The PI started asking me to run some analyses on a raw dataset. Since I was so new at it, I often messed up and had to rerun the whole thing after looking at the output; this was painful because the entire script took a few hours to run.
I started poking around to see whether it could be optimized at all. the raw data was divided up into hundreds files from different runs, sensors, etc..., that were each processed independently in sequence, and the results were all combined together into a big array for the final result. Seems reasonable enough.
Except this code was all written by scientists, and the combination was done in the "naive" way - after each of data files was processed, a new array was created and the previous results were copied into the new array, as were the results from the current data file. This meant that for the iterations at the end, we roughly needed to have Memory = 2 * Size of final data, which eventually exceeded the amount of physical memory on the machine (and because there were so many data files, it was doing this allocation and copying dozens of times after it used all the RAM).
I updated this to pre-allocate the required size at the beginning for a very very easy 3-4 fold improvement in the overall runtime and felt rather proud of myself.
To his credit once I (as nicely as possible) showed him how to do it with two nested for-loops he clearly felt stupid and conceded the point. He was otherwise a very smart guy and good to work with, but goes to show how we can take our training for granted. Even freshman-level stuff goes over the heads of PhDs, and I'm sure the same would be true if I were to drop into a biochem lab.
Without even looking at the processing function, which I considered some sciency science, I set up pthreads and mutexes on the result array and such to reap almost perfectly linear scaling. So far, so good.
Then I ran a profiler to see what was actually taking so long.
... Uh, why are you spending all this time copying strings back and forth?
Turns out they passed all strings by value. Sprinkling in a few const & here and there got a 1000-fold speedup or such. I felt pretty stupid for my multithreading antics after that.
Also, H5 data formats[0] have been a god-send for scientific computing, due to its ability to inherently make sense of how to store your data. You can have your previous results curried over into your new analysis without doubling your data.
I believe what was roughly happening under the hood was: 1. Allocate an array `tmp` of size `length of allOrbitFiles` + `length of currentOrbitFiles`. 2. Copy data from `allOrbitFiles` over to `tmp`. 3. Copy data from `currentOrbitFiles` to `tmp` 4. Reassign `allOrbitFiles` to the new array `tmp`. 5. Garbage collect the old `allOrbitFiles`.
So the doubling of memory usage comes after Step 1. I would imagine (but don't know for sure) that this would actually occur in any garbage collected language I'm familiar with as well (Java, Python, Javascript).
A good example: there was recently a thread on the Julia discourse comparing Julia and Mojo. Julia used no external libraries (compared to 7 with Mojo) implemented a simpler, faster, and cleaner version of the Mojo code that was used to showcase how fast Mojo was: https://discourse.julialang.org/t/julia-mojo-mandelbrot-benc.... Then further still, folks were able to optimize for even more speed with various abstractions that let Julia take more advantage of the hardware.
That's the promise I think Julia makes and delivers on - you can write incredibly "fast" code simply and cleanly. Yes, you can have a higher standard of "fast" which requires a bit more advanced knowledge but I'd argue that Julia still offers the cleanest/simplest way to take advantage of those micro-optimizaitons.
Performance is on a spectrum, and usually a tradeoff against readability and conciseness. I think it IS true that Julia excels in that it gives, by far, the best expressibility/performance tradeoff.
Also, often, you really can get "free lunch" - there are many times where if you just do the obvious thing, Julia and its backend LLVM can optimise it to extremely efficient code. For a simple example, just summing an array with a for loop, for example.
This is quite a different situation to traditional scientific computing.
The site is a staticly published version of a Pluto notebook, which uses modern web features to enable interactivity, reactivity, code syntax highlighting, etc. etc. Tradeoffs to enable those features but requires enabling your browser features. The underlying file that the notebook is based on is just a basic `.jl` file, so you could happily run the notebook from a Julia instance instead of the browser-based notebook environment.
Julia itself will be happy to run however you'd like it to of course.
What scientists must know about hardware to write fast code (2020) - https://news.ycombinator.com/item?id=29601342 - Dec 2021 (29 comments)
For those folks, getting the output they need is much more important than the CPU cycles - as it should be.
As a C++ programmer, I posed the question as to why they don’t hire coders to do this for them. The answer was cost which rather surprised me given the cost of the LHC.
But for large problems the article falls short. Scientific applications may need to use several computers at a time, COMP Superscalar (COMPSs) is a task-based programming model which aims to ease the development of applications for distributed infrastructures. COMPSs programmers do not need to deal with the typical duties of parallelization and distribution, such as thread creation and synchronization, data distribution, messaging or fault tolerance. Instead, the model is based on sequential programming, which makes it appealing to users that either lack parallel programming expertise or are looking for better programmability. Other popular frameworks such as LEGION offer a lower-level interface.
A minor detail I find a bit confusing, though, is explaining the potential benefits of SMT/hyperthreading with an example where threads are spending some of their time idle (or sleeping).
I don't know Julia so I don't know if sleep is implemented with busy-waiting or something there, but generally if a thread is put to sleep, the thread gets blocked from being run until the timer expires or the sleep is interrupted. The operating system doesn't schedule the blocked thread for running on the CPU in the first place, so a thread that's sleeping is not sharing a CPU core with another thread that's being executed.
So the example does not finish 8 jobs almost as fast as 4 or 1 jobs using 4 cores due to SMT; it's rather that half of the time each of the threads is not even being scheduled for running. A total of eight concurrent jobs/threads works out to approximately four of them being eligible to run at a time, matching the four physical cores available.
If there are only four concurrent jobs/threads, each sleeping half of the time, you end up not utilizing the four cores fully because on average two of the cores will be idle with no thread scheduled.
AFAIK SMT should only really be beneficial in cases of stalls due to CPU internal reasons such as cache misses or branch mispredictions, not in cases of threads being blocked for I/O (or sleeping).
The post is of course correct in that the example computation benefits from a higher number of concurrent jobs because of each thread being blocked half of the time. However, that's unrelated to SMT.
Considering how meticulous and detailed the post generally is, I think it would make sense to more clearly separate SMT from the benefits of multithreading in case of partially I/O-bound work.
Thanks for the heads up!
This link is not meant for you. It is meant for a scientist, and most scientists do not also have an EE degree or CS degree.
How much graduate level biology, oceanography, physics, geology, chemistry, meteorology, or other scientific field do you know?
All of those have subfields where computational performance is important. My experience is scientists are more likely to pick up the software skills than EEs are willing to pick up the science background. (In part because scientific software development generally pays less well than commercial software development.)
If you had prepended the comment with something like "I love this topic!" to show enthusiasm or approval, you probably would have gotten a much different response.
