After a considerable gap, a gave Go another go!
As part of a consulting engagement, I accepted a project to develop some statistical inference models in the area of drug (medicine) repositioning. Input data comprises three sets of associations: (i) between drugs and adverse effects, (ii) between drugs and diseases, and (iii) between drugs and targets (proteins). Using drugs as hub elements, associations are inferred between the other three kinds of elements, pair-wise.
The actual statistics computed vary from simple measures such as sensitivity (e.g. how sensitive is a given drug to a set of query targets?) and clustering coefficients of the multi-mode graph, to construction of rather complex confusion matrices, computation of measures such as Matthews Correlation Coefficient, to construction of generalised profile vectors for drugs, diseases, etc. Accordingly, the computational intensity varies considerably across parts of the models.
For the size of the test subset of input data, the in-memory graph of direct and transitive associations currently has about 15,000 vertices and over 14,000,000 edges. This is expected to grow by two orders of magnitude (or more) when the full data set is used for input.
I had some temptation initially to prototype the first model (or two) in a language like Ruby. Giving the volume of data its due weight though, I decided to use Ruby for ad hoc validation of parts of the computations, with coding proper happening in a faster, compiled language. I have been using Java for most of my work (both open source as well as for clients). However, considering the fact that statistics instances are write-only, I hoped that Go could help me make the computations parallel easily.
My choice of Go caused some discomfort on the part of my client's programmers, since they have to maintain the code down the road. No serious objections were raised nevertheless. So, I went ahead and developed the first three models in Go.
Practical Issues With Go
The Internet is abuzz with success stories involving Go; there isn't an additional perspective that I can add! The following are factors, in no particular order, that inhibited my productivity as I worked on the project.
Set in the Language
Through (almost) every hour of this project, I found myself needing an efficient implementation of a set data structure. Go does not have a built-in set; it has arrays, slices and maps (hash tables). And, Go lacks generics. Consequently, whichever generic data structure is not provided by the compiler can not be implemented in a library. I ended up using maps as sets. Everyone who does that realises the pain involved, sooner than later. Maps provide uniqueness of keys, but I needed sets for their set-like properties: being able to do minus, union, intersection, etc. I had to code those in-line every time. I have seen several people argue vehemently (even arrogantly) in
golang-nuts that it costs just a few lines each time, and that it makes the code clearer. Nothing could be further from truth. In-lining those operations has only reduced readability and obscured my intent. I had to consciously train my eyes to recognise those blocks to mean union, intersection, etc. They also were very inconvenient when trying different sequences of computations for better efficiency, since a quick glance never sufficed!
Also, I found the performance of Go maps wanting. Profiling showed that get operations were consuming a good percentage of the total running time. Of course, several of those get operations are actually to check for the presence of a key.
BitSet in the Standard Library
Since the performance of maps was dragging the computations back, I investigated the possibility of changing the algorithms to work with bit sets. However, there is no
BitArray in Go's standard library. I found two packages in the community: one on
code.google.com and the other on
github.com. I selected the former both because it performed better and provided a convenient iteration through only the bits set to true. Mind you, the data is mostly sparse, and hence both these were desirable characteristics.
Incidentally, both the bit set packages have varying performance. I could not determine the sources of those variations, since I could not easily construct test data to reproduce them on a small scale. A well-tested, high performance bit set in the standard library would have helped greatly.
Generics, or Their Absence
The general attitude in Go community towards generics seems to have degenerated into one consisting of a mix of disgust and condescension, unfortunately. Well-made cases that illustrate problems best served by generics, are being dismissed with such impudence and temerity as to cause repulsion. That Russ Cox' original formulation of the now-famous tri-lemma is incomplete at best has not sunk in despite four years of discussions. Enough said!
In my particular case, I have six sets of computations that differ in:
- types of input data elements held in the containers, and upon which the computations are performed (a unique combination of three types for each pair, to be precise),
- user-specified values for various algorithmic parameters for a given combination of element types,
- minor computational steps and
- types (and instances) of containers into which the results aggregate.
These differences meant that I could not write common template code that could be used to generate six versions using extra-language tools (as inconvenient as that already is). The amount of boiler-plate needed externally to handle the differences very quickly became both too much and too confusing. Eventually, I resorted to six fully-specialised versions each of data holders, algorithms and results containers, just for manageability of the code.
This had an undesirable side effect, though: now, each change to any of the core containers or computations had to be manually propagated to all the corresponding remaining versions. It soon led to a disinclination on my part to quickly iterate through alternative model formulations, since the overhead of trying new formulations was non-trivial.
This was simply unexpected! With fully-specialised versions of graph nodes, edges, computations and results containers, I was expecting very good performance. Initially, it was not very good. In single-threaded mode, a complete run of three models on the test set of data took about 9 minutes 25 seconds. I re-examined various computations. I eliminated redundant checks in some paths, combined two passes into one at the expense of more memory, pre-identified query sets so that the full sets need not be iterated over, etc. At the end of all that, in single-threaded mode, a complete run of three models on the test set of data took about 2 minutes 40 seconds. For a while, I thought that I had squeezed it to the maximum extent. And so thought my client, too! More on that later.
At that point, my client requested for three enhancements, two of which affected all the six + six versions of the models. I ploughed through the first change and propagated it through the other eleven specialised versions. I had a full taste of what was to come, though, when I was hit with the realisation that I was yet working on Phase 1 of the project, which had seven proposed phases in all!
Back to Java!
I took a break of one full day, and did a hard review of the code (and my situation, of course). I quickly identified three major areas where generics and (inheritance-based) polymorphism would have presented a much more pleasant solution. I had already spent 11 weeks on the project, the bulk of that going into developing and evaluating the statistical models. With the models now ready, I estimated that a re-write in Java would cost me about 10 working days. I decided to take the plunge.
The full re-write in Java took 8 working days. The ease with which I could model the generic data containers and results containers was quite expected. Java's
BitSet class was of tremendous help. I had some trepidation about the algorithmic parts. However, they turned out to be easier than I anticipated! I made the computations themselves parts of formally-typed abstract classes, with the concrete parts such as substitution of actual types, the user-specified parameters and minor variations implemented by the subclasses. Conceptually, it was clear and clean: the base computations were easy to follow in the abstract classes. The overrides were clearly marked so, and were quite pointed.
Naturally, I expected a reduction in the size of the code base; I was not sure by how much, though. The actual reduction was by about 40%. This was nice, since it came with the benefit of more manageable code.
The most unexpected outcome concerned performance: a complete run of the three models on the test set of data now took about 30 seconds! My first suspicion was that something went so wrong as to cause a premature (but legal) exit somewhere. However, the output matched what was produced by the Go version (thanks Ruby), so that could not have been true. I re-ran the program several times, since it sounded too good to be true. Each time, the run completed in about 30 seconds.
I was left scratching my head. My puzzlement continued for a while, before I noticed something: the CPU utilisation reported by
/usr/bin/time was around 370-380%! I was now totally stumped.
conky showed that all processor cores were indeed being used. How could that be? The program was very much single-threaded.
After some thought and Googling, I saw a few factors that potentially enabled a utilisation of multiple cores.
- All the input data classes were
- All the results classes were
final, with all of their members being
- All algorithm subclasses were
- All data containers (masters), the multi-mode graph itself, and all results containers had only insert and look-up operations performed on them. None had a delete operation.
Effectively, almost all of the code involved only
final classes. And, all operations were append-only. The compiler may have noticed those; the run-time must have noticed those. I still do not know what is going on inside the JRE as the program runs, but I am truly amazed by its capabilities! Needless to say, I am quite happy with the outcome, too!
Update: As several responses (both here and on Hacker News) stated, Java's multi-threaded GC appears to be primary reason for the utilisation of all the processor cores.
- If your problem domain involves patterns that benefit from type parameterisation or polymorphism that is easily achievable through inheritance, Go is a poor choice.
- If you find your Go code evolving into having few interfaces but many higher-order functions (or methods) that resort to frequent type assertions, Go is a poor choice.
- Go runtime can learn a trick or two from JRE 7 as regards performance.
These may seem obvious to more informed people; but to me, it was some enlightenment!
 I tried Haskell and Elixir as candidates, but nested data holders with multiply circular references appear to be problematic to deal with in functional languages. Immutable data presents interesting challenges when it comes to cyclic graphs! The solutions suggested by the respective communities involved considerable boiler-plate. More importantly, the resulting code lost direct correspondence with the problem's structural elements. Eventually, I abandoned that approach.↩
 Not an exclusive or.↩