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When will Microsoft stop bothering to sell compilers?
The amount of money Microsoft make from selling compilers is peanuts compared to the profits from their Office and OS products (they may even lose money), and this will never change.
In the 80s, when the IBM PC was first introduced and began to take over the computing world, the Microsoft C compiler was not the market leader and not the favorite among developers who had a choice (large companies preferred to buy from large companies and so Microsoft C did well in the corporate market).
The developer market back then was not large and companies selling compilers soon found themselves selling into a saturated market, and many disappeared.
C++ came along and compiler companies jumped on this bandwagon, giving them a new product to sell for a few years.
As an OS vendor, Microsoft needed its own compiler to appear credible to developers. The libraries (the one part of the compiler suite that everybody liked) came along for free, courtesy of being an OS vendor. The IDE, Visual Studio, has grown on developers over time and has gotten a lot better. Visual C++ became the dominant Windows compiler because it was the last man standing.
Like all compilers, Visual C++ supports its own way of doing things in various parts of the language. Use of compiler language foibles are the mechanism by which application developers build porting cost barriers, to other compilers, into their code. Any large application built using Visual C++ will need a lot of effort to port to another compiler, a benefit for Microsoft.
The porting cost generated by compiler differences is a two-way street. Porting applications from another compiler to Visual C++ could be just as expensive as porting from Visual C++.
Microsoft can remove the cost of porting to Windows by supporting llvm within Visual Studio (I don’t ever see them supporting gcc for licensing reasons), plus command line support so that existing make files work and at the end of last year they sort of did just that (they bolted the llvm C/C++ front end onto the code generators for Visual C++; they obviously did not want to cause embarrassment by making it easy for developers to compare the performance of generated code ;-).
There is a large potential downside for Microsoft supporting llvm, it provides an in-your-face opportunity for current Visual C++ users to try another compiler (I suspect a goodly number have never touched any other C+ compiler). The problem with existing Visual C++ user accessing other compilers is not about code quality (Microsoft compilers have never been known for generating the fastest code), it is about making it easy for developers to learn how to make their code portable to other compilers.
Why would a company want to support a version of its product that compiles with Visual C++ abd a version that compiles with llvm? In the short term expediency, but in the long term it makes sense to use a single compiler.
Microsoft has ported it’s Office products to non-Windows platforms. Was Visual C++ involved? Using Visual C++ would have meant extending it to support the language extensions that occur in the headers files present on other platforms. There are advantages to using the compiler used to build the OS and I suspect this is what the Office team did (I don’t have any inside knowledge).
Is the only long term customer of Visual C++ the Windows development group? I wonder how hard it would be to tweak Windows+llvm so that one could build the other?
The writing is on the wall for Visual C++; how long does it have? Five years, 10?
I would not be surprised if there are a few very large customers, with many millions of lines of code they do not want to touch, who would put up the money to keep the development group on life support. Visual studio could be around 50 years from now, kept alive by the need to compile huge dinosaurs that are not quite important enough or cause enough grief to warrant rewriting. Perhaps it will end up being the oldest commercial compiler still in production use.
Least Recently Used: The experiment that made its reputation
Today we all know that least recently used is the best page replacement algorithm for virtual memory systems (actually paging is complicated in today’s intertwined computing world).
Virtual memory was invented in 1959 and researchers spent the 1960s trying to figure out the best page replacement algorithm.
Programs were believed to spend most of their time in loops and this formed the basis for the rationale for why FIFO, First In First Out, was the best page replacement algorithm (widely used at the time).
Least recently used, LRU, was on people’s radar as a possible technique and was mathematically analysed, along with various other techniques. The optimal technique was known and given the name OPT; a beautifully simple technique with one implementation drawback, it required knowledge of future memory usage behavior (needless to say some researchers set to work trying to predict future memory usage, so this algorithm could be used).
An experiment by Tsao, Comeau and Margolin, published in 1972, showed that LRU outperformed FIFO and random replacement. The rest, as they say, is history; in this case almost completely forgotten history.
The paper “A multifactor paging experiment: I. The experiment and conclusions” was published as one of a collection of papers in “Statistical Computer Performance Evaluation” edited by Freiberger. A second paper by two of the authors in the same book outlines the statistical methodology. Appearing in a book means this paper can be very hard to track down. I recently bought a copy of the book on Amazon for one penny (the postage was £2.80).
The paper contains a copy of the experimental results and below are the page swap numbers:
loadseq group group group freq freq freq alpha alpha alpha Pages 24 20 16 24 20 16 24 20 16 LRU S 32 48 538 52 244 998 59 536 1348 LRU M 53 81 1901 112 776 3621 121 1879 4639 LRU L 142 197 5689 262 2625 10012 980 5698 12880 FIFO S 49 67 789 79 390 1373 85 814 1693 FIFO M 100 134 3152 164 1255 4912 206 3394 5838 FIFO L 233 350 9100 458 3688 13531 1633 10022 17117 RAND S 62 100 1103 111 480 1782 111 839 2190 RAND M 96 245 3807 237 1502 6007 286 3092 7654 RAND L 265 2012 12429 517 4870 18602 1728 8834 23134 |
Three Fortran programs were used, Small (55 statements), Medium (215 statements) and Large (595 statements). These programs were loaded by group (sequences of frequently called subroutines grouped together), freq (subrotines causing the most page swaps were grouped together), alpha (subroutines were grouped alphabetically).
The system was configured with either 24, 20 or 16 pages of 4,096 bytes; it had a total of 256K of memory (a lot of memory back then). The experiment consumed 60 cpu hours.
Looking at the table, it is easy to see that LRU has the best performance. In places random replacement beats FIFO. A regression model (code and data) puts numbers on the performance advantage.
The paper says that the only interaction was between memory size (i.e., number of pages) and how the programs were loaded. I found pairwise interaction between all variables, but then I am using a technique that was being invented as this paper was being published (see code for details).
Number of page swaps was one of three techniques used for measuring performed. The other two were activity count (average number of pages in main memory referenced at least once between page swaps) and inactivity count (average time, measured in page swaps, of a page in secondary storage after it had been swapped out). See data for details.
I vividly remember dropping in on a randomly chosen lecture in computer science in the mid-70s (I studied physics and electronics), the subject was page selection algorithms and there were probably only a dozen people in the room (physics and electronics sometimes had close to 100). The lecturer regaled those present with how surprising it was that LRU was the best and somebody had actually done an experiment showing this. Having a physics/electronics background, the experimental approach to settling questions was second nature to me.
GitHub, the logical next purchase for Microsoft after LinkedIn
Active directory sits at the center of the Microsoft corporate product line, providing identity related services. All the Microsoft services use it to figure out what users can and cannot do. Ensuring it was possible for third parties to implement Active Directory was on the must-do task list during the compliance phase of the EU/Microsoft competition case.
With the LinkedIn purchase Microsoft has acquired the base from which to implement Active Directory for companies operating in the cloud; the identity service used by companies to monitor employees looks like it will continue to be owned and operated by Microsoft.
People have been known to be inventive when writing their cvs and LinkedIn profiles are unlikely to be any different. How do Microsoft introduce some quality control to the claims appearing in profiles?
One way of verifying the claims made by software developers, about what projects they have worked on and how much they contributed, is to look at the code they have written. If Microsoft owned GitHub they would be in a position to do just that, starting with its 14 million users.
What you know a developers employment history and the code they wrote, all sorts of services can be offered. Companies are rightly concerned about the intellectual property of the software they produce and use. Microsoft would be in a position to provide a code IP checking service, the code produced by company employees could be compared against the code they wrote at previous companies to produce a similarity value. This checking option can be sold to all companies in a developer’s work history; companies want to know that when an employee leaves they don’t take anything with them.
Technical details. You would be correct to point out that quantity of code written is not a sensible predictor of developer productivity. Not a problem when selling to management, there are enough academic studies associating quantity with productivity to make management believe otherwise.
Microsoft don’t actually need to see the source code to perform a lot of similarity checks. Many clone detection tools work by comparing hashes of small sequences of particular code features; comparing constructs.
Finding the gold nugget papers in software engineering research
Academic research projects are like startups in that most fail to make any return on their investment (e.g., the tax payer does not get any money back) and a few pay for themselves and all the failures. Irrespective of whether a project succeeds or fails, those involved will publish papers on the work, give talks at conferences and workshops and general try to convince anyone who will listen that the project was a great success.
Number or papers published plays an important role in evaluating the quality of a university department and the performance on an individual researcher. As you can imagine, this publish or perish culture leads to huge amounts of clueless nonsense ending up in print. Don’t be fooled by the ‘peer reviewed’ label, most of this gets done by the least experienced people (e.g., postgrad students) as a means of gaining social recognition in their specific research community, i.e., those doing the reviewing don’t always know much.
The huge number of papers describing failed projects and/or containing clueless nonsense is a major obstacle for anyone wanting to locate useful new knowledge.
While writing my C book I refined the following approach to finding high quality papers and created filtering rules for the subjects it covered. The rules below are being applied to papers relating to my Empirical Software Engineering book. I don’t claim any usefulness for these rules outside of academic software engineering research.
I use a scatter gun approach to obtain a basic collection of papers followed by ruthless filtering.
The scatter gun approach might start with one or two papers; following links on Google Scholar or even just Google search results filtered on “filetype:pdf”, in the past I have used CiteSeer which Google now does a better job of indexing, and Semantic Scholar is now starting to be quite good.
After 30 minutes or so I have 50 pdfs (I download maybe 4,000 a year). Now I need to quickly filter the nonsense to end up with maybe 10 that I will spend 5 minutes each reading, leaving maybe 2 or 3 for detailed reading (often not the original ones I started with).
When dealing with this kind of volume you have to be ruthless.
Spend just 10 seconds on the first pass. If a paper has some merits, let it remain for the next pass. Scan the paper looking for major indicators of clueless nonsense; these are not hard to spot, don’t linger, hit the delete (if data is involved, it is worth quickly checking the footnotes for a url to a dataset, which may be new and worth collecting):
- it relies on machine learning,
- it relies on information theory,
- it relies on Halstead’s metric,
- it investigates software quality. This is a marketing term used to give a patina of relevance to the worthless metric that is likely used in the research. Be on the lookout for other high relevance terms being used to provide a positive association with a worthless metric, e.g., developer productivity defined as volume of code written
- it involves fault prediction. Academic folk psychology includes a belief that some project files contain more faults than other files, because more faults are reported in some files than others (or even that entire projects are more reliable because fewer faults have been reported). This is a case of the drunk searching under a streetlight for lost keys because that is where the ground can be seen. Faults are found by executing code, more execution means more faults. I only know of two papers that are exceptions to this rule (one of them is discussed here),
- the primary claim in the conclusion is to have done something novel. Research requires doing new stuff, novel is a key attribute that is rather pointless for its own sake. Typing code using your nose would be novel, but would you want to spend more than 10 seconds reading a paper on the subject (and this example is at the more sensible end of the spectrum of novel research I have read about).
The first pass removes around 70-80% of the papers, at least for me.
For the second pass I will spend a minute or so doing a slightly slower scan. This often cuts the papers remaining in half.
By now, I have been collecting and filtering for over 90 minutes; time to do something else, perhaps not returning for many hours.
The third pass involves trying to read the paper. The question is: Am I having trouble reading this paper because the author has managed to compress a lot of useful information into a publication page limit, or is the paper so bad I cannot see the wood for the dead trees?
Answering this question takes practice and some knowledge of the subject area. You will speed up with practice and learning about the subject.
Some things that might be thought worth paying attention to, but should be ignored:
- don’t bother looking at the names of the authors or which university they work at (who wrote the paper almost always provides no clues to its quality; there are very few exceptions to this and you will learn who they are over time),
- ignore the journal or conference that published the paper (gold nuggets appear everywhere and high impact venues only restrict the clueless nonsense to the current trendy topics and papers citing the ‘right’ people),
- ignore year of publication, quality is ageless (and sometimes fades from view because research fashions change).
If you have your own tips for finding the gold nuggets in software engineering, please let me know.
Predicting the next value in an integer sequence
There was a Kaggle meetup group hackathon on Saturday. Integer sequence learning is a recently posted Kaggle challenge; build a model to predict the next value in an integer sequence, with example data coming from the On-Line Encyclopedia of Integer Sequences. How could I not want to try my hand at this challenge, and I signed up for the hackathon hoping to find like-minded folk.
My only previous encounter with mining the OEIS was a paper that attempted to combine two or more existing sequences to match one existing sequence and to contribute a few sequences I had found.
The event was well attended, and I found a fellow enthusiast in Lampros.
We kicked around a few ideas and while I jumped in and started investigating the characteristics of the data, Lampros started searching for solutions that others had found to the next value in an integer sequence problem (a much more sensible approach, but probably not as much fun as jumping in feet first; this was his first hackathon, so he has not picked up any bad habits yet ;-).
This is one of those few problems when over-fitting is required for things to work.
I concentrated on characterizing the 113,845 sequences in the training set and the following is a summary (code and data):
- 74,465 sequences contained a maximum value less than
and 98,916 less than 
. So symbolic maths is going to be needed (a shame since I had found tscount, an R package for analyzing count time series), - a goodly 72,202 sequences are in sorted order, leaving 41,643 to go up and down,
- The least significant digit of the values in many sequences are a subset of the ten possible values. The following is a list of the number of unique least significant digits in the training sequences:
1 2 3 4 5 6 7 8 9 10 1289 4692 6800 11589 15773 13701 7635 8644 10837 32885
After removing sequences containing fewer than 20 values (the -1 count)
-1 1 2 3 4 5 6 7 8 9 10 33398 764 3006 3171 6068 8930 7898 3690 5604 9073 32243
Lampros found Martin Rubey’s work on guessing formulas for sequences, which had an Open Source implementation using FriCAS (source code, which needs Steel Bank Common Lisp to build, which itself needs your OS to support 512 open files {OS X default is 256}).
Other software and papers include: the gfun package plus paper, sequence prediction in Mathematica and a Masters thesis on Inductive Inference of Integer Sequences.
These systems are all based on fitting, a potentially very complicated, polynomial to each sequence and achieve a success rate of around 20% on the complete OEIS.
What are the characteristics of the 80% for which a polynomial fit does not predict the next value? Perhaps there are not enough values specified in these sequence to fit the necessary polynomial. Perhaps the values grow at a faster rate than can be fitted by any polynomial expression, e.g., the Busy beaver function.
A 11:00-17:00 hackathon is not really enough time to get anything sensible up and running. People doing Masters projects had more hours and were managing to get around 20% correct.
The competition leaderboard currently has somebody with a score of 0.74. This is a big lead over everybody else. Am I being cynical by thinking the model might be reading the OEIS text to find the formula describing the sequence and then evaluating this?
Update
Comparing Computer Models Solving Number Series Problems provides an interesting review of systems using an AI based approach, i.e., trying to mimic what people do.
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