Our preliminary benchmark suggests it is ~30% faster than our previous air-cooled 5950X in a variety of computing task. 100% faster when running matrix multiplication with Intel MKL.
![](https://i0.wp.com/www.ticoneva.com/journal/wp-content/uploads/2023/07/IMG-20230704-WA0008-1024x768.jpg?resize=604%2C453&ssl=1)
Running RStudio within Jupyter has been possible for quite some time with jupyter-server-proxy
. Doing so has its benefits, notably the ability to leverage JupyterHub’s systemdspawner
to control the amount of resources users can use, a feature that is not available in the free version of RStudio.
It would have been nice to have the ability to choose between different R versions, which is another feature that is only available in the paid version of RStudio. Because jupyter-server-proxy
relies on iterating through entry points in each proxy package, the only way to enable that right now is to modify jupyter-ression-proxy
itself.
This is where our #PR133 comes in. By allowing setup_rserver
to receive a custom name and a configuration file as arguments, all it takes to add additional R versions on Jupyter is to create a new skeleton package that imports jupyter-ression-proxy:setup_rserver and
has additional entry points for jupyter_serverproxy_servers.
Here is a working example we currently use on our HPC cluster.
How hard it is to build a server with four top-of-the-line GPU for an high-performance computing cluster? Harder than you might think.
When I started building the SCRP cluster back in 2020 summer, the GPU servers were provided by Asrock Rack. Everything except the GPUs were preassembled. This is the sensible thing to do in normal times.
Fast forward to 2021 summer, and times were not normal. The supply chain distribution and semiconductor shortage were in high gear. Pretty much every name-brand server manufacturer quoted us months-long lead time, if they were willing to deal with us at all. To get everything in for the new academic year, I constructed a series of servers with parts sourced from different parts of the world. It is actually not that hard to build servers—they are basically heavy-duty PC’s with all sorts of specialized parts—that is, unless you want a GPU server suitable for an HPC cluster.
So what is so special about GPU servers for HPC cluster?
To conclude, if you think building your own PC is challenging, building a GPU server for an HPC cluster is probably three times the challenge. Another reason why you should not maintain your own infrastructure.
That’s one long extending cord.
There, nothing a bit of art and craft couldn’t fix.
Spent over an hour trying to figure out why some new GPUs were not working. The server is concern is a Asrock Rack 2U4G-EPYC-2T, which is a specialized server that allows four GPUs to be installed in a relatively small case. Google was not helpful because, understandably, this is a niche product produced only in small quantities.
What did not work:
What work:
Took me a good hour to figure out that the issue was caused by the PCIe extender board. The three GPU positions at the front require the extender board, but the board was only for PCIe Gen 3. Normally, Gen 4 GPUs can negotiate with Gen 3 mainboards to communicate in PCIe Gen 3, but apparently they cannot do that through the extender board. Once the issue had been identified, the solution was actually very straightforward—manually setting the PCIe lanes to Gen 3 solves everything.
Yet another reason why maintaining your own computing infrastructure is not for the faint hearted.
今屆諾貝爾經濟學獎由加洲大學柏克萊分校的大衛卡德(David Card)獲得一半獎金。以「泰斗」來形容卡德在柏克萊經濟系的地位絕不為過。柏克萊經濟系每年有近三分一的博士生由卡德指導。因為門生眾多,學生要見卡德的話好幾星期前就要登記。學系的最佳導師獎亦多次由卡德奪得,以至學系其他教授有一年半開完笑的說,要把獎項命名為「卡德獎」,以免他再次獲獎。
作為全世界首屈一指的經濟系,柏克萊每星期有近十場的學術研討會,廣邀各範疇經濟學者來分享其研究。當中卡德領導的勞工經濟學研討會肯定是最受歡迎的,即使研究範疇和勞工經濟學無關的學生都一定參加過。何解?因為研討會包精美午餐。柏克萊作為公營大學,又座落生活費高昂三藩市灣區,博士生的助學金實在捉襟見肘,勞工經濟學研討會那次午餐對不少學生來說大概是全週吃得最好的一次了。
卡德以其最低工資研究最為人熟知,但最低工資研究只是果,卡德最看重的是研究方法,所以諾獎亦強調他對自然實驗這研究方法的貢獻。我在柏克萊就讀其間,卡德正大力推廣在統計分析中加入理論架構,希望經濟學研究不止找出簡單的平均關係。這看法對近十多年來柏克萊出身的經濟學者有著深遠的影響。
又:卡德該篇最低工資研究的合著者,普林斯頓大學經濟學教授克魯格(Alan Krueger)不幸在2019離世。行內普遍認同若克魯格仍在生,他幾無疑問會和卡德分享今年的獎項。
(照片攝於2007年5月3日,卡德教授的柏克萊勞工經濟學博士課程。)
We will be running tests and benchmarks here at CUHK SCRP over the next few days. Users should be able to access the new RTX 3090 through Slurm after the scheduled maintenance next week.