There are a few parts of Chapel that we haven't touched on.  Here's a quick summary until we're able to provide a more detailed description:

        var dommap : dmap(Block(rank=2)) = new dmap(new Block((1..4,1..4));

        var dom : domain(rank=2) dmapped dommap;

        var dom : domain(rank=2) dmapped Block((1..4, 1..4));

        var counting : domain(string) = { "one", "two", "three", "four" };

        var testarr : [counting] int;

        testarr("four") = 4;

        testarr("five") = 5;     /* or counting.add("five");  counting += "five" */

        dom : domain(rank(2)) = { 1..ncol, 1..nrow }

        var sparsedom : sparse subdomain(dom);

        var sparseimg : [sparsedom] uint(8);

        sparsedom += (1, 1);

        sparseimg(1, 1) = 5;

        sparseimg.IRV = 128;     /* all pixels but (1,1) have this value now */

        class subclass : superclass { .. }

As we have said, the doc, doc/technotes, modules/standards directories, and examples/primers directories in CHPL_HOME contain a diverse collection of design and usage notes.


This brings us to the end of our study of Chapel.  It's time to look back and answer the question posed at the start: Is this a language we can use?

We've seen Chapel in action in pixel-level computations (color conversion and FAST corner detector), at the matrix level (Gabor filter convolution), and at a more general level (k-means clustering and RANSAC feature matching). We've used most, but not all, of the language's features, which we can summarize in a list:

Data Structures

Parallel Programming

Generic Programming

Language Fundamentals


But what has it been like programming with the language?


Writing programs in Chapel has been pleasant.  The language fits this problem domain well.  Domains and slices are natural ways to work with images and clearly signal the intent behind the code.  Having domain changes re-allocate arrays is a strong encouragement to properly set them up.  Parallelism is easy to add to programs, but not entirely worry-free.  You still need to keep in mind what data is crossing task boundaries and if there might be races, and it's not clear how a piece of code will act on actual hardware – the runtime is a bit mysterious.  Generic classes and queried arguments provide the flexibility for re-usable code.  Argument intents make the role of each argument clear, and supporting multiple output arguments is nice.  Interfacing to C is straightforward although a little verbose when needing to use the C types.  The automatic creation and parsing of command-line constants means never having to program a parser again.  Chapel is an expressive language and that is apparent while coding.

We have also found developing in it to be somewhat frustrating.  The flexibility of the language and its default behavior is partly to blame, as is the compiler.  Part of Chapel's expressiveness depends on its defaults, automatically generated constructors, accessors, and iterators, and overloaded operators that uniformly handle different data types.  They reduce clutter in the code and are mostly sensible and intuitive, but we found ourselves constantly butting up against them while compiling and debugging.  The overall impression is that the language is a bit squirrelly as we were constantly reminded of how much was happening behind the scenes, and thus how unsure we were of exactly what was happening.  The flexibility can be confusing.  For example, if out of habit you type array references with brackets instead of parentheses the program will compile, but the behavior might not be what you expect (at least for us, “strange results” cleared up after switching the brackets).  Or, if you make a typo in the name of a constructor you will end up silently using the default, which is not what you want; this happened while developing the circumference class.  The behavior of a program can also be confusing.  We're left with the impression that passing arrays around in structures, particularly records, is not reliable.  The data seems to corrupt eventually, and in the worst case the program fails.  This happened a few times developing the RANSAC program, and its final shape, with only primitives stored in the mapinfo and tryinfo records and the map1to2 and map2to1 arrays being re-generated with the best try, rather than caching them as the program proceeded, is a result.  The compiler did not help.  Error messages are sometimes indirect; if you forget to tell a forall loop that a variable outside the loop is being used, the warning message complains about a bad lvalue.  The compilation stops on the first error, and since the current version is a bit slow the 'fix typo - compile - fix typo - compile' cycle is tedious.  Chapel also only seems to look at code that is being used.  If you test compile a stand-alone library it can come out clean, with errors only appearing when you start calling the library functions from the main program.

Perhaps this is part of the learning process and we find ourselves in something like an uncanny valley of confidence where we're starting to assume we know the language while pushing on it and exposing the gaps in our understanding.  We don't yet have a clear idea of what is happening, and there's little feedback about missed opportunities for optimization or warnings about inefficient code (say, flagging array accesses that may be out of bounds when the compiler can't prove they are within the array's domain).  It's a bit like learning the CUDA model for memory and execution without the profiling tools to show how you can improve your performance: there's a lot of stumbling about and trial-and-error, and any conclusions drawn about best coding practices may be due as much to luck and circumstance as to actual reasons.


You might have noticed that for a High-Performance Computing language we haven't talked much about how fast Chapel programs run, other than to compare different implementations against each other.  There's a few reasons for this. Most importantly the team has been focused on the language definition and only recently started working on optimizing the code the compiler produces.  Their presentations from the end of 2014 acknowledge that performance is poor but is getting better.  We shouldn't be surprised if our runs are slow. Another reason is that we can't compare the programs we've developed here directly with our internal versions, either because we've changed the approach or more often the data type.  As an example we tend to work with integer images, and scale the greyscale plane and the Gabor kernel filter before running the convolution; our implementation here runs in floating point.  There are also things we can't explain, such as  where to best use forall (top level? everywhere? spot placement?).  Finally, there is little feedback about how to optimize programs.  It's a bit of a black box.  Our impression, though, from these programs and others in the language shootout benchmarks is that Chapel is slow compared to C, and some constructs, namely reductions, are borderline unusable.

Maybe a story is in order.  The k-means clustering was our first thought at writing a parallel program in Chapel because we had already gone through the exercise in C.  Our serial version had run too slowly, so the first change we made was to split the image into equal parts, one per thread, converting the next-pass clusters into per-thread sub-totals and combining them at the end.  In other words, it was the approach taken for kmeans_v1. The code edits took an hour and gave us about a 3.5X speed-up with four cores/threads.  We then ported the algorithm over to CUDA, where the biggest changes needed were to fit into the GPU's memory model.  The running time improved by a factor of 6.  The Chapel kmeans programs, in comparison, were 15 times slower than the serial version, or 40% slower if compiled with --fast.  Again, the differences between the C and Chapel versions affect the behavior of the clustering and the results are not directly comparable, but we have tried to have the same amount of work (same image so pixel count is the same, limiting the number of passes to be the same) so the rough result stands: the Chapel program is slower.

[For a better comparison, we've re-worked kmeans_v2.chpl to use only integers.  This version converts each color plane to 8-bit, ie. clrimage -> rgbimage, and changes the types in the cluster record and procedure arguments from real to int.  There are still differences in the algorithm that cause the Chapel version to take two or even three times as many iterations to converge, but the per-iteration time of the integer version is only 20-40% slower than the threaded C version.  The per-iteration time of the float version is three or four times slower than the integer, which gives us the 40% slowdown to the serial C version.]

Work In Progress

Chapel is still in active development.  There are several language features that have not been implemented or will change.  Some, such as strings and data hiding in classes/records, seem fundamental and pose the biggest risk for code re-work in the future.  Others like the order of atomic operations over a distributed network hint at design issues that are outside our experience.  (One reason for not trying locales and domain maps is that we haven't used that kind of hardware.)  Many features are partially implemented and not yet well documented, or have bits and pieces of text describing them scattered about between the modules, docs, and examples directories.  And of course there are bugs.

Any of these mean that there might be conclusions in these examples that are not, or should not, be valid, another example of trial-and-error learning.  Two that come to mind are the decision not to use enumerations as constants or the recommendation not to use arrays-of-arrays as they cannot be used as arguments.  Both apparently should work.  This is not a reason we should reject using Chapel, only something to be kept in mind.  The language will continue to evolve, and we must be prepared to move with it.

One piece we would like to see is CUDA support in the runtime, for both practical reasons and curiosity.  Practical because it's the parallel environment most accessible to us and which has shown significant benefits, but we also wonder about how well the language maps to the GPU and handles the constraints of the hardware, especially the memory model.

Overall Conclusion

Early on, about a quarter of the way through this project, we made a placeholder note in the outline here that an example of a conclusion might be "Performance not there yet, will follow.  Good for prototyping."  That was a prescient remark, for it is our conclusion.  Chapel is a comfortable language for programming for us and we expect it will get better with each half-yearly release. Its lineage is clear, with roots in the world of C and Unix, and this is the environment we use.  Should we need to port a prototype back into that world, say the kd-tree class or the RANSAC algorithm, it seems straightforward enough to do.  But first we need to determine if RANSAC can be tweaked and its accuracy with scaled images improved.  Onward to the next project ...




And that brings us to the end of the tutorials.  If you have comments or feedback, they would be very much appreciated.  We can best be reached by e-mail at chapel_by_ex@primordand.com.  Thank you for your time and attention.