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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. |