Is khmer evolving?
The khmer project is our software package to work with short reads, and it enables a lot of things like k-mer counting and de Bruijn graph exploration and modification. As data volume grows, interest in partitioning and digital normalization is also growing. But we haven't really talked much about new features in khmer.
So, you might wonder, is the project stagnant? The answer: No.
As more and more people start to use khmer, we are faced with the question of whether or not to improve it incrementally, or to shoot for the moon. These are not entirely orthogonal, but my lab doesn't have the humanpower to help people use khmer, AND fix rough corners, AND optimize it, AND do research. While we've been trying to balance these needs, we've mostly been focusing on our research, 'cause that's where our incentives are.
But while our research is, indeed, going to be awesome, it doesn't really help users in the short term.
However, our software engineer, Eric, has been bravely bucking this trend and working to provide the next set of really cool optimizations. His latest set of nifty refactoring just hit the bleeding edge branch and it is truly awesome to behold.
Briefly, Eric has implemented multithreaded read parsing in C++, and provided a Python iterator interface to it. This is further along the path of some of his earlier work to make our core data structures threadsafe, and improve read parsing performance dramatically (described in some detail in our chapter on Working with Big Data in Bioinformatics). The big thing for me, to reiterate, is that we now have a Python API to access multithreaded read parsing! This makes it quite generally useful, as well as flexible and usable for prototyping new khmer code.
How does it work?
I put together a script to try it out; you can see the entire functioning script as a gist on github if you're interested in trying it out yourself.
How does it work? I would argue "as you would expect" :).
Let's suppose you are dealing with a large read data set, and you want to do some mildly expensive computation on each read. Below, I wrote a simple function to simulate a mildly expensive operation by sleeping for 2 tenths of a second for every 1000 reads, while adding the read names into a set:
s = set() def read_names(rparser): for n, read in enumerate(rparser): s.add(read.name) if n % 1000 == 0: print 'sleeping', n time.sleep(0.2)
Now, rather than running this function in a normal serial way, you note that hey, perhaps you could speed things up by running it in parallel! How would you do that?
First, instantiate a ReadParser:
rparser = khmer.ReadParser(filename, n_threads)
and then ... you're done. Simply start up that many Python threads, and use 'read_names' as the target of a threading.Thread.
print 'starting threads' threads =  # start n_threads threads for tnum in xrange(n_threads): t = threading.Thread(target=read_names, args=(rparser,)) threads.append(t) t.start()
Then, wait for the threads to exit.
# wait until all threads have exited for t in threads: t.join() print 'done; loaded %s sequences' % len(s)
A not-so-scientific trial shows the basic speedup -- with 1 thread, 'time' reports
real 0m5.191s <-- 5.2 seconds of walltime user 0m0.138s sys 0m0.015s
with 2 threads,
real 0m2.789s <-- 2.8 seconds of wall time user 0m0.178s sys 0m0.081s
with 4 threads,
real 0m1.794s <-- 1.8 seconds of walltime user 0m1.234s sys 0m0.115s
and with 8 threads:
real 0m1.418s <-- 1.4 seconds of walltime user 0m2.453s sys 0m0.074s
(Since this is a fairly small set of sequences, and there is overhead to setting up the threading, the times are not going to be linear with the number of threads used. But for larger files and real processing, Eric's shown that we can get nearly linear speedups of 0.9x for each thread added. You can read the book chapter if you want more performance and design details.)
Also note that Eric has also added paired-end reading, which makes sure that each thread gets both ends.
Overall, I think this is really exciting. Eric's put in a lot of work to make the code threadsafe and to refactor the read parsing code to have a consistent set of APIs, and it's bearing fruit. While there is more work to be done, it's nearing the point where it's going to simply be the bedrock on which we can drive future optimizations and scaling.
To get the latest bleeding edge, do
git clone https://github.com/ged-lab/khmer.git -b bleeding-edge
Whither the future?
Factoring out this multithreaded read stuff into its own set of Python- accessible APIs does make it immediately useful to other programmers. I'm not sure how far we want to go down the path of becoming a general library for things like read parsing in Python and C++, but it should be possible to swipe the code (BSD license!) for those who are interested in doing so. We could probably put it into screed if we wanted... hmmmm....
Also, I should point out that most of the features in khmer master so far are completely serial -- the only exception is some aspects of partitioning, 'cause that's so slow. That means that people using diginorm on 3 billion reads are doing so in a single thread so far! Since the diginorm algorithm is single pass, they don't yell at me too much, but it does take a fair amount of time. But, good news -- those people should see significant improvements in speed with the next version of khmer...
As for the broader future of khmer, it's on hold until we receive good (or bad) news about funding. I've got a bunch of students looking to graduate, a bunch of collaborators who want to finish off their analyses, and not much money to pursue things like good software engineering. Hopefully that will change, and if or when it does, I'll blog about my thoughts on the future of khmer.