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First two not verbatim quotes, real programmers can write FORTRAN code in any language and computer scientists solve yesterday's problems with tomorrow's hardware. i am as old as FORTRAN and have coded in FORTRAN, C, APL ALGOL, Forth, LISP, Basic, Pascal, Mathematica, MATLAB, various scripting languages ,... even run scientific computation on laser printers overnight using POSTSCRIPT. Also I have written real time operating systems in C and assembly language. I firmly believe that the design philosophy of modern computer languages became decoupled from consideration of current and future hardware capabilities and the more general resources available prior and during execution of a program. Also operating system support for client processes is mostly very poor. For example, parallel code execution was not possible on early computers. Modern vector processors, FPGAs or multi core CPUs can handle concurrent parallel computation but efficient formalized code design is sorely lacking.

everything is nice, super-duper, but show a software example that uses AIE for..AI, a code that starts from c/c++ level....no FIR filter, no RGB conversion, but really something related to AI...show at least something!

thank you for introducing some concepts i had not really considered before. will future compiler mix languages and optimise codes to use different hardware for operations. great talk,thank you, now i am thinking AI tools could optimise fortran, c/cuda, python... ?

is this planned to be released in a general purpose release/integration with current kafka versions. Is this usable for production use-cases or is it still under some testing?

Dear Mr. Hoefler! Thanks for offering the useful video. May I ask if you could please share the slide of the video with me?

TIOBE Index for May 2024: Fortran in the top 10

Fortran is still used today, particularly in scientific, engineering, and high-performance computing applications where numerical computation and performance are critical. While newer languages like Python and Julia have gained popularity for general-purpose programming and rapid prototyping, Fortran remains widely used in fields such as computational physics, climate modeling, computational chemistry, and finite element analysis

Correction: 33.45. When he says, "4 ecks speed up", he means "four _times_ "

good work

i really doubt you can run yolo on versal :P

My friend claimed that he can program in Fortran everything. How it is true! I also converted many original weather model calculations from Algol to Fortran. They work great.

Thank you so much, it was very helpful.

great explaination, this video saved my hours of spending in understanding it from docs.

the sound is terrible! Fix it if you want to listened

Thank you very much for this talk, and especially for providing context for where it sits in the field.

Excellent presentation ... the AI Engine (AIE) looks great !

Nice talk! Super interesting to see this DL-HPC double perspective. Btw the email address of the website mentioned at 57:55 for hiring seems unreacheable. The emails sent are bounced back after 2-3 days without being delivered.

Which software did u used for designing

Great stuff!

But what happens when one node has multiple connected nodes which have a lower ID. Then one node would need to point to multiple other nodes. What am I missing?

Very good paper, love from 🇨🇳

wasn't there an interesting way to deliver this? i mean, seriously?

Most of the paths in a large system are 4 hops long, not three. In the 545 group example he uses, there is only one link between each global group. So, the first two hops get you to the correct global bus. The next two hops get you to the terminal switch. The largest system that would have a three hop maximum is 9,216 comprising 18 groups.

Well, yeh, you are really not interested DDL.

Nice one pal 😀!! Get rapid results > 'promosm' .

That's really cool, hope I can get an opportunity for the possible PhD postion in your lab😃

Is there any open PhD position at ETH on these topics ?

and where am I?

Society 5.0 ... Is that where we finally get a for-loop to iterate through the entire population to serve every inhabitant's needs and desires, instead of passing policies in a top-down approach and wondering why there are still dissatisfied people left behind somehow... or is this going to be another instance of promising technology that only entrenches preexisting distributions of security, opportunities, comforts and luxuries. And for the record, karma is illegal vigilantism. Most promising technology starts with idealistic intentions and ends up being misused to dish out varying degrees of harm. Pardon me if I don't understand why my for-loop hasn't already been implemented on a 486. Maybe that'll be Society 6.0. But obviously, great work by the researchers. Let's just hope the decision-makers live up to the same standard of effort and quality of intent.

What is the difference between a program dependence graph[by Ferrante & ottenstein] and contextual flow graph?

Insightful

Could you please upload your source code again?It seems to be 404 now:(

The best tutorial to get idea of how cool actual programming is for hpc using hls fpga

Y f

she is amazing

Good day Mr. Hoefler, a very good and extensive overview of this huge topic. Thanks a lot.

This is just truly a great gem!

can I have this ppt?

This is very informative content, but you need to slow down because you end up mispronouncing words at times.

Excellent review of the state of the art, well explained and concise, thank you Torsten!

Nice slides! However the speaker speaks too fast like a rapper, leaves me with painful headaches after the talk. : ((((

Very helpful! Thank you!

Very nice presentation

Thanks a ton. Very helpful 👌

where can i find the slides of this presentation

Thanks for your efforts.

Great presentation!

GPT-2 model memory will saturate one of the WSC SRAM, right?

At 1:50 Prof. Hoefler says we will not use linearizability in the lecture. To clarify: We do not use linearizability in this lecture, but we will introduce linearizability in a later lecture.

You can put a random projection before a sparse neural network. This shares out all the information everywhere in the input evenly. Then each sparse dot product gets a fair sub-sample of the input vector. A more structured sub-random projection could be better.