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digitalmars.D.learn - howto count lines - fast

reply Nitram <martin.brzenska googlemail.com> writes:
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?
May 30
next sibling parent reply Jordan Wilson <wilsonjord gmail.com> writes:
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
May 30
parent Jordan Wilson <wilsonjord gmail.com> writes:
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
May 30
prev sibling next sibling parent Daniel Kozak via Digitalmars-d-learn <digitalmars-d-learn puremagic.com> writes:
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?
May 30
prev sibling next sibling parent reply "H. S. Teoh via Digitalmars-d-learn" <digitalmars-d-learn puremagic.com> writes:
On Tue, May 30, 2017 at 08:02:38PM +0000, 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 nave 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: real 0m0.344s user 0m0.153s 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: real 0m0.422s user 0m0.366s 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: real 0m0.519s user 0m0.320s 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: real 0m0.421s user 0m0.379s 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: real 0m0.242s user 0m1.151s 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; // real 0m0.422s // user 0m0.366s // 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'); } // real 0m0.519s // user 0m0.320s // 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 0m0.421s // user 0m0.379s // sys 0m0.042s 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; } // real 0m0.242s // user 0m1.151s // sys 0m0.057s size_t lineCount4(string filename) { import std.algorithm.comparison : min; import std.algorithm.iteration : sum; import std.mmfile; import std.parallelism; import std.range : chunks; 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); foreach (i; blockSize*j .. end) { if (data[i] == '\n') subtotal++; } } return subtotals.sum; } int main(string[] args) { if (args.length < 2) { stderr.writeln("Please specify target file."); return 1; } auto filename = args[1]; //auto count = lineCount1(filename); //auto count = lineCount2(filename); //auto count = lineCount3(filename); auto count = lineCount4(filename); writeln(count); return 0; } // vim:set sw=4 ts=4 et ai: ---------snip--------- T -- BREAKFAST.COM halted...Cereal Port Not Responding. -- YHL
May 30
next sibling parent Stanislav Blinov <stanislav.blinov gmail.com> writes:
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 :)
May 30
prev sibling parent reply Patrick Schluter <Patrick.Schluter bbox.fr> writes:
On Tuesday, 30 May 2017 at 23:41:01 UTC, H. S. Teoh wrote:
 On Tue, May 30, 2017 at 08:02:38PM +0000, 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; } // real 0m0.242s // user 0m1.151s // 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.
May 31
parent Nitram <martin.brzenska googlemail.com> writes:
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
10500000 ./benchmark.dat
wc -l ./benchmark.dat  0,06s user 0,03s system 99% cpu 0,089 
total
time ./lcs
10500000
./lcs  0,05s user 0,01s system 99% cpu 0,061 total
May 31
prev sibling next sibling parent reply =?UTF-8?Q?Ali_=c3=87ehreli?= <acehreli yahoo.com> writes:
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 real 0m0.086s user 0m0.068s sys 0m0.048s $ time cat deleteme.txt | ./deneme 5062176 real 0m0.174s user 0m0.152s sys 0m0.072s Ali
May 30
next sibling parent reply Daniel Kozak via Digitalmars-d-learn <digitalmars-d-learn puremagic.com> writes:
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

 real    0m0.086s
 user    0m0.068s
 sys    0m0.048s

 $ time cat deleteme.txt | ./deneme
 5062176

 real    0m0.174s
 user    0m0.152s
 sys    0m0.072s

 Ali
How do you compile it? When I use ldc2 -O3 -release -mcpu=bdver1 lc.d my code is even faster than wc
May 30
parent reply =?UTF-8?Q?Ali_=c3=87ehreli?= <acehreli yahoo.com> writes:
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
May 31
parent cym13 <cpicard openmailbox.org> writes:
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.
May 31
prev sibling next sibling parent "H. S. Teoh via Digitalmars-d-learn" <digitalmars-d-learn puremagic.com> writes:
On Tue, May 30, 2017 at 05:13:46PM -0700, Ali ehreli 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
May 31
prev sibling next sibling parent Jonathan M Davis via Digitalmars-d-learn writes:
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
May 31
prev sibling next sibling parent reply "H. S. Teoh via Digitalmars-d-learn" <digitalmars-d-learn puremagic.com> writes:
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 nave 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 nave 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 nave loop; for large arrays where the match is not found until later, it easily outperforms the nave 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 nave loops; for sparsely-matching arrays this should show significant speedups. T -- My program has no bugs! Only unintentional features...
May 31
parent Patrick Schluter <Patrick.Schluter bbox.fr> writes:
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&curpostid=168700
May 31
prev sibling parent "H. S. Teoh via Digitalmars-d-learn" <digitalmars-d-learn puremagic.com> writes:
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 ehreli 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 & 0x00000000000000FF) == eol ) subtotal++; if ((i & 0x000000000000FF00) == (eol << 8)) subtotal++; if ((i & 0x0000000000FF0000) == (eol << 16)) subtotal++; if ((i & 0x00000000FF000000) == (eol << 24)) subtotal++; if ((i & 0x000000FF00000000) == (eol << 32)) subtotal++; if ((i & 0x0000FF0000000000) == (eol << 40)) subtotal++; if ((i & 0x00FF000000000000) == (eol << 48)) subtotal++; if ((i & 0xFF00000000000000) == (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 nave 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 | real 0m0.475s | real 0m0.080s | real 0m0.083s | | | user 0m0.417s | user 0m0.034s | user 0m0.062s | | | sys 0m0.058s | sys 0m0.046s | sys 0m0.020s | +-----------+------------------+-------------------+------------------+ | linecount | real 0m0.181s | real 0m0.190s | real 0m0.099s | | | user 0m0.138s | user 0m0.129s | user 0m0.059s | | | sys 0m0.042s | sys 0m0.060s | sys 0m0.040s | +-----------+------------------+-------------------+------------------+ As expected, wc -l loses when dealing with blank lines (and, presumably, short lines); the D version was able to beat it by more than a factor of 2. On the file with 100-byte lines, though, the performance of wc improved tremendously, because glibc's memchr is optimized for scanning large amounts of data before finding a match, whereas the D version performs more-or-less on par with the blank line case, but losing out to wc by about a factor of 2. The results of the 10x Phobos code runs are not directly comparable with the first two test files, because the total file size is different. Here, the D code still loses out slightly to wc, presumably because memchr is ultimately still more efficient given the average line length in Phobos code. These results show that the performance of these algorithms depend on the kind of data you feed them, and there's probably no "optimal" line-counting algorithm unless you can predict the average line lengths in advance. In general, though, if you're dealing with text, I'd wager that the average line length should be closer to the 100-byte lines end of the extreme than the file filled with blank lines, so wc probably wins on your typical text file. I guess that means that using memchr or its equivalent in D would be the best strategy to obtain results on par with wc, as far as reading from stdin is concerned. When you can use std.mmfile, though, the D version still beats wc by a large margin. :-D T -- Help a man when he is in trouble and he will remember you when he is in trouble again.
Jun 01
prev sibling next sibling parent "H. S. Teoh via Digitalmars-d-learn" <digitalmars-d-learn puremagic.com> writes:
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-------
// real    0m0.240s
// user    0m1.045s
// 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.
May 30
prev sibling next sibling parent Russel Winder via Digitalmars-d-learn <digitalmars-d-learn puremagic.com> writes:
On Tue, 2017-05-30 at 17:22 -0700, H. S. Teoh via Digitalmars-d-learn
wrote:
 [=E2=80=A6]
 performance in a significant way.=C2=A0=C2=A0But I thought this might be =
a
 useful
 tip for people who want to squeeze out the last drop of juice from
 their
 CPUs. ;-)
=20
[=E2=80=A6] 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".=20 --=20 Russel. =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D Dr Russel Winder t: +44 20 7585 2200 voip: sip:russel.winder ekiga.n= et 41 Buckmaster Road m: +44 7770 465 077 xmpp: russel winder.org.uk London SW11 1EN, UK w: www.russel.org.uk skype: russel_winder
May 31
prev sibling next sibling parent "H. S. Teoh via Digitalmars-d-learn" <digitalmars-d-learn puremagic.com> writes:
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 / nave 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 nave 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.
May 31
prev sibling next sibling parent reply Jonathan M Davis via Digitalmars-d-learn writes:
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 nave 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 nave 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 nave loop; for large arrays where the match is not found until later, it easily outperforms the nave 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 nave 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
May 31
parent reply Patrick Schluter <Patrick.Schluter bbox.fr> writes:
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 10000 cycles before AVX switches from 128 bits to 256 bits operations), reduced turboing, etc.).
May 31
next sibling parent Jonathan M Davis via Digitalmars-d-learn writes:
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 10000 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
May 31
prev sibling parent "H. S. Teoh via Digitalmars-d-learn" <digitalmars-d-learn puremagic.com> writes:
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
 10000 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 nave 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 nave 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.
May 31
prev sibling next sibling parent Russel Winder via Digitalmars-d-learn <digitalmars-d-learn puremagic.com> writes:
On Wed, 2017-05-31 at 16:37 -0700, H. S. Teoh via Digitalmars-d-learn
wrote:
 [=E2=80=A6]
=20
 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.
=20
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.=20 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. =20 --=20 Russel. =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D Dr Russel Winder t: +44 20 7585 2200 voip: sip:russel.winder ekiga.n= et 41 Buckmaster Road m: +44 7770 465 077 xmpp: russel winder.org.uk London SW11 1EN, UK w: www.russel.org.uk skype: russel_winder
Jun 01
prev sibling next sibling parent Jonathan M Davis via Digitalmars-d-learn writes:
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 nave 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
Jun 01
prev sibling next sibling parent Russel Winder via Digitalmars-d-learn <digitalmars-d-learn puremagic.com> writes:
On Wed, 2017-05-31 at 16:37 -0700, H. S. Teoh via Digitalmars-d-learn
wrote:
=20
[=E2=80=A6]
 With D, we can have the cake and eat it too.=C2=A0=C2=A0The understandabl=
e /
 na=C3=AFve
 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".=C2=A0=C2=A0There 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 na=C3=AFve 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.=C2=A0=C2=A0When 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. --=20 Russel. =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D Dr Russel Winder t: +44 20 7585 2200 voip: sip:russel.winder ekiga.n= et 41 Buckmaster Road m: +44 7770 465 077 xmpp: russel winder.org.uk London SW11 1EN, UK w: www.russel.org.uk skype: russel_winder
Jun 01
prev sibling next sibling parent "H. S. Teoh via Digitalmars-d-learn" <digitalmars-d-learn puremagic.com> writes:
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 algorithms in an optimal way so that it will continue to be useful for non-trivial problem sizes. You cannot just say, "here is the problem specification, now just let the computer / AI system / whatever figure out for themselves how to obtain the answer". *Somebody* has to actually sit down and specify exactly how to compute the answer, because generic ways of arriving at the answer are exponential or worse and are therefore useless. And even a feasible algorithm may require a lot of "heroics" in order to make medium-sized problems more tractable. You want your weather forecasting model to produce an answer by tomorrow before the 10am news, not 3 months later when the answer is no longer relevant.
 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.
Some problems are inherently hard, and no amount of searching can reduce its complexity past a certain limit. These require extreme measures to be even remotely tractable.
 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.
[...] 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. 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. T -- Question authority. Don't ask why, just do it.
Jun 02
prev sibling next sibling parent Russel Winder via Digitalmars-d-learn <digitalmars-d-learn puremagic.com> writes:
On Fri, 2017-06-02 at 10:32 -0700, H. S. Teoh via Digitalmars-d-learn
wrote:
 [=E2=80=A6]
=20
 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=E2=84=A2 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.
 [=E2=80=A6]
 be able to generate optimal code in all cases. There will always be
 cases where you have to manually tweak it yourself.=C2=A0=C2=A0Of 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).=C2=A0=C2=A0Then 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.
=20
[=E2=80=A6]
 But *somebody* has to implement those computational models and
 programming languages.=C2=A0=C2=A0If nobody knows how to write "heroic" c=
ode,
 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.=20 --=20 Russel. =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D Dr Russel Winder t: +44 20 7585 2200 voip: sip:russel.winder ekiga.n= et 41 Buckmaster Road m: +44 7770 465 077 xmpp: russel winder.org.uk London SW11 1EN, UK w: www.russel.org.uk skype: russel_winder
Jun 02
prev sibling parent "H. S. Teoh via Digitalmars-d-learn" <digitalmars-d-learn puremagic.com> writes:
On Sat, Jun 03, 2017 at 07:00:47AM +0100, Russel Winder via Digitalmars-d-learn
wrote:
[...]
 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.
Fair enough. I can see how this would lead to unnecessarily ugly, prematurely-optimized code. It's probably the origin of premature optimization culture especially prevalent in C circles, where you just get into the habit of automatically thinking things like "i = i + 1 is less efficient than ++i", which may have been true in some bygone era but is no longer relevant in the machines and optimizing compilers of today. And also, constantly "optimizing" code that actually aren't the bottleneck, because of some vague notion of wanting "everything" to be fast, yet not being willing to use a profiler to find out where the real bottleneck is. As a result you spend inordinate amounts of time writing the absolutestly fastest O(n^2) algorithm rather than substituting a moderately unoptimized O(n) algorithm that's far superior, thus actually introducing new bottlenecks instead of fixing existing ones.
 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.
Well, there is some value in targeting a specific execution environment, but I agree that holding that up as being exemplary of how code should be written would be rather misguided. [...]
 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.
I've been thinking about this for a while now, actually. It almost seems as though there ought to be two distinct layers of abstraction in a given piece of code, a high-level, logical layer that specifies using some computation model the desired results, and another lower-level layer that contains either implementation details or target-specific tweaks. There should be an automatic translation from the upper layer to the lower layer, but after the automatic translation you can go in and tweak the lower layer *while keeping the upper layer intact*, and the system (IDE or whatever) would keep track of *both*, with the lower layer customizations tracked as a set of diffs against the automatically translated version. When the upper layer changes, any corresponding diffs in the lower layer get invalidated and either produces a conflict the programmer must manually resolve, or else defaults to the new automated translation. Furthermore, there should be some system of tracking multiple diff sets for the lower layer, so that you can specify diff A as applying to target machine X, and diff B as applying to target machine Y. So you can target the same logical piece of code to different target machines with different implementations. [...]
 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.
[...] Yes, and that is why the people who actually know what they're doing need to be able to write the "hackish", optimized implementations of the nicer APIs provided by the language / system, so that at the very least the API calls would do something sane, even if the code above that is crap. T -- Questions are the beginning of intelligence, but the fear of God is the beginning of wisdom.
Jun 02