Re: howto count lines - fast
On Fri, 2017-06-02 at 10:32 -0700, H. S. Teoh via Digitalmars-d-learn wrote: > […] > > Also, compiler implementors do still have to do the "heroics", or > rather, teach the compiler to do the "heroics" when compiling > straightforward code. So while the general programmer probably will > have > less need for it, compiler writers still need to know how to do them > in > order to write the optimizing compilers in the first place. There are many different sorts of programming. Operating systems, compilers, GUIs, Web services, machine learning, etc., etc. all require different techniques. Also there are always new areas, where idioms and standard approaches are yet to be discovered. There will always be a place for "heroic", but to put it up on a pedestal as being a Good Thing For All™ is to do "heroic" an injustice. We should also note that in the Benchmark Game, the "heroic" solutions are targetted specifically at Isaac's execution machine, which often means they are crap programs on anyone else's computer. > […] > be able to generate optimal code in all cases. There will always be > cases where you have to manually tweak it yourself. Of course, that > doesn't mean you go around micro-optimizing everything -- the usual > approach is to write it the straightforward way first, then profile > it, > identify the hotspots, and find ways to improve performance in the > hotspots. Well, at a higher level, the first order of business is > really > to look at it from an algorithmic POV and decide whether or not a > different algorithm ought to be used (and no optimizing compiler can > help you there). Then if that's still not enough, then you dig into > the > details and see if you can squeeze more juice out of your current > algorithms -- if the profiler has indicated that they are the > bottleneck. The optimisations are though generally aimed at the current execution computer. Which is fine in the short term. However in the long term, the optimisations become the problem. When the execution context of an optimised code changes then the optimisations should be backed out and new optimisations applied. Sadly this rarely happens, and you end up with new optimisations laid on old (redundant) optimisations, and hence to incomprehensible code that people darn't amend as they have no idea what the is going on. > […] > But *somebody* has to implement those computational models and > programming languages. If nobody knows how to write "heroic" code, > then > nobody would know how to write an optimizing compiler that produces > such > code either, and these computational models and programming languages > wouldn't exist in the first place. Which returns us to there are different sorts of programming, and there are people at "the bleeding edge" of languages, techniques, and hardware, researching these new things. Or there ought to be, it's just that you need funds to do it. > I know that the average general programmer doesn't (and shouldn't) > care. > But *somebody* has to, in order to implement the system in the first > place. *Somebody* had to implement the "heroic" version of memchr so > that others can use it as a primitive. Without that, everyone would > have > to roll their own, and it's almost a certainty that the results will > be > underwhelming. It may be worth noting that far too few supposedly professional programmers actually know enough about the history of their subject to be deemed competent. -- Russel. = Dr Russel Winder t: +44 20 7585 2200 voip: sip:russel.win...@ekiga.net 41 Buckmaster Roadm: +44 7770 465 077 xmpp: rus...@winder.org.uk London SW11 1EN, UK w: www.russel.org.uk skype: russel_winder signature.asc Description: This is a digitally signed message part
Re: howto count lines - fast
On Thu, Jun 01, 2017 at 08:39:07AM +0100, Russel Winder via Digitalmars-d-learn wrote: > On Wed, 2017-05-31 at 16:37 -0700, H. S. Teoh via Digitalmars-d-learn > wrote: > > > […] > > With D, we can have the cake and eat it too. The understandable / > > naïve implementation can be available as a fallback (and reference > > implementation), with OS-specific optimized implementations guarded > > under version() or static-if blocks so that one could, in theory, > > provide implementations specific to each supported platform that > > would give the best performance. > > But isn't that the compiler's job? Unfortunately, the compiler can only go so far, because it doesn't understand the larger context of what you're trying to accomplish. Modern optimizing compilers certainly go a lot further than before, but still, at some point some amount of human judgment is needed. Also, compiler implementors do still have to do the "heroics", or rather, teach the compiler to do the "heroics" when compiling straightforward code. So while the general programmer probably will have less need for it, compiler writers still need to know how to do them in order to write the optimizing compilers in the first place. > The lesson of functional programming, logic programming, etc. (when > the acolytes remember the actual lesson) is that declarative > expression of goal is more comprehensible to people than detailed > explanation of how the computer calculates a result. Object-oriented > computing lost the plot somewhere in the early 2000s. There is no argument that straightforward code is more comprehensible to people. The question here is whether it delivers maximum performance. We know from Kolgomorov complexity theory that global optimization, in the general case, is undecidable, so no optimizing compiler is going to be able to generate optimal code in all cases. There will always be cases where you have to manually tweak it yourself. Of course, that doesn't mean you go around micro-optimizing everything -- the usual approach is to write it the straightforward way first, then profile it, identify the hotspots, and find ways to improve performance in the hotspots. Well, at a higher level, the first order of business is really to look at it from an algorithmic POV and decide whether or not a different algorithm ought to be used (and no optimizing compiler can help you there). Then if that's still not enough, then you dig into the details and see if you can squeeze more juice out of your current algorithms -- if the profiler has indicated that they are the bottleneck. > The advance of Scala, Kotlin, Groovy, and now Rust and Go (but only to > some extent), which D has, is to express algorithms as declarative > requirements in a dataflow manner. > > One day hardware people will catch up with the hardware innovations of > the 1970s and 1980s regarding support of dataflow rather than state. Dataflow can only capture a limited subset of algorithms. Of course, in my observation, 90% of code being written today generally belongs in the category of what I call "linear shuffling of data", i.e., iterate over some linear set of objects and perform some transformation X on each object, copying linear memory region X to linear memory region Y, rearranging some linear set of objects, permuting the order of some linear set of things, etc.. This category basically subsumes all of GUI programming and web programming, which in my estimation covers 90% or maybe even 95% of code out there. The bulk of game code also falls under this category -- they are basically a matter of copying X items from A to B, be they pixels to be copied to the video buffer, traversing the list of in-game objects to update their current position, direction, speed, or traversing scanlines of a polygon to map a 3D object to 2D, etc.. Almost all business logic also falls under this category. All of these are easily captured by dataflow models. However, there are algorithms outside of this category, that are not easily captured by the dataflow model. Solving certain graph theory problems, for example, require actual insight into the structure of the problem than mere "move X items from A to B". Route planning, which is an NP-complete problem that, for practical applications, can only be approximated, and therefore actual thought is required for how you actually go about solving the problem beyond "data X moves from A to B". Computing the convex hull of a set of input points, used for solving optimization problems, if expressed and solved in a naïve way, has O(n^3) time complexity, and therefore impractical for non-trivial problem instances. True, your average general programmer may not even know what a convex hull problem is, and probably doesn't even need to care -- at worst, there are already libraries out there that implement the algorithms for you. But the point is, *somebody* out there needs to write these algorithms, and they need to implement these
Re: howto count lines - fast
On Wed, May 31, 2017 at 12:13:04PM -0700, H. S. Teoh via Digitalmars-d-learn
wrote:
> On Tue, May 30, 2017 at 05:13:46PM -0700, Ali Çehreli via Digitalmars-d-learn
> wrote:
> [...]
> > I could not make the D program come close to wc's performance when the
> > data was piped from stdin.
> [...]
>
> Hmm. This is a particularly interesting case, because I adapted some
> of my algorithms to handle reading from stdin (i.e., std.mmfile is not
> an option), and I could not get it to reach wc's performance! I even
> installed ldc just to see if that made a difference... it was somewhat
> faster than gdc, but still, the timings were about twice as slow as
> wc.
[...]
Here's a little update on my experiments w.r.t. reading from stdin
without being able to use std.mmfile: I found that I was able to achieve
decent performance by modifying the loop so that it loads data from the
array in size_t chunks rather than by individual bytes, and looping over
the bytes in the size_t to check for matches. Here's the code:
size_t lineCount7(ref File input)
{
import std.algorithm.searching : count;
ubyte[] buf;
size_t c = 0;
buf.length = 8192;
foreach (d; input.byChunk(buf))
{
if (d.length == buf.length)
{
auto ichunk = cast(ulong[]) d;
size_t subtotal = 0;
foreach (i; ichunk)
{
enum eol = cast(ulong) '\n';
if ((i & 0x00FF) == eol ) subtotal++;
if ((i & 0xFF00) == (eol << 8)) subtotal++;
if ((i & 0x00FF) == (eol << 16)) subtotal++;
if ((i & 0xFF00) == (eol << 24)) subtotal++;
if ((i & 0x00FF) == (eol << 32)) subtotal++;
if ((i & 0xFF00) == (eol << 40)) subtotal++;
if ((i & 0x00FF) == (eol << 48)) subtotal++;
if ((i & 0xFF00) == (eol << 56)) subtotal++;
}
c += subtotal;
}
else
c += d.count('\n');
}
return c;
}
When the last chunk of the file (possibly the entire file, if it's < 8
KB) is incomplete, we revert back to the naïve loop-over-bytes search.
While this superficially may seem like unnecessary complication, it
actually makes a significant performance difference, because:
(1) Reading the array in size_t chunks means less roundtrips to RAM or
L1/L2/L3 caches.
(2) Since a size_t fits within a single CPU register, the inner loop can
be completely done inside the CPU without needing to even go to L1
cache, which, while it's pretty fast, is still a memory roundtrip. The
register file is the fastest memory of all, so we maximize this
advantage here.
(3) Since size_t has a fixed size, the loop can be completely unrolled
(ldc does this) and thus completely eliminate branch hazards from the
inner loop.
I originally tried to copy the glibc memchr implementation's xor trick
for checking whether a size_t word contains any matching bytes, but I
got mixed results, and in any case it loses out to my system's wc
implementation. I suppose given enough effort I could track down what's
causing the D code to slow down, but then I realized something else
about wc: since it uses memchr to find EOL bytes, and now that I know
memchr's implementation in glibc, it means that a lot of overhead is
introduced when the data being scanned contains a lot of matches.
So I did a little test: I created two text files, both 200 million bytes
long, with all 200 million bytes newlines (i.e., 200,000,000 blank
lines), and one with 100-character lines (2,000,000 lines in total).
Then as an intermediate between these two extremes, I concatenated all
of the .d files in Phobos 10 times to make a file with 2.8 million lines
of varying lengths. Then I tested both my system's wc and my linecount
written in D to see how they performed on these files (piped through
stdin, so no mmap-ing is involved).
Here are the results (note: these are raw measurements; I did not
account for system background noise):
+--+---+--+
| 200M blank lines | 2M 100-byte lines | 10x Phobos code |
+---+--+---+--+
| wc -l | real0m0.475s | real0m0.080s | real0m0.083s |
| | user0m0.417s | user0m0.034s | user0m0.062s |
| | sys 0m0.058s | sys 0m0.046s | sys 0m0.020s |
+---+--+---+--+
| linecount | real0m0.181s | real0m0.190s | real0m0.099s |
| | user0m0.138s | user0m0.129s |
Re: howto count lines - fast
On Wed, 2017-05-31 at 16:37 -0700, H. S. Teoh via Digitalmars-d-learn wrote: > […] > With D, we can have the cake and eat it too. The understandable / > naïve > implementation can be available as a fallback (and reference > implementation), with OS-specific optimized implementations guarded > under version() or static-if blocks so that one could, in theory, > provide implementations specific to each supported platform that > would > give the best performance. But isn't that the compiler's job? The lesson of functional programming, logic programming, etc. (when the acolytes remember the actual lesson) is that declarative expression of goal is more comprehensible to people than detailed explanation of how the computer calculates a result. Object-oriented computing lost the plot somewhere in the early 2000s. The advance of Scala, Kotlin, Groovy, and now Rust and Go (but only to some extent), which D has, is to express algorithms as declarative requirements in a dataflow manner. One day hardware people will catch up with the hardware innovations of the 1970s and 1980s regarding support of dataflow rather than state. > I disagree about the philosophy of "if you need to go faster, use a > bigger computer". There are some inherently complex problems (such > as > NP-complete, PSPACE-complete, or worse, outright exponential class > problems) where the difference between a "heroic implementation" of a > computational primitive and a naïve one may mean the difference > between > obtaining a result in this lifetime vs. practically never. Or, more > realistically speaking, the difference between being able to solve > moderately-complex problem instances vs. being able to solve only > trivial toy instances. When you're dealing with exponential > complexity, > every small bit counts, and you can never get a big enough computer. There are always places for experimentation, and innovation. Hard problems will always be hard, we just need to find the least hard way of expressing the solutions. The crucial thing is that people always work to remove the heroicism of the initial solutions, creating new computational models, new programming languages, etc. to do it. -- Russel. = Dr Russel Winder t: +44 20 7585 2200 voip: sip:russel.win...@ekiga.net 41 Buckmaster Roadm: +44 7770 465 077 xmpp: rus...@winder.org.uk London SW11 1EN, UK w: www.russel.org.uk skype: russel_winder signature.asc Description: This is a digitally signed message part
Re: howto count lines - fast
On Wednesday, May 31, 2017 22:50:19 H. S. Teoh via Digitalmars-d-learn wrote: > Perhaps the right approach is to check if the array length is less than > some arbitrary threshold, and just use a naïve loop below that, and only > switch to the complicated hackish stuff where you're sure it will > actually benefit, rather than hurt, performance. Based on some previous discussions, I think that this is the sort of thing that std.algorithm.sort does (switch algorithms depending on the size of the range to be sorted), but I've never actually verified it. - Jonathan M Davis
[OT] Re: howto count lines - fast
On Wed, 2017-05-31 at 16:37 -0700, H. S. Teoh via Digitalmars-d-learn wrote: > […] > > An even more down-to-earth counterargument is that if CPU vendors had > been content with understandable, simple CPU implementations, and > eschewed "heroic", hard-to-understand things like instruction > pipelines > and cache hierarchies, we'd still be stuck with 16 MHz CPU's in 2017. > The people looking at modern, ultra-parallel hardware architectures are indeed looking to use very simple CPU with ultra-low power use. Just because Moore Law, the demand for computation, etc, during the 1980s, 1990s and 2000s led to x86_64 with its "heroic" silicon wafer layout, doesn't mean that is where we have to stay. That's legacy thinking based on huge investments of capital and requirement of a company to continue to force an income stream from it's customers. The current state of mainstream hardware is all about not innovating. The problem with supercomputing just at the moment, is that you have to build a power station for each one. The x86_64 and GPGPU approach hasn't hit the end of Moore's Law, it's hit the "we can't supply enough power to run it" wall. The Cloud is also not the answer, it's just and income stream for a couple of companies pretending to continue to innovate. -- Russel. = Dr Russel Winder t: +44 20 7585 2200 voip: sip:russel.win...@ekiga.net 41 Buckmaster Roadm: +44 7770 465 077 xmpp: rus...@winder.org.uk London SW11 1EN, UK w: www.russel.org.uk skype: russel_winder signature.asc Description: This is a digitally signed message part
Re: howto count lines - fast
On Wed, May 31, 2017 at 10:05:50PM -0700, Jonathan M Davis via Digitalmars-d-learn wrote: > On Thursday, June 01, 2017 04:52:40 Patrick Schluter via Digitalmars-d-learn [...] > > See my link above to realdworldtech. Using SIMD can give good > > results in micro-benchmarks but completely screw up performance > > of other things in practice (the alignment requirements are heavy > > and result in code bloat, cache misses, TLB misses, cost of > > context switches, AVX warm up time (Agner Fog observed around > > 1 cycles before AVX switches from 128 bits to 256 bits > > operations), reduced turboing, etc.). > > Whenever you attempt more complicated optimizations, it becomes harder > to get it right, and you always have the problem of figuring out > whether you really did make it better in general. It's the sort of > thing that's easier when you have a specific use case and it's very > difficult to get right when dealing with a general solution for a > standard library. So, it doesn't surprise me at all if a particular > optimization turns out to be a bad idea for Phobos even if it's great > for some use cases. [...] It makes me want to just say, write a naïve loop expressing exactly what you intend to achieve, and let the compiler's optimizer figure out how to best optimize it for your target architecture. Unfortunately, just earlier today while testing an incomplete version of count() that uses the ulong iteration optimization, I discovered to my horror that ldc (at -O3) apparently recognizes that exact hack and turns the loop into a massive bowl of SSE/MMX/AVX/etc soup that's many times the size of the "unoptimized" loop. After reading the thread Patrick pointed out from realworldtech, I'm starting to wonder if the result is actually faster in practice, or if it only looks good in benchmarks, because that code bloat is going to add instruction cache pressure and probably TLB misses, etc.. If your program mostly calls count() on smallish arrays (which I'd say is rather likely in cases that matter, because I can't imagine someone would want to count bytes in 1MB arrays inside an inner loop -- in inner loops you'd tend to be working with smaller chunks of data and thus you'd want count() to be fast for small to medium-sized arrays), then a small, tight "unoptimized" loop is going to perform much better, because it would be easily inlineable, won't add 1KB to your function body size, and thus increase the chances your function body won't overflow the instruction cache. Plus, reducing the amount of complicated branches and other paraphrenalia will make the CPU's branch predictor more likely to get it right, so you're less likely to cause pipeline stalls. Perhaps the right approach is to check if the array length is less than some arbitrary threshold, and just use a naïve loop below that, and only switch to the complicated hackish stuff where you're sure it will actually benefit, rather than hurt, performance. T -- Not all rumours are as misleading as this one.
Re: howto count lines - fast
On Thursday, June 01, 2017 04:52:40 Patrick Schluter via Digitalmars-d-learn wrote: > On Thursday, 1 June 2017 at 04:39:17 UTC, Jonathan M Davis wrote: > > On Wednesday, May 31, 2017 16:03:54 H. S. Teoh via > > > > Digitalmars-d-learn wrote: > >> [...] > > > > Digitalmars-d-learn wrote: > >> [...] > > > > If you're really trying to make it fast, there may be something > > that you can do with SIMD. IIRC, Brian Schott did that with his > > lexer (or maybe he was just talking about it - I don't remember > > for sure). > > See my link above to realdworldtech. Using SIMD can give good > results in micro-benchmarks but completely screw up performance > of other things in practice (the alignment requirements are heavy > and result in code bloat, cache misses, TLB misses, cost of > context switches, AVX warm up time (Agner Fog observed around > 1 cycles before AVX switches from 128 bits to 256 bits > operations), reduced turboing, etc.). Whenever you attempt more complicated optimizations, it becomes harder to get it right, and you always have the problem of figuring out whether you really did make it better in general. It's the sort of thing that's easier when you have a specific use case and it's very difficult to get right when dealing with a general solution for a standard library. So, it doesn't surprise me at all if a particular optimization turns out to be a bad idea for Phobos even if it's great for some use cases. - Jonathan M Davis
Re: howto count lines - fast
On Thursday, 1 June 2017 at 04:39:17 UTC, Jonathan M Davis wrote: On Wednesday, May 31, 2017 16:03:54 H. S. Teoh via Digitalmars-d-learn wrote: [...] Digitalmars-d-learn wrote: [...] If you're really trying to make it fast, there may be something that you can do with SIMD. IIRC, Brian Schott did that with his lexer (or maybe he was just talking about it - I don't remember for sure). See my link above to realdworldtech. Using SIMD can give good results in micro-benchmarks but completely screw up performance of other things in practice (the alignment requirements are heavy and result in code bloat, cache misses, TLB misses, cost of context switches, AVX warm up time (Agner Fog observed around 1 cycles before AVX switches from 128 bits to 256 bits operations), reduced turboing, etc.).
Re: howto count lines - fast
On Wednesday, 31 May 2017 at 23:03:54 UTC, H. S. Teoh wrote: On Wed, May 31, 2017 at 03:46:17PM -0700, Jonathan M Davis via Digitalmars-d-learn wrote: On Wednesday, May 31, 2017 12:13:04 H. S. Teoh via Digitalmars-d-learn wrote: > I did some digging around, and it seems that wc is using > glibc's memchr, which is highly-optimized, whereas > std.algorithm.count just uses a simplistic loop. Which is > strange, because I'm pretty sure somebody optimized > std.algorithm some time ago to use memchr() instead of a > loop when searching for a byte value in an array. Whatever > happened to that?? I don't know, but memchr wouldn't work with CTFE, so someone might have removed it to make it work in CTFE (though that could be done with a different branch for CTFE). Or maybe it never made it into std.algorithm for one reason or another. [...] I checked the Phobos code again, and it appears that my memory deceived me. Somebody *did* add memchr optimization to find() and its friends, but not to count(). CTFE compatibility is not a problem, since we can just if(__ctfe) the optimized block away. I'm currently experimenting with a memchr-optimized version of count(), but I'm getting mixed results: on small arrays or large arrays densely packed with matching elements, the memchr version runs rather slowly, because it involves a function call into the C library per matching element. On large arrays only sparsely populated with matching elements, though, the memchr-optimized version beats the current code by about an order of magnitude. Since it wouldn't be a wise idea to assume sparsity of matches in Phobos, I decided to do a little more digging, and looked up the glibc implementation of memchr. The main optimization is that it iterates over the array not by byte, as a naïve loop would do, but by ulong's. That's what I suggested above. It's the first optimisation to do when looping over a buffer (memcpy, memset, memchr etc.). (Of course, the first n bytes and last n bytes that are not ulong-aligned are checked with a per-byte loop; so for very short arrays it doesn't lose out to the naïve loop.) In each iteration over ulong, it performs the bit-twiddling hack alluded to by Nitram to detect the presence of matching bytes, upon which it breaks out to the closing per-byte loop to find the first match. For short arrays, or arrays where a match is quickly found, it's comparable in performance to the naïve loop; for large arrays where the match is not found until later, it easily outperforms the naïve loop. It is also important to not overdo the optimisations as it can happen that the overhead generated manifests in pessimations not visible in a specific benchmark. The code size explosion may induce I-cache misses, it can also cost I-TLB misses. Worse, using SSE or AVX can kill thread switch time or worse even reduce the turboing of the CPU. It's currently a hot topic on realworldtech[1]. Linus Torvalds rants about this issue wit memcpy() which is over-engineered and does more harm than good in practice but has nice benchmark result. My current thought is to adopt the same approach: iterate over size_t or some such larger unit, and adapt the bit-twiddling hack to be able to count the number of matches in each size_t. This is turning out to be trickier than I'd like, though, because there is a case where carry propagation makes it unclear how to derive the number of matches without iterating over the bytes a second time. But this may not be a big problem, since size_t.sizeof is relatively small, so I can probably loop over individual bytes when one or more matches is detected, and a sufficiently-capable optimizer like ldc or gdc would be able to unroll this into a series of sete + add instructions, no branches that might stall the CPU pipeline. For densely-matching arrays, this should still have comparable performance to the naïve loops; for sparsely-matching arrays this should show significant speedups. That's what I think too, that a small and simple loop to count the matching bytes in the ulong would be a somehow faster than the bit twiddling trick which requires a population count of bits. [1]: http://www.realworldtech.com/forum/?threadid=168200=168700
Re: howto count lines - fast
On Wednesday, May 31, 2017 16:03:54 H. S. Teoh via Digitalmars-d-learn wrote: > On Wed, May 31, 2017 at 03:46:17PM -0700, Jonathan M Davis via Digitalmars-d-learn wrote: > > On Wednesday, May 31, 2017 12:13:04 H. S. Teoh via Digitalmars-d-learn > > > > wrote: > > > I did some digging around, and it seems that wc is using glibc's > > > memchr, which is highly-optimized, whereas std.algorithm.count just > > > uses a simplistic loop. Which is strange, because I'm pretty sure > > > somebody optimized std.algorithm some time ago to use memchr() > > > instead of a loop when searching for a byte value in an array. > > > Whatever happened to that?? > > > > I don't know, but memchr wouldn't work with CTFE, so someone might > > have removed it to make it work in CTFE (though that could be done > > with a different branch for CTFE). Or maybe it never made it into > > std.algorithm for one reason or another. > > [...] > > I checked the Phobos code again, and it appears that my memory deceived > me. Somebody *did* add memchr optimization to find() and its friends, > but not to count(). > > CTFE compatibility is not a problem, since we can just if(__ctfe) the > optimized block away. > > I'm currently experimenting with a memchr-optimized version of count(), > but I'm getting mixed results: on small arrays or large arrays densely > packed with matching elements, the memchr version runs rather slowly, > because it involves a function call into the C library per matching > element. On large arrays only sparsely populated with matching > elements, though, the memchr-optimized version beats the current code by > about an order of magnitude. > > Since it wouldn't be a wise idea to assume sparsity of matches in > Phobos, I decided to do a little more digging, and looked up the glibc > implementation of memchr. The main optimization is that it iterates over > the array not by byte, as a naïve loop would do, but by ulong's. (Of > course, the first n bytes and last n bytes that are not ulong-aligned > are checked with a per-byte loop; so for very short arrays it doesn't > lose out to the naïve loop.) In each iteration over ulong, it performs > the bit-twiddling hack alluded to by Nitram to detect the presence of > matching bytes, upon which it breaks out to the closing per-byte loop to > find the first match. For short arrays, or arrays where a match is > quickly found, it's comparable in performance to the naïve loop; for > large arrays where the match is not found until later, it easily > outperforms the naïve loop. > > My current thought is to adopt the same approach: iterate over size_t or > some such larger unit, and adapt the bit-twiddling hack to be able to > count the number of matches in each size_t. This is turning out to be > trickier than I'd like, though, because there is a case where carry > propagation makes it unclear how to derive the number of matches without > iterating over the bytes a second time. > > But this may not be a big problem, since size_t.sizeof is relatively > small, so I can probably loop over individual bytes when one or more > matches is detected, and a sufficiently-capable optimizer like ldc or > gdc would be able to unroll this into a series of sete + add > instructions, no branches that might stall the CPU pipeline. For > densely-matching arrays, this should still have comparable performance > to the naïve loops; for sparsely-matching arrays this should show > significant speedups. If you're really trying to make it fast, there may be something that you can do with SIMD. IIRC, Brian Schott did that with his lexer (or maybe he was just talking about it - I don't remember for sure). - Jonathan M Davis
Re: howto count lines - fast
On Thu, Jun 01, 2017 at 12:20:53AM +0100, Russel Winder via Digitalmars-d-learn wrote: [...] > However, I note here that the Chapel folk are taking a quite > interesting view of algorithm implementation in the Benchmarks Game. > They are totally eschewing "heroic implementations" such as all the C, > C++, etc. in favour of understandable and simple ones. Their take on > raw performance is "if you need it to go faster, use a bigger > computer". Which is quite easy when you have a number of Cray > computers at your disposal. :-) Whilst having some fun, the > Benchmark's Game has become all about heroic implementations on a > specific computer. Which makes the Chapel line an excellent one. > > OK for wc the case is different because it is about performance on the > users computer. But still I like the "keep the implementation > comprehensible, avoid heroic implementation". [...] With D, we can have the cake and eat it too. The understandable / naïve implementation can be available as a fallback (and reference implementation), with OS-specific optimized implementations guarded under version() or static-if blocks so that one could, in theory, provide implementations specific to each supported platform that would give the best performance. I disagree about the philosophy of "if you need to go faster, use a bigger computer". There are some inherently complex problems (such as NP-complete, PSPACE-complete, or worse, outright exponential class problems) where the difference between a "heroic implementation" of a computational primitive and a naïve one may mean the difference between obtaining a result in this lifetime vs. practically never. Or, more realistically speaking, the difference between being able to solve moderately-complex problem instances vs. being able to solve only trivial toy instances. When you're dealing with exponential complexity, every small bit counts, and you can never get a big enough computer. An even more down-to-earth counterargument is that if CPU vendors had been content with understandable, simple CPU implementations, and eschewed "heroic", hard-to-understand things like instruction pipelines and cache hierarchies, we'd still be stuck with 16 MHz CPU's in 2017. T -- Not all rumours are as misleading as this one.
Re: howto count lines - fast
On Tue, 2017-05-30 at 17:22 -0700, H. S. Teoh via Digitalmars-d-learn wrote: > […] > performance in a significant way. But I thought this might be a > useful > tip for people who want to squeeze out the last drop of juice from > their > CPUs. ;-) > […] I have the beginnings of wc written in various languages. I may well follow this thread up to create a full D implementation of wc that people can then optimise a bit. However, I note here that the Chapel folk are taking a quite interesting view of algorithm implementation in the Benchmarks Game. They are totally eschewing "heroic implementations" such as all the C, C++, etc. in favour of understandable and simple ones. Their take on raw performance is "if you need it to go faster, use a bigger computer". Which is quite easy when you have a number of Cray computers at your disposal. :-) Whilst having some fun, the Benchmark's Game has become all about heroic implementations on a specific computer. Which makes the Chapel line an excellent one. OK for wc the case is different because it is about performance on the users computer. But still I like the "keep the implementation comprehensible, avoid heroic implementation". -- Russel. = Dr Russel Winder t: +44 20 7585 2200 voip: sip:russel.win...@ekiga.net 41 Buckmaster Roadm: +44 7770 465 077 xmpp: rus...@winder.org.uk London SW11 1EN, UK w: www.russel.org.uk skype: russel_winder signature.asc Description: This is a digitally signed message part
Re: howto count lines - fast
On Wed, May 31, 2017 at 03:46:17PM -0700, Jonathan M Davis via Digitalmars-d-learn wrote: > On Wednesday, May 31, 2017 12:13:04 H. S. Teoh via Digitalmars-d-learn > wrote: > > I did some digging around, and it seems that wc is using glibc's > > memchr, which is highly-optimized, whereas std.algorithm.count just > > uses a simplistic loop. Which is strange, because I'm pretty sure > > somebody optimized std.algorithm some time ago to use memchr() > > instead of a loop when searching for a byte value in an array. > > Whatever happened to that?? > > I don't know, but memchr wouldn't work with CTFE, so someone might > have removed it to make it work in CTFE (though that could be done > with a different branch for CTFE). Or maybe it never made it into > std.algorithm for one reason or another. [...] I checked the Phobos code again, and it appears that my memory deceived me. Somebody *did* add memchr optimization to find() and its friends, but not to count(). CTFE compatibility is not a problem, since we can just if(__ctfe) the optimized block away. I'm currently experimenting with a memchr-optimized version of count(), but I'm getting mixed results: on small arrays or large arrays densely packed with matching elements, the memchr version runs rather slowly, because it involves a function call into the C library per matching element. On large arrays only sparsely populated with matching elements, though, the memchr-optimized version beats the current code by about an order of magnitude. Since it wouldn't be a wise idea to assume sparsity of matches in Phobos, I decided to do a little more digging, and looked up the glibc implementation of memchr. The main optimization is that it iterates over the array not by byte, as a naïve loop would do, but by ulong's. (Of course, the first n bytes and last n bytes that are not ulong-aligned are checked with a per-byte loop; so for very short arrays it doesn't lose out to the naïve loop.) In each iteration over ulong, it performs the bit-twiddling hack alluded to by Nitram to detect the presence of matching bytes, upon which it breaks out to the closing per-byte loop to find the first match. For short arrays, or arrays where a match is quickly found, it's comparable in performance to the naïve loop; for large arrays where the match is not found until later, it easily outperforms the naïve loop. My current thought is to adopt the same approach: iterate over size_t or some such larger unit, and adapt the bit-twiddling hack to be able to count the number of matches in each size_t. This is turning out to be trickier than I'd like, though, because there is a case where carry propagation makes it unclear how to derive the number of matches without iterating over the bytes a second time. But this may not be a big problem, since size_t.sizeof is relatively small, so I can probably loop over individual bytes when one or more matches is detected, and a sufficiently-capable optimizer like ldc or gdc would be able to unroll this into a series of sete + add instructions, no branches that might stall the CPU pipeline. For densely-matching arrays, this should still have comparable performance to the naïve loops; for sparsely-matching arrays this should show significant speedups. T -- My program has no bugs! Only unintentional features...
Re: howto count lines - fast
On Wednesday, May 31, 2017 12:13:04 H. S. Teoh via Digitalmars-d-learn wrote: > I did some digging around, and it seems that wc is using glibc's memchr, > which is highly-optimized, whereas std.algorithm.count just uses a > simplistic loop. Which is strange, because I'm pretty sure somebody > optimized std.algorithm some time ago to use memchr() instead of a loop > when searching for a byte value in an array. Whatever happened to > that?? I don't know, but memchr wouldn't work with CTFE, so someone might have removed it to make it work in CTFE (though that could be done with a different branch for CTFE). Or maybe it never made it into std.algorithm for one reason or another. - Jonathan M Davis
Re: howto count lines - fast
I am glad to see this participation on this issue :)
The hints about trying another compiler and std.mmfile turned out
to be very effective.
Even this simple code is faster then my systems "wc -l" now:
void main() {
import std.stdio;
writeln(lcs("benchmark.dat"));
}
size_t lcs(string filename) {
import std.mmfile;
auto f = new MmFile(filename);
auto data = cast(ubyte[]) f[];
size_t c = 0;
foreach(ref i ; data) {
if (i == '\n') c++;
}
return c;
}
time wc -l ./benchmark.dat
1050 ./benchmark.dat
wc -l ./benchmark.dat 0,06s user 0,03s system 99% cpu 0,089
total
time ./lcs
1050
./lcs 0,05s user 0,01s system 99% cpu 0,061 total
Re: howto count lines - fast
On Tuesday, 30 May 2017 at 23:41:01 UTC, H. S. Teoh wrote:
On Tue, May 30, 2017 at 08:02:38PM +, Nitram via
Digitalmars-d-learn wrote:
After reading
https://dlang.org/blog/2017/05/24/faster-command-line-tools-in-d/ , i was wondering how fast one can do a simple "wc -l" in D.
size_t lineCount3(string filename)
{
import std.mmfile;
auto f = new MmFile(filename);
auto data = cast(ubyte[]) f[];
size_t c;
foreach (i; 0 .. data.length)
{
if (data[i] == '\n') c++;
}
return c;
}
// real0m0.242s
// user0m1.151s
// sys 0m0.057s
You should try something more like
auto data = cast(ulong[]) f[];
foreach (i; 0 .. data.length/ulong.sizeof)
and then using bitfiedling to count the number of \n in the
loaded ulong. This divides by 8 the number of load instructions.
The counting of \n in the loaded word then only uses registers.
It is also possible to use bit fiddling to detect and count the
characters in that ulong. I don't know if it is really faster then
Here a function to detect if a given character is in an ulong
auto detect(alias CHAR)(ulong t)
{
enum ulong u = CHAR;
enum mask1 = u|(u<<8)|(u<<16)|(u<<24UL)|(u<<32UL);
enum mask = (mask1<<32)|mask1;
return ((t^mask) - 0x0101010101010101UL) & ~(t^mask) &
0x8080808080808080UL;
}
The returned value is 0 if the character is not in t. And the
highest bit of each byte is set if it contained the character. If
the CPU has a fast popcnt it should be easy to count.
Re: howto count lines - fast
On Tue, May 30, 2017 at 05:13:46PM -0700, Ali Çehreli via Digitalmars-d-learn wrote: [...] > I could not make the D program come close to wc's performance when the > data was piped from stdin. [...] Hmm. This is a particularly interesting case, because I adapted some of my algorithms to handle reading from stdin (i.e., std.mmfile is not an option), and I could not get it to reach wc's performance! I even installed ldc just to see if that made a difference... it was somewhat faster than gdc, but still, the timings were about twice as slow as wc. I did some digging around, and it seems that wc is using glibc's memchr, which is highly-optimized, whereas std.algorithm.count just uses a simplistic loop. Which is strange, because I'm pretty sure somebody optimized std.algorithm some time ago to use memchr() instead of a loop when searching for a byte value in an array. Whatever happened to that?? T -- Sometimes the best solution to morale problems is just to fire all of the unhappy people. -- despair.com
Re: howto count lines - fast
On Wednesday, 31 May 2017 at 17:23:46 UTC, Ali Çehreli wrote: On 05/30/2017 11:50 PM, Daniel Kozak via Digitalmars-d-learn wrote: > How do you compile it? When I use ldc2 -O3 -release -mcpu=bdver1 lc.d > my code is even faster than wc My bad: I'm not familiar with ldc's optimization options. (I used -O3 but not -release) Now I get the same performance as 'wc -l' when I add -release. Ali It seems to me that your initial result is more interesting: you manage to get faster than wc *while keeping bound safety*. At a time where safety is finally getting the importance it should always have had showing that you can write fast code without sacrifiying any of it is important I think.
Re: howto count lines - fast
On 05/30/2017 11:50 PM, Daniel Kozak via Digitalmars-d-learn wrote: > How do you compile it? When I use ldc2 -O3 -release -mcpu=bdver1 lc.d > my code is even faster than wc My bad: I'm not familiar with ldc's optimization options. (I used -O3 but not -release) Now I get the same performance as 'wc -l' when I add -release. Ali
Re: howto count lines - fast
Dne 31.5.2017 v 02:13 Ali Çehreli via Digitalmars-d-learn napsal(a): I could not make the D program come close to wc's performance when the data was piped from stdin. A typical run with Daniel Kozak's program: $ time cat deleteme.txt | wc -l 5062176 real0m0.086s user0m0.068s sys0m0.048s $ time cat deleteme.txt | ./deneme 5062176 real0m0.174s user0m0.152s sys0m0.072s Ali How do you compile it? When I use ldc2 -O3 -release -mcpu=bdver1 lc.d my code is even faster than wc
Re: howto count lines - fast
On Tuesday, 30 May 2017 at 23:41:01 UTC, H. S. Teoh wrote: This little challenge piqued my interest. So I decided to take a shot at seeing if I could beat my system's /usr/bin/wc -l. First order of business: whenever it comes to performance, always choose the right compiler for the job... Woohoo!!! Finally, we beat the system's wc -l!! And by a pretty fair margin, too. Eat your heart out, wc!!! (The large user time is because we're using all 6 cores at once. But the actual elapsed time is shorter.) Hm... I cheated a little bit: took your program and compiled with `ldc2 -release -O3 -mcpu=skylake`. For data I took std.datetime concatenated 1000 times (35446000 lines). Results (minimum of 10 runs each): wc -l real 0.50 user 0.40 sys 0.09 lineCount1 real 0.23 user 0.19 sys 0.04 lineCount2 real 0.29 user 0.17 sys 0.12 lineCount3 real 0.23 user 0.18 sys 0.04 lineCount4 real 0.22 user 1.52 sys 0.04 Seems like all of them beat wc, so machine and compiler matter a great deal in this comparison. Thing is, on small files wc is going to win pretty much always, due to D's clunky startup. But with larger ones - wc falls far behind. Interestingly, both lineCount1 and lineCount3 on this machine are nearly even with lineCount4 :)
Re: howto count lines - fast
P.S. After I posted the code, I took a closer look at the disassembly
and found that gdc wasn't generating the best code for the parallel
foreach loop body. I haven't fully traced the cause yet, but I did find
a simple optimization (arguably a micro-optimization): updating the
subtotal inside the inner loop is a bit inefficient, because the
subtotal is outside the loop body and, due to the way std.parallelism is
implemented, is passed by reference to the loop body. This caused gdc
not to enregister the subtotal, so incrementing it requires a cache
roundtrip at best, a full RAM roundtrip at worst.
So I introduced a local count variable for incrementing, and only assign
to the subtotal array at the end of the block:
---snip---
// real0m0.240s
// user0m1.045s
// sys 0m0.059s
size_t lineCount4(string filename)
{
import std.algorithm.comparison : min;
import std.algorithm.iteration : sum;
import std.mmfile;
import std.parallelism;
auto f = new MmFile(filename);
auto data = cast(ubyte[]) f[];
size_t[] subtotals;
enum blockSize = 16384;
if (data.length == 0) return 0;
subtotals.length = 1 + (data.length - 1) / blockSize;
foreach (j, ref subtotal; parallel(subtotals))
{
size_t end = min(blockSize*(j+1), data.length);
size_t c = 0;
foreach (i; blockSize*j .. end)
{
if (data[i] == '\n') c++;
}
subtotal = c;
}
return subtotals.sum;
}
---snip---
A look at the disassembly showed that gdc has enregistered the subtotal,
and thereby able to eliminate a branch from the inner loop using a sete
instruction and an add for the if statement. The measured performance
is only marginally better, though, and doesn't change the overall
performance in a significant way. But I thought this might be a useful
tip for people who want to squeeze out the last drop of juice from their
CPUs. ;-)
Next, I'll have to look into why gdc didn't unroll the inner loop, like
I thought it would. But I'm out of time now, so that will have to wait
for another day.
T
--
The peace of mind---from knowing that viruses which exploit Microsoft system
vulnerabilities cannot touch Linux---is priceless. -- Frustrated system
administrator.
Re: howto count lines - fast
On 05/30/2017 01:02 PM, Nitram wrote: After reading https://dlang.org/blog/2017/05/24/faster-command-line-tools-in-d/ , i was wondering how fast one can do a simple "wc -l" in D. So i made a couple short implementations and found myself confronted with slow results compared to "/usr/bin/wc -l". How would a implementation look like in D, which is fast? I could not make the D program come close to wc's performance when the data was piped from stdin. A typical run with Daniel Kozak's program: $ time cat deleteme.txt | wc -l 5062176 real0m0.086s user0m0.068s sys 0m0.048s $ time cat deleteme.txt | ./deneme 5062176 real0m0.174s user0m0.152s sys 0m0.072s Ali
Re: howto count lines - fast
On Tue, May 30, 2017 at 08:02:38PM +, Nitram via Digitalmars-d-learn wrote:
> After reading
> https://dlang.org/blog/2017/05/24/faster-command-line-tools-in-d/ , i
> was wondering how fast one can do a simple "wc -l" in D.
>
> So i made a couple short implementations and found myself confronted
> with slow results compared to "/usr/bin/wc -l".
>
> How would a implementation look like in D, which is fast?
This little challenge piqued my interest. So I decided to take a shot
at seeing if I could beat my system's /usr/bin/wc -l.
First order of business: whenever it comes to performance, always choose
the right compiler for the job. While dmd is the reference compiler and
has the latest and greatest features, it doesn't do too well in the
performance department. So the first thing I did was to use gdc
instead. Specifically, gdc -O3, to get the most aggressive optimizations
the gcc backend can give me.
Second order of business: the naïve algorithm of reading 1 character at
a time from the file is obviously suboptimal, so I didn't even consider
it.
Third order of business: I constructed a large text file containing
10634420 lines by concatenating 5 copies of a large CSV file I obtained
online. The sample had to be large enough so that overhead like program
startup times and background system noise don't bias the test results
too much.
With this setup, I measured the performance of my system's wc -l as the
baseline for comparing the performance of the D solutions. Here's a
typical output of my shell's `time wc -l` command:
real0m0.344s
user0m0.153s
sys 0m0.191s
Since we're talking about beating wc, and others have already posted
the obvious solutions of loading into a buffer and scanning the buffer,
the most obvious next step up is to use std.mmfile to memory-map the
file so that we don't waste effort managing file buffers ourselves: let
the OS do it for us.
So here are the algorithms I tried:
1) lineCount1(): use std.mmfile to memory-map the file so that we can
scan it as a single, contiguous array without any fussy
buffer-management details. Result: pretty fast, but still loses to wc.
Typical measurements:
real0m0.422s
user0m0.366s
sys 0m0.054s
2) lineCount2(): just as a comparison, I did a read-into-buffer
algorithm so that we have a reference as to how it performs. As
expected, it's worse than the std.mmfile solution. Typical measurements:
real0m0.519s
user0m0.320s
sys 0m0.198s
3) lineCount3(): In lineCount1(), I used std.algorithm.searching.count
for counting newlines; so I thought, what if the range abstraction is
introducing too much overhead? So I decided to write a simple foreach
loop instead, in hope that the simpler code will allow the gcc optimizer
do a better job. Sadly, the result is pretty much the same as
lineCount1:
real0m0.421s
user0m0.379s
sys 0m0.042s
(The good news, though, is that this shows that using std.algorithm does
not introduce excessive overhead compared to a hand-written loop. This
proves that the high-level range abstraction does not necessarily equate
to slower performance.)
4) lineCount4(): Having failed to beat wc thus far, it's time to bring
out the big guns. Since we're essentially counting newlines in the
input, who says we have to process the data sequentially from start to
end? Counting from front to back or back to front gives the same
answer, as does counting from middle to end, then front to middle. In
particular, counting from middle to end *in parallel* with counting from
front to middle, then summing the subtotals, gives the same answer.
I.e., time to bust out std.parallelism. :-) So, in my 4th algorithm, I
split the input into 16KB chunks, and then scan them for newlines in
parallel, creating file_size/16384 subtotals. Then I sum the subtotals
in the end. Here's the result, tested on my AMD Phenom 6-core CPU:
real0m0.242s
user0m1.151s
sys 0m0.057s
Woohoo!!! Finally, we beat the system's wc -l!! And by a pretty fair
margin, too. Eat your heart out, wc!!! (The large user time is because
we're using all 6 cores at once. But the actual elapsed time is
shorter.)
Here's the code for all of the above:
-snip-
import std.stdio;
// real0m0.422s
// user0m0.366s
// sys 0m0.054s
size_t lineCount1(string filename)
{
import std.algorithm.searching : count;
import std.mmfile;
auto f = new MmFile(filename);
auto data = cast(ubyte[]) f[];
return data.count('\n');
}
// real0m0.519s
// user0m0.320s
// sys 0m0.198s
size_t lineCount2(string filename)
{
import std.algorithm.searching : count;
auto f = File(filename);
ubyte[] buf;
size_t c = 0;
buf.length = 32768;
do
{
auto d = f.rawRead(buf);
c += d.count('\n');
} while (!f.eof());
return c;
}
// real
Re: howto count lines - fast
I do not know this is my first attempt and it is almost same fast as wc
on my pc:
int main(string[] args)
{
import std.stdio : writeln, writefln, File;
import std.array : uninitializedArray;
auto f = File("data");
size_t c = 0;
auto buffer = uninitializedArray!(ubyte[])(1024);
foreach (chunk; f.byChunk(buffer))
{
foreach (ch; chunk)
if (ch == '\n')
++c;
}
writeln(c);
return 0;
}
And I belive it could be faster when iopipe is used
Dne 30.5.2017 v 22:02 Nitram via Digitalmars-d-learn napsal(a):
After reading
https://dlang.org/blog/2017/05/24/faster-command-line-tools-in-d/ , i
was wondering how fast one can do a simple "wc -l" in D.
So i made a couple short implementations and found myself confronted
with slow results compared to "/usr/bin/wc -l".
How would a implementation look like in D, which is fast?
Re: howto count lines - fast
On Tuesday, 30 May 2017 at 20:37:44 UTC, Jordan Wilson wrote:
On Tuesday, 30 May 2017 at 20:02:38 UTC, Nitram wrote:
After reading
https://dlang.org/blog/2017/05/24/faster-command-line-tools-in-d/ , i was wondering how fast one can do a simple "wc -l" in D.
So i made a couple short implementations and found myself
confronted with slow results compared to "/usr/bin/wc -l".
How would a implementation look like in D, which is fast?
Not sure if this is the fastest, but anyway
void main(){
import std.file : read;
import std.conv : to;
import std.algorithm : count;
auto data = cast(ubyte[])read("somefile.txt");
auto lc = data.count('\n'.to!ubyte);
}
Jordan
I should say, if you don't care about storing the data,
File("somefile.txt","r").byLine.count is probably more idiomatic,
and probably just as fast.
Jordan
Re: howto count lines - fast
On Tuesday, 30 May 2017 at 20:02:38 UTC, Nitram wrote:
After reading
https://dlang.org/blog/2017/05/24/faster-command-line-tools-in-d/ , i was wondering how fast one can do a simple "wc -l" in D.
So i made a couple short implementations and found myself
confronted with slow results compared to "/usr/bin/wc -l".
How would a implementation look like in D, which is fast?
Not sure if this is the fastest, but anyway
void main(){
import std.file : read;
import std.conv : to;
import std.algorithm : count;
auto data = cast(ubyte[])read("somefile.txt");
auto lc = data.count('\n'.to!ubyte);
}
Jordan
howto count lines - fast
After reading https://dlang.org/blog/2017/05/24/faster-command-line-tools-in-d/ , i was wondering how fast one can do a simple "wc -l" in D. So i made a couple short implementations and found myself confronted with slow results compared to "/usr/bin/wc -l". How would a implementation look like in D, which is fast?
