Skimming through this book, one thing i was constantly wondering, is how well does this ocaml framework use the hardware.<p>Leaving ocaml aside, the connection between scientific computing and hardware is the one thing I miss the most in "scientific computing" books and courses, because it sooner or later limits the science that any researcher doing scientific computing can do.<p>To give an example, earlier this week, one of our scientists was waiting 10 minutes between each interactive iteration of their data-set, so I was called to help, and the only feedback they gave was that "its slow", to which I replied "slow with respect to what? how fast are you expecting this to be and _why_?".<p>The answer to these questions is the difference between "maybe they just need a faster computer", "maybe they need a different algorithm", or even "maybe this problem cannot be solved today because computers this fast do not exist".<p>From their facial expression, it looked to me that they actually had never thought about any of this, probably because whatever they did before was always fast enough, but now this issue was limiting their science and they were lacking the bare minimum set of tools to even get proper help.<p>If you are doing scientific computing, chances are that the problems you are going to be dealing with are going to be getting bigger and harder as you advance in your career. For many scientists, the first problems will actually be big enough for the hardware to matter.<p>I wish scientific computing courses and books will at least provide the most basic tools to these scientist for them to at least be able to get meaningful help. Having someone on call for when this matters is quite expensive.