Here is the latest OCaml Weekly News, for the week of June 13 to 20, 2017.
Archive: https://sympa.inria.fr/sympa/arc/caml-list/2017-06/msg00056.htmlEvgeny Roubinchtein asked and Gabriel Scherer replied:
> Is there a suite of micro-benchmarks for floating-point operations in > OCaml? If so, could someone point me to such suite(s)? While they are not "micro-benchmarks" in the sense of "testing only one operation", the set of short-running benchmarks in operf-micro contains many short floating-point-heavy programs (almabench, fft, kahan_sum...): https://github.com/OCamlPro/operf-micro/tree/master/share/operf-micro/benchmarks https://www.typerex.org/operf-micro.html
Archive: https://sympa.inria.fr/sympa/arc/caml-list/2017-06/msg00062.htmlFrancois BERENGER asked:
I am wondering if some high performance OCaml experts out there can know in advance if some code can go faster by executing it on a GPU. I'd like to get some feeling before I invest into some new technology. I'd like to stay in OCaml, not drop down to C. So I mention SPOC, SAREK and MetaOCaml which look like the right technologies. I have some clear bottleneck in my program. Here is how the code looks like: --- let f (points: (Vector3.t * float) list) = let acc = ref  in let ac p1 x (p2, y) = acc := (Vector3.dist p1 p2, x *. y) :: !acc in let rec loop = function |  -> () | (p1, x) :: xs -> L.iter (ac p1 x) xs; loop xs in loop points; !acc --- So, in terms of size: each point is 3 floats (a 3D coordinate) plus 1 float (some value). I usually have several thousands of points, but less than 10_000. The f function will be called thousands of times for one run of the program (let's say 300k calls is a big but reasonable use case). I don't care about the order of points in the input list. I also don't care about the order of the results in acc. I very probably don't care about using single point precision (32 bit floats) for everything, instead of double precision. Thanks a lot for any feedback. Best regards, Francois. PS: vector3 is available in opam, if that helpsRonan Le Hy replied:
As a baseline before attempting anything on the GPU, I'd vectorize this. Put all your vectors in a big matrix. Put all your numbers in a vector. Compute the distances and the products all at once using Lacaml (make sure you use OpenBLAS as a backend). I'd expect it to be much faster than the above loop already.Ivan Gotovchits also replied:
Your code is actually bound by memory, not by computation. The problem is that you create lots of data, and all the time is spent in the allocation functions and GC. The actual computation consumes less than 5% of overall program execution, that can be easily seen by profiling (always profile before starting optimization). Thus moving to GPU will make this 5% of code run faster, at the expense of even higher overhead of moving data between CPU and GPU. So, let's see how we can optimize your code. First of all, your code uses too much mutable state, that can have a negative performance impact in OCaml due to write barriers. Let's make it a little bit cleaner and faster: let nonmutab points = let rec loop xs acc = match xs with |  -> acc | (x,w) :: xs -> List.fold_left (fun acc (x',w') -> (Vector3.dist x x', w *. w') :: acc) acc xs |> loop xs in loop points  This code runs a little bit faster (on 10000 points): original: 14084.1 ms nonmutab: 11309.2 ms It computes absolutely the same result, using the same computational core, and allocating the same amount of trash. The only difference is that we are not using a mutable state anymore, and we are rewarded for that. The next step is to consider, do you really need to produce these intermediate structures, if the result of your program is the computed data, then you can just store it in a file. Or, if you need later this data to be reduced to some value, then you shouldn't create an intermediate result, and apply the reduction in place (so called de-foresting). So, let's generalize a little bit the function so that instead of producing a new list, the function just applies a user-provided function to all produced elements, along with an accumulator. let nonalloc f acc points = let rec loop xs acc = match xs with |  -> acc | (x,w) :: xs -> List.fold_left (fun acc (x',w') -> f acc (Vector3.dist x x') (w *. w')) acc xs |> loop xs in loop points acc This function will not allocate any unnecessary data (it will though allocate a pair of floats per each call). So we can use this function to implement the `nonmutab` version, by passing a consing operator and an empty list. We can also try to use it to store data. The data storage process depends on how fast we can reformat the data, and how fast is the sink device. If we will output to /dev/null (that is known to be fast), then we can use the Marshal module and get about 300 MB/s throughtput. Not bad, but still about 10 seconds of running time. If, for example, we just need some scalar metrics, then we don't need to pay an overhead of data, and it will be as fast as possible. For example, with the following implementations of the kernel function let flags = [Marshal.No_sharing] let print () x1 x2 = Marshal.to_channel stdout x1 flags; Marshal.to_channel stdout x2 flags let product total x1 x2 = total +. x1 *. x2 We will have the following timings: printall: 9649.54 ms nonalloc: 541.186 ms So, the actual computation took only half a second, the rest is data processing. Please find attached the whole example. You can run it with the following command: ocamlbuild -pkgs vector3,unix points.native -- > /dev/null
Here are some links to messages at http://discuss.ocaml.org that may be of interest to the readers. - Dave Scott talks about "[ANN]: io-page 2.0.0, with packaging changes" https://discuss.ocaml.org/t/ann-io-page-2-0-0-with-packaging-changes/440/1 - Reynir Björnsson talks about "Ocp-index-top v0.4.1 - first release on opam" https://discuss.ocaml.org/t/ocp-index-top-v0-4-1-first-release-on-opam/442/1
Here is a sneak peek at some potential future features of the Ocaml compiler, discussed by their implementers in these Github Pull Requests. - Unsigned 32-bit and 64-bit integers https://github.com/ocaml/ocaml/pull/1201
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