Why isn't Rust top at modeling and simulation yet?
This is about Julia Computing, the company, not Julia the language.
Maybe they are using Rust, then...
Julia receives DARPA award to accelerate electronics simulation
41–50 of 175 posts
Re: Julia receives DARPA award to accelerate electronics simulation
#42Is scientific computing getting some revival with the advent of quantum computers? From what I could see the niche is relatively small and not well paid, with most jobs somehow tied to the public sector. Not sure how Julia factors into all of this. I don't think the programming language makes that big of a difference, ultimately. Very interesting field at the intersection of all my skills, but I'm hesitant to get int…
Scientific computing has always been around, but as you allude, a bit in the background. It used to be matlab/mathematica on your desktop or a supercomputer that very few could get access to and program. With cloud computing, GPUs, ability to get terabytes of RAM on a single compute node, and all the exciting developments in CPUs despite skirting the edge of Moore's Law - a lot of opportunities are showing up.
You can now simulate science in ways like never before. Today, the median scientist can easily rent a cluster of hundreds of nodes for a few hundred dollars an hour. It is increasingly the case that you can actually simulate entire products in silico before you do anything in the lab. SciML is a large part of that story because we are able to use ML to approximate science and speed it up even more.
I like to think about it as follows - 10x faster CPUs, 100x from GPUs when possible, 100x from ML when possible, 100x through easier access to parallel computing on cloud. So your best case speedup compared to a decade ago is easily 10^7x. Because of this huge space for improvement, we can easily find 1000x improvements in so many cases.
And this is what we as software engineers can do to change the world - by simulating science, building new batteries, designing new drugs, solving power infrastructure, getting climate right and its impact on our cities, food production, and so on and so forth.
Bret Victor captures this really well in his essay: http://worrydream.com/ClimateChange/ and at Julia Computing, we are doing a lot of what it outlines, and really grateful that ARPA-e and DARPA are funding all this hard science and improvements to Julia and its ecosystem.
Re: Julia receives DARPA award to accelerate electronics simulation
#43Huh, did not expect our press release to end up on HN, but I'm the PI on this project, so AMA, I guess. Will jump into the questions already asked here and provide some context.
What was the technology replaced by Julia (i.e. the systems which were 100 or 1000 times slower)? Is the whole factor 1000 because of Julia/LLVM optimizations, or is there faster hardware, or was the old system an interpreter?
This is about funding for future work, not payment for achieved bonuses, it seems.
Re: Julia receives DARPA award to accelerate electronics simulation
#44Fix the headline - it should say "Julia Computing Receives DARPA Award to Accelerate Electronics Simulation by 1,000x" The money is going to a company.
Impossible. That has 84 characters. Also, since it doesn’t seem possible for a language to receive grant money, I don’t think there is much chance for a dangerous level of confusion.
Re: Julia receives DARPA award to accelerate electronics simulation
#45So the machines are gonna start building the machines?
Re: Julia receives DARPA award to accelerate electronics simulation
#46I'm having a lot of trouble understanding how "just add AI" is going to make electronics simulation 1000x faster.
Chris has posted the relevant papers in a sibling comment, but let me try to give an intuitive explanation. Say you have some sort of electronic device like an amplifier. It is made out of many individual transistors and parasitics, but its time evolution is overall guided mostly by the amplifier characteristics its supposed to implement plus boundary effects and corrections from the parasitics. So what you try to do…
Since this is based on ML, I would have trouble trusting the results. Can you verify the results with standard mathematical methods? E.g. solve a linear system using AI, then compute the residue and meaningfully interpret it? Would it be possible to apply ML techniques iteratively, and let the error approach zero?
Re: Julia receives DARPA award to accelerate electronics simulation
#47Huh, did not expect our press release to end up on HN, but I'm the PI on this project, so AMA, I guess. Will jump into the questions already asked here and provide some context.
What was the technology replaced by Julia (i.e. the systems which were 100 or 1000 times slower)? Is the whole factor 1000 because of Julia/LLVM optimizations, or is there faster hardware, or was the old system an interpreter?
The 1000x goal here specially refers to algorithmic accelerations. Depending on your baseline simulator, there may also be improvements because Julia's simulation capabilities are extremely fast, but those gains are multiplicative, since we're using our own simulator as a baseline for measuring the algorithmic improvements. Just to be clear though the 1000x is a goal. We've demonstrated the same techniques at about 100x algorithmic improvement (for close to 1000x overall), but haven't quite gotten to that point yet in this domain. It is still a research project at this point. We are quite actively exploring what to do with just the simulator components though, because we think there's a good opportunity to replace some legacy tooling in this space even before factoring in the algorithmic improvements.
Re: Julia receives DARPA award to accelerate electronics simulation
#48Why isn't Rust top at modeling and simulation yet?
What advantage does Rust have over Julia? I can think of a lot of disadvantages, but not many advantages.
Re: Julia receives DARPA award to accelerate electronics simulation
#49Earlier quoted context omitted.
Chris has posted the relevant papers in a sibling comment, but let me try to give an intuitive explanation. Say you have some sort of electronic device like an amplifier. It is made out of many individual transistors and parasitics, but its time evolution is overall guided mostly by the amplifier characteristics its supposed to implement plus boundary effects and corrections from the parasitics. So what you try to do…
Since this is based on ML, I would have trouble trusting the results. Can you verify the results with standard mathematical methods? E.g. solve a linear system using AI, then compute the residue and meaningfully interpret it? Would it be possible to apply ML techniques iteratively, and let the error approach zero?
Sure, you can always just run the baseline simulation and compare error rates. You can also do fancier analyses to get some rough idea of robustness and maximum error rates (over your particular parameter domain).
Re: Julia receives DARPA award to accelerate electronics simulation
#50I wish BioJulia[1][2] (bioinformatics ecosystem with Julia language) would get a similar attention. Currently, they seem largely underfunded[3]. Global pandemic highlighted importance of such projects, I hope more and more people would participate into the FOSS-based computational biology and medicine.