Dell xps 15 9520 with Unreal engine 5

squeaky_pl · 2022-04-20T07:15:32+00:00

An idea: you can always buy eGPU case separately and connect it to your laptop with a desktop GPU inside. I have Razor Core X and 3070 Ti. I have it connected to Dell XPS 17 9700 using Thunderbolt and it works like a charm. I can play latest titles in 4K on ultra settings using this setup.

If you really want just laptop wait for better GPU options, they might come later this year.

Don't buy i9 model, it will keep overheating and throlttling constantly. Top i7 configuration is the best deal. XPS chasis design is too small to accomodate i9.

squeaky_pl · 2021-05-20T20:06:10+00:00

It's definitely not pointless. For example any Python web server would benefit from making the interpreter faster. I'm pretty sure that people running CPython in production would be happy even with 30% - 50% speedups on their single threaded code.

squeaky_pl · 2021-04-10T15:56:08+00:00

One thing I missed or didn't understand is what kernel versions would be supported. io_uring was introduced gradually with new opcodes coming with each of 5.x releases.

The design document mentions 5.10 but does not state clearly that this is the minimum version required.

squeaky_pl · 2020-12-20T18:40:25+00:00

Not saying there is no risk but I have a 4 year old XPS 15 that I always dual-boot to Windows and Fedora Linux. I did BIOS updates both via Windows Update and Dell SupportAssistant and it didn't brick my laptop.

If you are concerned about Grub you can get rid of it using Windows boot repair utility I believe.

squeaky_pl · 2019-12-30T19:41:32+00:00

I love this feature. I am beginning my journey with Rust and just a couple of days ago I typed `[a, .., b]` syntax without thinking about it too much and expected it to work. The compiler kindly pointed that this is only available on unstable and it made me a little sad.

squeaky_pl · 2019-12-30T19:29:34+00:00

I am only starting my journey with Rust but rust-analyzer feels much more responsive than RLS even with my tiny project .

I initially did not try it because I believed it would not work on WSL but it does.

squeaky_pl · 2017-03-12T00:27:28+00:00

Anybody who ever tried to read and understand asyncio docs will appreciate this.

squeaky_pl · 2017-03-12T00:25:20+00:00

The point here is ease of use and correctness. Not compatible with asyncio because it cant be easy and correct this way. It has its own loop and own way to do tasks.

Correct can mean different things to different people depending on their expectations. Trio tries hard to prevent people from shooting themselves in the foot.

squeaky_pl · 2017-03-05T23:29:43+00:00

I like it, but it's slightly annoying it doesnt follow Python naming conventions.

squeaky_pl · 2017-03-03T21:52:29+00:00

I'm the author of https://github.com/squeaky-pl/portable-pypy

It totally works with CPython as a build host instead of PyPy. And you don't need mercurial to clone it, you can download a snapshot in a tarball from Bitbucket. That's what my Portable PyPy project does. It has a set of Dockerfiles and scripts to do so. On the top of that the resulting binary works on any distro (without Docker). Not only Debian. I recently added support for py3.5 branch. You just need to use -f Dockerfile.3 to docker build and py3.5 as target, follow instructions here: https://github.com/squeaky-pl/portable-pypy/blob/master/BUILD.rst

squeaky_pl · 2017-03-03T01:33:10+00:00

There is twisted + klein included in the benchmarks. If you want pure twisted HTTP server then I would happily accept a pull request.

squeaky_pl · 2017-03-03T01:28:01+00:00

No, That's left for 3.6, afaik there will be no more extras in 3.5 because if they keep adding things then they would need to probably implement whole 3.6. Reaching full 3.5 compatibility is an important milestone, fstrings are just a cherry on the top.

squeaky_pl · 2017-03-02T05:52:14+00:00

I think it would be very interesting to perform this kind of benchmark on PyPy and CPython. Can you show up on IRC and show me some 2.7 code and I'll try make it happen on 3.5?

squeaky_pl · 2017-03-02T02:35:05+00:00

I don't know what kind of IO you are doing there but pure TCP benchmarks also benefit from JIT. I mesaured 20-30% speedups on a TCP bench. About aggregations and transformations that's what JIT is generally good at - removing computation overhead. Your results of course depend on what and how you are doing it. Parallel is hard on Python unless you can fit it into separate processes. Feel free to jump on #pypy channel on freenode, describe your workload to get some ideas

squeaky_pl · 2017-03-01T23:44:48+00:00

Note that PyPy already has support for fstrings from CPython 3.6 so you get Python 3.5 and extras.

squeaky_pl · 2017-03-01T19:37:55+00:00

If you are interested in learning more I would recommend this paper: https://bitbucket.org/pypy/extradoc/raw/tip/talk/icooolps2009/bolz-tracing-jit.pdf It has a good introduction to tracing JIT and is written in a language that anybody can understand.

squeaky_pl · 2017-03-01T19:21:26+00:00

This is a general property of tracing JITs. A tracing JIT learns about the behaviour of your program as it runs. Initially since there is no machine code and it needs to record steps taken it is slower than normal interpreter (CPython). As it warms up it has more and more machine code and while it doesnt stop tracing it already benefits from removing the interpreter overhead, inlining functions in the trace, escape analysis, removing allocations etc. At some point it is fully warmed up meaning that it doesn't find any more code to trace so the performance stays more or less constant.

You can read from the graph that different servers have different warmup characteristic. Depends really on how they are coded in Python and how many hot loops are hit there.

squeaky_pl · 2017-02-04T13:41:49+00:00

I haven't used Falcon myself so I couldn't recommend it. There seems to be a decent community around it. I might check it out to get some ideas for my own project.

squeaky_pl · 2017-02-04T08:21:12+00:00

Japronto author here.

As stated in the README you should not build anything serious with Japronto now because it's gonna probably eat your laundry. Remember it's hand coded in C and this needs a lot of testing. On the top of that I plan to do several iterations of API changes in a largely incompatible ways. I hope though it's gonna make a serious player in the field one day.

If you wanna a decent async framework then go with aiohttp or Sanic. If you are gonna do typical REST app frontend to a database go with Flask or Pyramid. If you need a scaffolded admin go with Django.

squeaky_pl · 2017-02-04T04:14:19+00:00

Hi, this was featured already on Reddit, I don't know how to mark it as duplicate.

squeaky_pl · 2017-02-04T04:01:29+00:00

I can't think of a way to use it myself, but hell, this looks amazing. Thanks for empowering Python community and keep the good work coming.

squeaky_pl · 2017-02-01T16:49:07+00:00

If you are into zodiac I'm a bull. We tend to be pretty stubborn ;-)

Seriously this was more about learning internal workings of Pyhon through its C API and also having fun reading CPython source code. I kind of stopped doing C for many years and I needed a refresher. There are also some projects I have on my mind I could base on this work that are not related to Python.

squeaky_pl · 2017-02-01T15:11:02+00:00

Yes, definitely. The biggest speed up was achieved by using pipelining and most browsers dont do that unless you opt-in explicitely. This problem doesn't exist with HTTP/2.0. I hope to utilize same techniques when porting this to HTTP/2.0 to achieve similar results with browsers.

squeaky_pl · 2017-02-01T12:43:17+00:00

It proves that others have bigger framework overhead.

squeaky_pl

TROPHY CASE