Hi Kovas,
> By the way, I'd love to see matrix/tensor benchmarks of Neanderthal and > Vectorz vs ND4J, MXNet's NDArray, and BidMat.. :) > I don't have exact numbers, but will try to give you a few pointers to help you if you decide to investigate this further: 0. Neanderthal's scope is matrices and linear algebra. NNs and other stuff is something that could be built on top of it (assuming that the features needed are implemented, which may or may not be true yet), but certainly not in Neanderthal. 1. Neanderthal is a 100% Clojure solution. One of the main goals, other than the speed, of course, is that it is simple and straightforward, with no overhead. That means that you always know what backend you are using, and you get exactly the speed of that backend. If it works, you are sure it works at the full speed, with no slow fallback. Theoretically, of course, there is always some overhead of FFI, but in Neanderthal it is so miniscule that you can ignore it for all uses that come to my mind. So, basically, Neanderthal is as fast as ATLAS on CPU and CLBlast on GPU (both offer state-of-the-art speed) or any (not yet existing) pure java engine that I might plug in in the future if necessary. 2. All those other libraries, besides not targeting Clojure at all except for general "you can call Java from Clojure", are trying to be everything for everyone. That has its strengths, because you are, generally, able to accommodate more use cases. On the other hand, it complicates things too much, and can lead to overblown beasts. For example, it might seem good to support MKL, and, ATLAS, and OpenBLAS, and netlib BLAS, and some imaginary fallback solution, like ND4J does (or tries to do), but what's the point of it when today they have more or less the same performance (MKL being a bit faster - in percentages only - but requires $$), and supporting all that stuff makes code AND the installation much, much, more compelex. BLAS is so mature that I think it is better to choose one solution and offer it out of the box. Technically, neanderthal can support all other native blas libraries too, but I intentionally restricted that option because I think fiddling with it does more harm than good. I prefer to give users one Ford model T, than let them choose between 20 different horse carriages. And, if they can even choose the color, provided that their choice is black :) 3. ND4J is, in my opinion, a typical overblown solution. deeplearning4j guys got the investment dollars, and have to rush to the market with business-friendly solution, which usually favors having a checklist of features regardless of whether those features make sense for the little guy. I hope they succeed in the business sense, but the code I'm seeing from them does not seem promising to me regarding Java getting a great DL/NN library. 4. NDArray is actually, as the name suggests, a nd array library, and not a matrix library. Why is this important? Vectors and matrices are something that has been very well researched through decades. The ins and outs of algorithm/architecture fit are known and implemented in BLAS libraries, so you are sure that you get the full performance. N-dimensional vectors (sometime referred as tensors, although that name is not accurate IMO) not so much. So, it is easy that an operation that looks convenient does not lead to a good performance. I do not say it is bad, because if you need that operation, it is better to have something than nothing, but for now I decided to not support 3+ dimensions. This is something that might belong to Neanderthal or on top of it. A long term goal, to be sure. Another aspect of that story is knowledge: most books that I read from the fields of ML/AI give all formulas as vectors of matrices. Basically, matrices are at least 95% (if not more) of potential users need or even understand! 5. BidiMat seems to have much larger scope. For example, at their benchmark page, I see benchmarks for machine learning algorithms, but for nothing matrix-y. The speed comparison; it boils down to this: both Neanderthal and those libraries use (or can be linked to use) the same native BLAS libraries. I took great care to make sure Neanderthal does not incur any copying or calling overhead. From what I saw glancing at the code of other libraries, they didn't. They might support that if you set up everything well among lots of options, or they don't if you do not know how to ensure this. So I doubt any of those could be noticeably faster, and they can be much slower if you slip somewhere. I would also love to see straightforward numbers, but I was unable to find anything like that for those libraries. BidiMat, for example, gives benchmarks of K-Means on MNIST dataset - I do not know how this can be used to discern how fast is it with matrices, other that it is, generally, fast at K-Means. -- You received this message because you are subscribed to the Google Groups "Clojure" group. To post to this group, send email to clojure@googlegroups.com Note that posts from new members are moderated - please be patient with your first post. To unsubscribe from this group, send email to clojure+unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/clojure?hl=en --- You received this message because you are subscribed to the Google Groups "Clojure" group. To unsubscribe from this group and stop receiving emails from it, send an email to clojure+unsubscr...@googlegroups.com. For more options, visit https://groups.google.com/d/optout.
