Will probably be used for nefarious reasons
Julia receives DARPA award to accelerate electronics simulation
161–170 of 175 posts
Re: Julia receives DARPA award to accelerate electronics simulation
#162Call me skeptical but to claim 1000x improvement for circuit simulation is at least hyperbole or outright lying. I will love to be proven wrong though. This by the fact that Julia is still depending on third party numerical libraries, e.g. OpenBLAS that depends on faster numerical languages like Fortran. If you want to see next generation circuit simulation and automation that already working check out JITX. The simu…
I am not sure why you are downvoted.
Google has been doing electronic with ML for some time now : https://ai.googleblog.com/2021/02/machine-learning-for-compu...
Python and Tensorflow work just fine for that task, the speed comes from LLVM and dedicated hardware such as TPU. The language in which the high level ML network/algorithm is specified doesn't matter much.
It is always good to have several paths explored, but I'm skeptical on the Julia hype since its beginning.
Re: Julia receives DARPA award to accelerate electronics simulation
#163Earlier quoted context omitted.
I'm one of the main developers of BioJulia. I believe our main issue is lack of developer manpower, and not necessarily lack of funds. Of course, if we got enough money to actually employ a developer, that would be amazing. It's just not very realistic. Furthermore, having BioJulia be developed by working scientists has its advantages. If you, or anyone else, is interested in BioJulia, do think about making a contrib…
Are there signs of wider BioJulia adoption? Looks like bio frameworks for Python, Go and even Rust still more popular. Disclaimer: I'm just checking GitHub stars, have no glue what metrics would be more appropriate.
I can't tell how many users we have, or who they are. Unless they directly interact by e.g. raising a GitHub issue, I won't know they exist.
In my broader experience, almost no bioinformaticians use Julia. I think we, as a field, are more conservative than e.g. physicists when it comes to technology. My old institute taught me Perl as the lingua franca of bioinformatics as late as 2015 (but switched to Python the year after).
I think we, as a field, have been consistently fairly poor at choosing our programming tools. Old bioinfo scripts are cluttered mess of write-only spaghetti-Perl. Most bioinformaticians I know don't use Biopython or Bioperl or anything similar, but rather creates new programs or packages by either re-implementing the basics from scratch, or by duct-taping together static binaries and/or scripts through shell commands.
We will never get rid of having to use static binaries or external scripts, but I think BioJulia at least have a decent chance of stopping people from re-implementing the basics again and again, and providing a central "platform" that various external scripts communicate through (e.g. an old Perl script may produce DNA as a FASTA file, when can then be fed into BioJulia). The main issue is to have bioinformaticians understand the current situation is problematic.
It doesn't take much to have a big impact, I think. If we had an ecosystem of the most basic data types (biosequences, kmers, phylogenetic trees and protein structures), a collection of well-known fundamental functions to operate on them, and parsers for the 20 most common formats, we would already have a very compelling ecosystem.
Re: Julia receives DARPA award to accelerate electronics simulation
#164So Julia's a fed now, cool uninstalling
Re: Julia receives DARPA award to accelerate electronics simulation
#165Call me skeptical but to claim 1000x improvement for circuit simulation is at least hyperbole or outright lying. I will love to be proven wrong though. This by the fact that Julia is still depending on third party numerical libraries, e.g. OpenBLAS that depends on faster numerical languages like Fortran. If you want to see next generation circuit simulation and automation that already working check out JITX. The simu…
Those efforts are quite different, focusing primarily on the design side with a focus on digital, while our effort is on ML-accelerated simulation of continuous time analog domain problems. I've met Chris Latter many times, and I'm pleased to see him work on tooling improvement in this space. He has a very good design sense for what compilers ought to look like. I've also met Patrick Li and we had an extensive discus…
Also, the major point is that BLAS has little to no role played here. Algorithms which just hit BLAS are very suboptimal already. There's a tearing step which reduces the problem to many subproblems which is then more optimally handled by pure Julia numerical linear algebra libraries which greatly outperform OpenBLAS in the regime they are in:
https://github.com/YingboMa/RecursiveFactorization.jl#perfor...
And there are hooks in the differential equation solvers to not use OpenBLAS in many cases for this reason:
https://github.com/SciML/DiffEqBase.jl/blob/master/src/linea...
Instead what this comes out to is more of a deconstructed KLU, except instead of parsing to a single sparse linear solve you can do semi-independent nonlinear solves which are then spawning parallel jobs of small semi-dense linear solves which are handled by these pure Julia linear algebra libraries.
And that's only a small fraction of the details. But at the end of the day, if someone is thinking "BLAS", they are already about an order of magnitude behind on speed. The algorithms to do this effectively are much more complex than that.
Re: Julia receives DARPA award to accelerate electronics simulation
#166Earlier quoted context omitted.
Those efforts are quite different, focusing primarily on the design side with a focus on digital, while our effort is on ML-accelerated simulation of continuous time analog domain problems. I've met Chris Latter many times, and I'm pleased to see him work on tooling improvement in this space. He has a very good design sense for what compilers ought to look like. I've also met Patrick Li and we had an extensive discus…
Most of the methods I've mentioned apart from FIRRTL can be used for analog design. In fact, the JITX's product in particular only support ML-accelerated analog based design mainly circuit board level at the moment but nothing stopping them for doing it for both analog and digital design later on. Don't get me wrong I'm not dismissing your work, I'm just dismissal of the outrages 1000x claim, as they say "extraordina…
The pure Julia (sub)BLAS (because they are incomplete right now) that benchmarks the best right now are Octavian and PaddedMatrices.jl. On Ryzen these BLAS's are doing extremely well:
https://github.com/JuliaLinearAlgebra/Octavian.jl/issues/24#...
but also on Intel:
https://chriselrod.github.io/PaddedMatrices.jl/dev/arches/ca...
I personally wouldn't spend too much time on BLAS-limited applications though, and this kind of circuit modeling is not one of them as I describe in another post. Also, it's 1000x at 99% accuracy: it's essentially a form of automated model order reduction which allows you to choose a tolerance and get more speedup matching the original circuit to the given tolerance.
Re: Julia receives DARPA award to accelerate electronics simulation
#167Call me skeptical but to claim 1000x improvement for circuit simulation is at least hyperbole or outright lying. I will love to be proven wrong though. This by the fact that Julia is still depending on third party numerical libraries, e.g. OpenBLAS that depends on faster numerical languages like Fortran. If you want to see next generation circuit simulation and automation that already working check out JITX. The simu…
I am not sure why you are downvoted. Google has been doing electronic with ML for some time now : https://ai.googleblog.com/2021/02/machine-learning-for-compu... Python and Tensorflow work just fine for that task, the speed comes from LLVM and dedicated hardware such as TPU. The language in which the high level ML network/algorithm is specified doesn't matter much. It is always good to have several paths explored, bu…
What it's doing is searching through circuit architectures, not accelerating the circuit simulation itself. If you have small quick circuits that's fine, but if solving the circuit once is a hard enough problem (which is true in the problems which we are targeting) then this problem would be infeasible with Google's algorithm because it relies on whatever simulator to exist. So it's just different methods targeting completely different sets of problems.
Re: Julia receives DARPA award to accelerate electronics simulation
#168Earlier quoted context omitted.
I had to leave a company over that. I had made a personal fpga construct generator for various easily parameterizable modules. It worked pretty well but it was a mess and needed to be redone pretty badly, but it was good enough. I shared it upon request with a couple of engineers and before I knew it there were probably 25 EEs using it. They wanted "more" and I told them it was a personal tool and they were more than…
If they didn't lay claim to the IP, you should polish it and offer it for sale, or use it as interview material at someplace developing similar technology.
First and foremost: I AM NOT A LAWYER.
With that out of the way...
They could argue that since it was a tool that was made to help with his job at at the company, then it's internally developed. If there are no clear grounds or easily presentable evidence (even if there was!), he's out in the wild with a liability.
The only place where I can see him in the clear is if they had a repo going back to before the work on the company and they could prove the tool was a personal project, and unrelated to the job. Even then, Company could still sue and burden them with legal fees/process until they tire him out and BINGO, now they own their IP.
The alternative could be starting a new project, completely open source from the start (probably with one of the more liberal licenses) and get crowdfunding to develop and maintain it. Assuming they are interested in doing that, of course.
Re: Julia receives DARPA award to accelerate electronics simulation
#169Earlier quoted context omitted.
I am not sure why you are downvoted. Google has been doing electronic with ML for some time now : https://ai.googleblog.com/2021/02/machine-learning-for-compu... Python and Tensorflow work just fine for that task, the speed comes from LLVM and dedicated hardware such as TPU. The language in which the high level ML network/algorithm is specified doesn't matter much. It is always good to have several paths explored, bu…
What it's doing is searching through circuit architectures, not accelerating the circuit simulation itself. If you have small quick circuits that's fine, but if solving the circuit once is a hard enough problem (which is true in the problems which we are targeting) then this problem would be infeasible with Google's algorithm because it relies on whatever simulator to exist. So it's just different methods targeting c…
Well, that was just one public blog post. I would expect Google's R&D to go beyond what they publicly present.
Re: Julia receives DARPA award to accelerate electronics simulation
#170I guessing that as long as you're not trying to plot the results, julia is fast?