ZLUDA repo. After two years of development and some deliberation, AMD decided that there is no business case for running CUDA applications on AMD GPUs.

One of the terms of my contract with AMD was that if AMD did not find it fit for further development, I could release it. Which brings us to today.

這個其實還蠻好理解的,CUDA 畢竟是 Nvidia 家的 ecosystem,除非你反超越後自己定義一堆自家專屬的功能 (像是當年 MicrosoftIE 上的玩法),不然只是幫人抬轎。

Phoronix 在 open source 前幾天先拿到軟體進行測試,而他這幾天測試的結果給了「頗不賴」的評價:

Andrzej Janik reached out and provided access to the new ZLUDA implementation for AMD ROCm to allow me to test it out and benchmark it in advance of today's planned public announcement. I've been testing it out for a few days and it's been a positive experience: CUDA-enabled software indeed running atop ROCm and without any changes. Even proprietary renderers and the like working with this "CUDA on Radeon" implementation.

另外為了避免測試時有些測試軟體會回傳到伺服器造成資訊外洩,ZLUDA 在這邊故意設定為 Graphics Device,而在這次 open source 公開後會改回正式的名稱:

In my screenshots and for the past two years of development the exposed device name for Radeon GPUs via CUDA has just been "Graphics Device" rather than the actual AMD Radeon graphics adapter with ROCm. The reason for this has been due to CUDA benchmarks auto-reporting results and other software that may have automated telemetry, to avoid leaking the fact of Radeon GPU use under CUDA, it's been set to the generic "Graphics Device" string. I'm told as part of today's open-sourcing of this ZLUDA on Radeon code that the change will be in place to expose the actual Radeon graphics card string rather than the generic "Graphics Device" concealer.

作者的測試看起來在不同的測試項目下差異頗大,但如果依照作者的計算方式,整體效能跟 OpenCL 版本差不多:

Phoronix 那邊則是做了與 Nvidia 比較的測試... 這邊拿的是同樣都有支援 Nvidia 與 AMD 家的卡的 Blender 測試,然後跑出來的結果讓人傻眼,透過 ZLUDA 轉譯出來的速度比原生支援的速度還快,這 optimization 看起來又有得討論了:(這是 BMW27 的測試,在 Classroom 的測試也發現一樣的情況)

但即使如此,CUDA over AMD GPU 應該還是不會起來,官方會儘量讓各 framework 原生支援,而大多數的開發者都是在 framework 上面開發,很少會自己從頭幹...

Apple M1 上跑 Linux 的 GPU driver 會動了

Hacker News Daily 上看到「Native Linux GPU Driver for Apple M1 (twitter.com/linaasahi)」這個,講 Apple M1 上有 Linux GPU driver 可以用了,原文是 Twitter 上的推:

不過從 YouTube 的影片上可以看到就只是「會動」,還有不少 rendering bug,可以看到有時候會破格,但畢竟是開始支援了,後續如果有修到穩定的話,直接的好處應該就是把瀏覽器的 rendering 丟給 GPU 處理,就像 macOS 或是 Windows 上的情況。

NVIDIA 開源 Linux GPU Kernel Driver

NVIDIA 宣佈開源 Linux 下的 GPU Kernel Driver:「NVIDIA Releases Open-Source GPU Kernel Modules」。

從一些描述上可以看出來,應該是因為 Datacenter 端的動力推動的,所以這次 open source 的版本中,對 Datacenter GPU 的支援是 production level,但對 GeForce GPU 與 Workstation GPU 的支援直接掛 alpha level:

Which GPUs are supported by Open GPU Kernel Modules?

Open kernel modules support all Ampere and Turing GPUs. Datacenter GPUs are supported for production, and support for GeForce and Workstation GPUs is alpha quality. Please refer to the Datacenter, NVIDIA RTX, and GeForce product tables for more details (Turing and above have compute capability of 7.5 or greater).

然後 user-mode driver 還是 closed source:

Will the source for user-mode drivers such as CUDA be published?

These changes are for the kernel modules; while the user-mode components are untouched. So the user-mode will remain closed source and published with pre-built binaries in the driver and the CUDA toolkit.

nouveau 來說,是可以從 open source driver 裡面挖一些東西出來用,不過能挖到跟 proprietary 同樣效能水準嗎?

EC2 的 Monitoring 提供更多關於限流的資訊

AWS 對於 EC2 的網路推出了五個新的監控指標:「Amazon EC2 announces new network performance metrics for EC2 instances」。

看起來都是跟限流有關的指標,看起來是在壓榨機器極限時會用到:

The new metrics inform customers in real time of network traffic impacted when instance allowances for inbound and outbound bandwidth, packets-per-second (PPS), connections tracked and PPS to link-local services are exceeded.

需要有最新的 ENA driver 才會提供 (看了一下現有的機器沒出現這些值 XD):

These metrics are available today in all global commercial AWS regions on instances running the latest version of the Elastic Network Adapter (ENA) driver with support for Linux, Windows ENA driver support will be available soon with version 2.2.2.0.

不另外收費:

They can be accessed from within the instance at no extra cost using simple command line tools.

這個功能在所有 AWS 商業區以及 GovCloud (US) 都已經上線:

Instance Level Network Performance Metrics is available in all AWS Commercial and GovCloud (US) Regions, with the exception of China (Beijing) and China (Ningxia).

先記錄起來就好,一般用法應該都還好...

Linux Kernel 5.7 釋出...

在「The New Features Of The Linux 5.7 Kernel: Tiger Lake Graphics Stable, New exFAT, Zstd F2FS, Performance」這邊有列出重點來。

其中把過熱保護機制也一起考慮進來,這樣可以避免過熱被強制降速而反而變非常慢:

Thermal pressure tracking for systems that are thermally overloaded for better task placement on CPU cores running hot.

另外一個是把 exFAT 驅動換成由 Samsung 維護的版本,照其他文章的說明,這個版本比較穩定...

The new exFAT file-system driver that replaces the exFAT driver in the staging area that had been around for a few releases. This new exFAT driver is in much better shape and actively maintained by Samsung.

主要還是過熱保護那段還蠻值得期待,不然就是要硬上水冷壓,避免遇到溫度牆...

GrabFood 用定位資料修正餐廳的資訊

Grab 的「How we harnessed the wisdom of crowds to improve restaurant location accuracy」這篇是他們的 data team 整理出來,如何使用既有的資料快速的修正餐廳資訊。裡面提到的方法不需要用到 machine learning,光是一些簡單的統計算法就可以快速修正現有的架構。

這些資訊其實是透過司機用的 driver app 蒐集來的,在 driver app 上有大量的資訊傳回伺服器 (像是定時回報的 GPS 位置,以及取餐狀態),而這些司機因為地緣關係,腦袋裡的資訊比地圖會準不少:

One of the biggest advantages we have is the huge driver-partner fleet we have on the ground in cities across Southeast Asia. They know the roads and cities like the back of their hand, and they are resourceful. As a result, they are often able to find the restaurants and complete orders even if the location was registered incorrectly.

所以透過這些資訊他們就可以反過來改善地圖資料,像是透過司機按下「取餐」的按鈕的地點與待的時間,就可以估算餐聽可能的位置,然後拿這個資訊比對地圖上的資料,就很容易發現搬家但是地圖上沒更新的情況:

Fraction of the orders where the pick-up location was not “at” the restaurant: This fraction indicates the number of orders with a pick-up location not near the registered restaurant location (with near being defined both spatially and temporally as above). A higher value indicates a higher likelihood of the restaurant not being in the registered location subject to order volume

Median distance between registered and estimated locations: This factor is used to rank restaurants by a notion of “importance”. A restaurant which is just outside the fixed radius from above can be addressed after another restaurant which is a kilometer away.

另外也有不少其他的改善 (像是必須在離餐聽某個距離內才能點「取餐」,這個「距離」會因為餐聽可能在室內商場而需要的調整),整個成果就會反應在訂單的取消率大幅下降:

整體看起來是系統產生清單後讓人工後續處理 (像是打電話去店家問?),但這個方式所提供的清單準確度應該很高 (因為司機不會沒事跟自己時間過不去,跑到奇怪地方按下取餐),用這些資料跑簡單的演算法就能夠快速修正不少問題...

西班牙透過新法規限制 Uber 營業

包括 UberCabify 都受到新規範影響:「Ride-hailing companies suspend Barcelona services after new regulations」。

新規範限制乘客必須在上車前十五分鐘叫車:

The Catalan government ruled that ride-hailing services could only pick up passengers after a 15-minute delay from the time they were booked.

不是直接說你違法,而是用這個方式壓制隨叫隨到的服務... 這個方式應該會擴散到其他地區。

加州在四月將會開放無人自駕車上路了...

TechCrunch 看到加州要開放自駕車上路了:「California to allow testing of self-driving cars without a driver present」。

California’s Department of Motor Vehicles established new rules announced Monday that will allow tech companies and others working on driverless vehicle systems to begin trialling their cars without a safety driver at the wheel. The new rules go into effect starting April 2.

不過不是完全獨立運作,而是有附加條件,讓遠端的控制中心可以在必要時介入:

This doesn’t mean test vehicles will be out there on the roads without any kind of human intervention backup – the DMV will require that those testing autonomous cars without a driver present have a dedicated communications channel that ties the car to a remote operator, who can take over if needed. The cars will also need to be hardened against cyber attacks and be able to provide their owner and operator info to any other parties in the event of an accident.

馬上想到刷機 JB... XD

FreeBSD 上的 ccp (AMD Crypto Co-Processor)

看到 FreeBSD 上的「[base] Revision 328150」,將 AMD 的 AMD Crypto Co-Processor。

然後實測效能頗爛 XDDD 不過本來就不是以效能為主吧... 應該是以安全性與 Trusted Platform Module 考量?

像是 4KB buffer 的效能明顯比 AES-NI 慢了一大截 (少了一個零 XDDD):

aesni:      SHA1: ~8300 Mb/s    SHA256: ~8000 Mb/s
ccp:               ~630 Mb/s    SHA256:  ~660 Mb/s  SHA512:  ~700 Mb/s
cryptosoft:       ~1800 Mb/s    SHA256: ~1800 Mb/s  SHA512: ~2700 Mb/s

如果是 128KB buffer 時會好一些:

aesni:      SHA1:~10400 Mb/s    SHA256: ~9950 Mb/s
ccp:              ~2200 Mb/s    SHA256: ~2600 Mb/s  SHA512: ~3800 Mb/s
cryptosoft:       ~1750 Mb/s    SHA256: ~1800 Mb/s  SHA512: ~2700 Mb/s

然後 AES 也類似:

aesni:      4kB: ~11250 Mb/s    128kB: ~11250 Mb/s
ccp:               ~350 Mb/s    128kB:  ~4600 Mb/s
cryptosoft:       ~1750 Mb/s    128kB:  ~1700 Mb/s

所以是 sponsor 有認證需要的關係嗎...

Sponsored by: Dell EMC Isilon