a blast from the past

While reading some EE articles, I happened across a link to a Webring site. Remember Webrings? Probably not. They were the rage back in the 90’s. Not so popular anymore, as all content gets swallowed up into the Facebook/Tumblr/Google ecosystems.

The nostalgia hit of clicking through the various pages in the ring was quite impressive. All of those pages “made with notepad”. The blink tags, marquees and counters, all used without irony. Ahh, those were the days.

For the curious, start here.

Citibike: Bike Flow

I’m a big fan of the Citibike bike share program that started here recently.  One common issue I and others seem to suffer from is the lack of bikes (when starting a trip) or docks (when ending a trip).  Our neighborhood tends to be a very popular destination in the evening, so when I try to ride a bike in, I often end up a few blocks away from my desired station.  Similarly, if I get a late start in the morning I often find there are no bikes left to ride.

I was curious about how the flow of bikes works around the city — where do the bikes go to when they leave the East Village?  I crawled the Citibike web site and created a simple website to visualize the flow of bikes around the city; the results are pretty interesting:


With some more work on the data, it might be possible to use it for predictions (“will I be able to return this bike to my station”) and to aid in balancing (choosing which stations to move bikes between, and at what time).

The source code for the application is available on Github.

Making a JIT interpreter with LuaJIT

(N.B. The code for all of these experiments is on Github).

I recently read this post by François Perrad regarding interpreters, where he compared interpreter loops written in Lua, LuaJit and Pypy.  (I think the original toy interpreter example comes from PyPy).   After some suggestions, he ended up with a new PyPy version which performed very well — close to what you’d see from a static compiler.

The bytecode ‘program’ being used for all of these examples is simply calculating, in a round-about fashion, the square of an input number:

MOV_A_R,    0,
MOV_A_R,    1,
MOV_R_A,    0, 
MOV_A_R,    0,
MOV_R_A,    2, 
ADD_R_TO_A, 1,
MOV_A_R,    2,
MOV_R_A,    0, 
JUMP_IF_A,  4,
MOV_R_A,    2,

I made a slight modification to this interpreter to force PyPy to load the bytecode at runtime (to ensure it doesn’t “cheat” during translation by just statically optimizing for this particular program).    This version runs quickly, but not as fast as the version that has the bytecode baked in.  It evaluates 100M iterations of the bytecode loop in 1.6seconds; still this is roughly a hundred times faster then the CPython equivalent.  This is what you’d expect, after all — it’s what PyPy is designed for.

The lua based interpreter, when run with Luajit, takes 5.5 seconds; not bad, but it’s 4 times slower then PyPy. Can we do better?  What’s causing Luajit to run slowly?  If we turn on jit debugging for luajit, we see the problem immediately:

luajit -jdump toy-jit.lua 100000000

There’s no output!  The JIT compiler never activated.  What’s going on?

It turns out that our interpreter loop (like all interpreter loops), is unpredictable, as the path of the execution is very data dependent (‘data’ here meaning the bytecode we’re interpreting):

while true do
  local opcode = bytecode[pc]
  pc = pc + 1 
  if opcode == JUMP_IF_A then
    local target = bytecode[pc]
    pc = pc + 1 
    if a ~= 0 then
      pc = target
  elseif opcode == MOV_A_R then
  elseif opcode == MOV_R_A then
  elseif opcode == ADD_R_TO_A then
  elseif opcode == DECR_A then
  elseif opcode == RETURN_A then 

After executing a bytecode, the interpreter goes back up to the top of the while, and jumps to a different place. A tracing JIT never gets a chance to see the pattern, and so you end up running in the interpreter the whole time.  PyPy solves this problem by using magic meta-tracing.

It turns out we can get a similar effect in Luajit, without too much effort, using partial evaluation.  That is, given a chunk of bytecode, we’ll generate a specialized version of our interpreter for that bytecode.  We do this, in time-honored fashion, by copy-pasting. We step through each opcode, and instead of evaluating it, we build up a Lua string to evaluate it (A much cleaner approach would be to write our interpreter in some structured fashion, and generate the JIT interpreter from that):

if opcode == JUMP_IF_A then
      local target = bytecode[pc]
      pc = pc + 1
      f_str = f_str .. string.format([[
if a == 0 then
  goto op_%d
goto op_%d
]], pc, target)
    elseif opcode == MOV_R_A then
      local n = bytecode[pc]
      pc = pc + 1
    f_str = f_str .. string.format([[
a = reg_%d
]], n)

For our test program, this creates a Lua string like this:

function _jit(a)
  local reg = {0, 0, 0, 0, 0, 0, 0, 0}
reg[1] = a
reg[2] = a
a = reg[1]
a = a - 1
reg[1] = a
a = reg[3]
a = a + reg[2]
reg[3] = a
a = reg[1]
if a == 0 then
  goto op_20
goto op_5
a = reg[3]
return a

If we


this string, we get back an interpreter that’s been specialized for just this bytecode. What does our performance look like now?

time pypy-jit-c /home/power/tmp/bytecode.str 100000000
1.63s user 0.01s system 99% cpu 1.649 total

time luajit toy.lua 100000000       
5.51s user 0.01s system 99% cpu 5.549 total

time luajit toy-jit.lua 100000000
0.12s user 0.00s system 97% cpu 0.128 total

We’re now much faster then PyPy! Obviously this trick is easier to play with such a simple interpreter (we’re also using the native numeric type of our JIT, which isn’t always correct). Amore complex, dynamically typed systems might prove to be more difficult to do partial evaluation on. There also could be extra hints I could give to PyPy to make it work better (if you have any ideas, please tell me!).

Still, it’s somewhat surprising how easy it was to generate our ‘JIT’ interpreter — the code isn’t much bigger then the original version. Perhaps with some more scaffolding/helper libraries, this could be a viable way to create fast interpreters for new languages?

scidb performance

We searched around trying to find any reasonable comparison of Scidb performance to existing systems (specifically, we’re looking at doing straightforward bulk-parallel operations like logistic regression/k-means).  So far, we’ve found the performance is very poor — an order of magnitude or 2 worse then single machine runs or systems like Spark.

Any ideas what we’re doing wrong? The code is below. We’re running this across 4 machines with 32GB of memory each. The example code is just trying to do the regression on 100000 samples with 10 dimensions.


insert(multiply(X, W))

query itself seems to take several seconds. What’s going on here? On a single machine, this operation is less then a millisecond; even accounting for disk reads/network issues I’d expect this to be a hundred times faster then it is.

Trying to run with more data causes the system to run out of memory.


function create_randoms(){
  x=$(($1 - 1))
  y=$(($2 - 1))
  echo "Creating random array $3[$1, $2]..."
  iquery -anq "store(build([x=0:$x,$CHUNK_SIZE,0,
y=0:$y,$CHUNK_SIZE,0], double(1)/(random() % 10 + 1)), $3)" >/dev/null

function create_zeros(){
  x=$(($1 - 1))
  y=$(($2 - 1))
  echo "Creating zero array $3[$1, $2]..."
  iquery -anq "store(build([x=0:$x,$CHUNK_SIZE,0,y=0:$y,$CHUNK_SIZE,0],0),$3)" >/dev/null

function create_template(){
  x=$(($1 - 1))
  iquery -nq "create array Template [x=0:$x,$CHUNK_SIZE,0]" >/dev/null

function exe(){
  iquery -o csv+ -q  "$1"

function exe_silent(){
  iquery -nq "$1" >/dev/null

function clean(){
  exe_silent "drop array W"
  exe_silent "drop array X"
  exe_silent "drop array Y"
  exe_silent "drop array Pred"
  exe_silent "drop array Diff"
  exe_silent "drop array Grad"
  exe_silent "drop array Temp"
  exe_silent "drop array Template"

function init(){
  create_randoms $N $D X
  create_randoms $N 1 Y
  create_randoms $D 1 W
  create_zeros $N 1 Pred
  create_zeros $D 1 Grad
  create_zeros $N 1 Diff
  create_template $N

D=10 #Dimensions.
N=100000 #Points.

init #Create arrays.

for i in {1..5}
iquery <<HERE
set no fetch;

set lang afl;
insert(multiply(X, W), Pred);

set lang aql;
update Pred set val= double(1)/(double(1) + exp(-val));
select Pred.val - Y.val into Diff from Pred, Y;
select sum(new_val) as val into Temp from (select X.val * R.val as
new_val from X join reshape(Diff, Template) as R on X.x = R.x) group
by y;
select val into Grad from reshape(substitute(Temp, build(Temp, 0)), Grad);

set fetch;
select W.val + 0.001 * Grad.val into W from W, Grad;


a simple watchdog for subprocesses

A problem I often encounter when writing code that deals with subprocesses is the issue of orphans. These are perfectly useful in many situations, such as keeping program running via disown. They’re also real annoyances when you’re trying to fork worker processes; every time your parent process has a bug, you end up with orphaned workers that you have to manually terminate.

For instance, I might want to launch a bunch of worker processes, each listening on a separate port:

for i in range(num_procs):
  subprocess.Popen('python worker.py %d' % 9999 + i)

The orphan problem crops up if the parent dies without terminating the worker process (gracefully or otherwise). After the parent process is killed, the workers are left running in the background; holding onto both resources and their port. We now have to manually terminate them in order to try running again:

pkill -9 -f worker.py

You can use the atexit functionality from most languages to try to cleanup, but this doesn’t help if your process segfaults, or has some other ignominious end.

In a “real” environment, this is typically solved by having a cluster manager which schedules jobs and can start and kill processes as needed (for example SLURM or Torque. But often we’re running in an environment where this is unavailable (either our local machine, or we just haven’t bothered to set something up). What do we do?

The solution here is watchdog timer which terminates the workers if the master process dies. This can be accomplished in a number of ways, including via the RPC system, but I’ve found a simple mechanism that works well for me: polling the stdin channel.

class FileWatchdog(threading.Thread):
  """Watchdog for a file (typically sys.stdin).
  When the file closes, terminate the process.
  (This typically occurs when the parent process is lost.)
  def __init__(self, file_handle):
    threading.Thread.__init__(self, name='WatchdogThread')
    self.file_handle = file_handle
    # self.log = open('/tmp/watchdog.%d' % os.getpid(), 'w')
  def run(self):
    f = [self.file_handle]
    while 1:
      r, w, x = select.select(f, f, f, 1.0)
      # print >>self.log, 'Watchdog running: %s %s %s' % (r,w,x)
      # self.log.flush()
      if w:
        # print >>self.log, 'Watchdog: file closed.  Shutting down.'
        # self.log.flush()

I just have my worker processes spawn a watchdog thread at startup time, and they will terminate themselves as soon as they lose their connection to the master process. This simple mechanism falls apart if you’re using stdin to communicate to your child processes, but it works well for most other simple needs.

thread profiling in Python

Python has accumulated a lot of… character over the years.  We’ve got no less then 3 profiling libraries for single threaded execution and a multi-threaded profiler with an incompatible interface (Yappi).  Since many applications use more then one thread, this can be a bit annoying.

Yappi works most of the time.  Except it can sometimes cause your application to hang for unknown reasons (I blame signals, personally). The other issue is that Yappi doesn’t have a way of collecting call-stack information. (I don’t necessarily care that memcpy takes all of the time, I want to know who called memcpy). In particular, the lovely gprof2dot can take in pstats dumps and output a very nice profile graph.

To address this for my uses, I glom together cProfile runs from multiple threads. In case it might be useful for other people I wrote a quick gist illustrating how to do it. To make it easy to drop in, I monkey-patch the Thread.run method, but you can use a more maintainable approach if you like (I create a subclass ProfileThread in my applications).

from threading import Thread
import cProfile
import pstats
def enable_thread_profiling():
  '''Monkey-patch Thread.run to enable global profiling.
Each thread creates a local profiler; statistics are pooled
to the global stats object on run completion.'''
  Thread.stats = None
  thread_run = Thread.run
  def profile_run(self):
    self._prof = cProfile.Profile()
    if Thread.stats is None:
      Thread.stats = pstats.Stats(self._prof)
  Thread.run = profile_run
def get_thread_stats():
  stats = getattr(Thread, 'stats', None)
  if stats is None:
    raise ValueError, 'Thread profiling was not enabled,'
                      'or no threads finished running.'
  return stats
if __name__ == '__main__':
  import time
  t = Thread(target=time.sleep, args=(1,))

Swig+Directors = Subclassing from Python!

Swig is a fabulous tool — I generally rely on it to extricate myself from the holes I’ve managed to dig myself into using C++.  Swig parses C++ code and generates wrappers for a whole bunch of target languages — I normally use it to build Python interfaces to my C++ code.

A cool feature that I’ve never made use of before is “directors” — these let you write subclasses for your C++ code in Python/(whatever language use desire).  In particular, this provides a relatively easy mechanism for writing callbacks using Python.  Here’s a quick example:

// rpc.h
class RPCHandler {
void fire(const Request&, Response*) = 0;

class RPC {
void register_handler(const std::string& name, RPCHandler*);

Normally, I’d make a subclass of RPCHandler in C++ and register it with my RPC server. But with SWIG, I can actually write this using Python:

class MyHandler(wrap.RPCHandler):
  def fire(req, resp):
    resp.write('Hello world!')

It’s relatively straightforward to setup. I write an interface file describing my application:

// wrap.swig
// Our output module will be called 'wrap'; enable director support.
%module(directors="1") wrap
%feature("director") RPCHandler;

// Generate wrappers for our RPC code
%include "rpc.h"

// When compiling the wrapper code, include our original header.
#include "rpc.h"

That’s it! Now we can run swig:

 swig -c++ -python -O -o wrap.cc wrap.swig 

Swig will generate wrap.cc (which we compile and link into our application), and a wrap.py file, which we can use from Python.

ah, latex

I thought I wanted to customize the layout of my document a little bit, so I started looking at the various styles that are available.

Then I came upon the memoir package (which is supposed to help with these things); the 550 page manual that comes with it has so effectively scared the crap out of me that I’m now looking at my crappy looking document and thinking: “heck, it looks good enough”.

I will gladly leave typography to the typographers.


I’m a frequent (some might say avid) user of the Vim text editor. I’ve been using it off and on for the past 15 years, and it’s frequently saved me quite a bit of time with the handy macro system.

Now, if you’ve ever used Vim before you’re probably familiar with this intro screen:


Somehow, amazingly, and I’m sure like everyone else, I had managed to go on for all this time without really ever typing

:help uganda

. Last night, while installing Vim on my new laptop, I finally did. I saw Bram’s visit report. I was really touched at how much they were doing with relatively limited resources. And so I finally made a small donation to ICCF; I even got a personal email from Bram in reply, which was nice.

Anyway, if you’re a Vim user, I encourage you to do the same.

kde desktop magic

Something miraculous that I just realized: middle clicking (aka pasting the X buffer) on the KDE desktop creates a “post-it” note with the contents of the clipboard.

Whether I will ever make use of this feature is unclear but it’s definitely the “right” behavior and a nice thought on the developers part.