Onis Nazareth

1. IO Count IO count is the easiest requirement to start with
2. Number of Gates There are different FPGA categories in the market and within each category there are different FPGA sizes (in terms of gates or resources)
3. Operating frequency
4. Operating Temperature
5. Cost
6. Vendors
7. Conclusions

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Unless the function you're aiming for does something very special to mark "one instance of me is active on the stack" (IOW: if the function is pristine and untouchable and can't possibly be made aware of this peculiar need of yours), there is no conceivable alternative to walking frame by frame up the stack until you hit either the top (and the function is not there) or a stack frame for your function of interest. As several comments to the question indicate, it's extremely doubtful whether it's worth striving to optimize this. But, assuming for the sake of argument that it was worthwhile...:

Edit: the original answer (by the OP) had many defects, but some have since been fixed, so I'm editing to reflect the current situation and why certain aspects are important.

First of all, it's crucial to use try/except, or with, in the decorator, so that ANY exit from a function being monitored is properly accounted for, not just normal ones (as the original version of the OP's own answer did).

Second, every decorator should ensure it keeps the decorated function's __name__ and __doc__ intact -- that's what functools.wraps is for (there are other ways, but wraps makes it simplest).

Third, just as crucial as the first point, a set, which was the data structure originally chosen by the OP, is the wrong choice: a function can be on the stack several times (direct or indirect recursion). We clearly need a "multi-set" (also known as "bag"), a set-like structure which keeps track of "how many times" each item is present. In Python, the natural implementation of a multiset is as a dict mapping keys to counts, which in turn is most handily implemented as a collections.defaultdict(int).

Fourth, a general approach should be threadsafe (when that can be accomplished easily, at least;-). Fortunately, threading.local makes it trivial, when applicable -- and here, it should surely be (each stack having its own separate thread of calls).

Fifth, an interesting issue that has been broached in some comments (noticing how badly the offered decorators in some answers play with other decorators: the monitoring decorator appears to have to be the LAST (outermost) one, otherwise the checking breaks. This comes from the natural but unfortunate choice of using the function object itself as the key into the monitoring dict.

I propose to solve this by a different choice of key: make the decorator take a (string, say) identifier argument that must be unique (in each given thread) and use the identifier as the key into the monitoring dict. The code checking the stack must of course be aware of the identifier and use it as well.

At decorating time, the decorator can check for the uniqueness property (by using a separate set). The identifier may be left to default to the function name (so it's only explicitly required to keep the flexibility of monitoring homonymous functions in the same namespace); the uniqueness property may be explicitly renounced when several monitored functions are to be considered "the same" for monitoring purposes (this may be the case if a given def statement is meant to be executed multiple times in slightly different contexts to make several function objects that the programmers wants to consider "the same function" for monitoring purposes). Finally, it should be possible to optionally revert to the "function object as identifier" for those rare cases in which further decoration is KNOWN to be impossible (since in those cases it may be the handiest way to guarantee uniqueness).

So, putting these many considerations together, we could have (including a threadlocal_var utility function that will probably already be in a toolbox module of course;-) something like the following...:

import collections"import functools"import threading""threadlocal = threading.local()""def threadlocal_var(varname, factory, *a, **k):"  v = getattr(threadlocal, varname, None)"  if v is None:"    v = factory(*a, **k)"    setattr(threadlocal, varname, v)"  return v""def monitoring(identifier=None, unique=True, use_function=False):"  def inner(f):"    assert (not use_function) or (identifier is None)"    if identifier is None:"      if use_function:"        identifier = f"      else:"        identifier = f.__name__"    if unique:"      monitored = threadlocal_var('uniques', set)"      if identifier in monitored:"        raise ValueError('Duplicate monitoring identifier %r' % identifier)"      monitored.add(identifier)"    counts = threadlocal_var('counts', collections.defaultdict, int)"    @functools.wraps(f)"    def wrapper(*a, **k):"      counts[identifier] += 1"      try:"        return f(*a, **k)"      finally:"        counts[identifier] -= 1"    return wrapper"  return inner

I have not tested this code, so it might contain some typo or the like, but I'm offering it because I hope it does cover all the important technical points I explained above.

Is it all worth it? Probably not, as previously explained. However, I think along the lines of "if it's worth doing at all, then it's worth doing right";-).

Ok, did something really stupid. I've forgotten exactly where I downloaded Freetype2 from, but the 2.1.3 version is YEARS out of date. I just updated to 2.3.9 and it freaking compiles perfectly. Bleh.

Be warned, fellow Google searchers. Step one to troubleshooting is to make sure you're using the most recent release versions of your requirements.

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advice on the tornado google group points to using an async callback (documented at http://www.tornadoweb.org/documentation#non-blocking-asynchronous-requests) to move the file to the cdn.

the nginx upload module writes the file to disk and then passes parameters describing the upload(s) back to the view. therefore, the file isn't in memory, but the time it takes to read from disk–which would cause the request process to block itself, but not other tornado processes, afaik–is negligible.

that said, anything that doesn't need to be processed online shouldn't be, and should be deferred to a task queue like celeryd or similar.

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