spacedome

6 ms·

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Looking at the FFT in Haskell and Futhark(spacedome.tv)
4 points by spacedome 2y ago|1 comments
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by spacedome 2y ago
A small writeup on the Fast Fourier Transform and implementing it in functional languages.
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by spacedome 4y ago
Thanks for writing this up, Chris! I took a break from Julia a year or two ago because of some of these issues, one of the big ones being I didn't want to write and maintain a set of non-allocating LAPACK wrappers for iterative solvers
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by spacedome 5y ago
I love Julia and choose to work in it almost exclusively, but I agree with the points in the article. I've run into a lot of issues just writing numerical linear algebra type algorithms. Even core, and not quite core but maintained by
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by spacedome 5y ago
Why not just use fixed point? That's specifying how much precision you need in a fixed way and has the benefit of being quite simple
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by spacedome 6y ago
You can do operator overloading in fortran to add a new number type, don't remember the details but I wrote a quaternion library once.
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by spacedome 6y ago
I meant doing day trades of long term puts, but yes I agree any money here is a huge gamble.
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by spacedome 6y ago
It looks good, I'll definitely give it a real try if I start trading options more, the order flow data alone looks like it would make it worthwhile. Do you incorporate L2 data? Couldn't find that anywhere, only thing that seems li
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by spacedome 6y ago
You can make big money trading the volitility, if you get really lucky. The price of some puts I looked at went up 500%+ during the drop today. Would not try this personally lol
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by spacedome 6y ago
Looking right now some of the options a few months out have implied volitility of 1000%+, your best bet might be selling them off on big crashes instead of waiting it out? Some of the puts I looked at went up 500%+ after this dip
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by spacedome 6y ago
What kind of premiums did you pay on those puts though? Last I looked the prices were pretty insane.
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by spacedome 6y ago
What do you think of tradytics? Looks really interesting, though I mainly trade OTC, which I sort of doubt it has much support for.
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by spacedome 6y ago
You absolutely can use regular jupyter notebooks for julia! Pluto has some advantages, like being stored as a normal julia file. The julia startup time issues affect both.
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by spacedome 6y ago
You are making a lot of ontological and epistemological assumptions that are contentious in the philosophy of math. Not saying you are wrong in thinking this, metaphysical questions don't necessarily have answers, but many would not ag
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by spacedome 6y ago
I agree this is a common sentiment among mathematicians, but this is a very modern perspective. If you look back 100 years ago to Hilbert, there was less distinction between physicists and mathematicians, much less the pure/applied ri
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by spacedome 6y ago
As an 'academic' who has done plenty of physical labor, I find this argument reductive and offensive. You can disagree with the author without painting this negative picture of them.
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by spacedome 6y ago
On demand printing has also been horrible for textbooks/monographs, Springer being one of the worst. Very few copies are printed, but they serve as an important means of preserving this knowledge, and books that do not survive a single
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by spacedome 6y ago
It is entirely possible, but is not trivial, especially for the user who then needs to know "arcane knowledge" of BLAS/LAPACK work array sizes and flags. There was some discussion about this on github, but it sort of trailed
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by spacedome 6y ago
Yes, many of these are "in-place" but will still allocate. I typically use the geev!/ggev!/geevx! routines, if you look at the source code you will see that the work arrays are still allocated inside the call. The in-pla
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by spacedome 6y ago
Higher level BLAS operations, such as solving a linear system, or computing svd/eigen, cannot be done in-place the way matrix multiplication can, and require additional memory of a predetermined, fixed size, called the work array. This
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by spacedome 6y ago
As much as I like Julia, I think "trivial to write allocation free code" is a bit of an overstatement. Depending on what you are doing, it can be difficult, for example iteratively calling any of the LinearAlgebra methods, since t
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by spacedome 6y ago
This is good to know, thanks Chris. I mostly solve sparse PDEs, being able to always use LU makes everything much simpler, especially with quaternions.
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by spacedome 6y ago
I found this approach to work well for linear systems, here is some rough code I used for the representation map (note the jmag/kmag functions are part of my implementation, not sure what the equivalent is for Quaternions.jl). func
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by spacedome 6y ago
My experience with various linear solvers (which a diffeq solver typicaly relies on) is that if they assume commutivity anywhere at all, which they often do, they almost definitely will not work for quaternions. Even if a derivation of the
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by spacedome 6y ago
I think the functionality of Julia's sparse arrays is mostly on par with scipy.sparse, it's just a bit rough around some edges still, and spread out into non-base packages (e.g. into IterativeSolvers.jl, ARPACK.jl, MUMPS(*).jl etc
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by spacedome 6y ago
I have only seen this happen with packages that depend on non-julia libraries, such as ARPACK, but the move to providing binaries with BinaryBuilder should fix this.
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by spacedome 6y ago
The person I was responding to seemed to have read my comment and thought I had an issue with raw performance. My point above is that writing iterative code that makes many LAPACK calls becomes difficult to write in Julia because there is n
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by spacedome 6y ago
This is great, a lot of the performance in BLAS comes from memory management. This does not solve my problem though. Sometimes you want to manually control memory, and Julia does not make it easy to do so. In particular when interfacing wit
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by spacedome 6y ago
They are solving the linear system Ax=b , written in Julia/Matlab usually as A\b , specifically for a fixed size 5x5 matrix. The Grassmann algebras can give a way of representing the matrix and vectors, and a comparison is being made
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by spacedome 6y ago
This has been my general experience writing numerical linear algebra methods in Julia. Optimizing a specific algorithm, eliminating unnecessary allocations is the first thing I do, and can give large performance gains, especially for iterat

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