12 ms·

This highlights, for me, one of the reasons why our obsession with "human readable" serialization formats is so misplaced. You're burning up a lot of CPU power

This highlights, for me, one of the reasons why our obsession with "human readable" serialization formats is so misplaced. You're burning up a lot of CPU power here, potentially creating a synchronization point, just to do something that could be addressed with a simple 4 or 8 byte memcpy.

taneq 7y ago
The thing is, "human readable" files aren't, once they get to a nontrivial size. Picking apart a megabyte XML file in a text editor is arguably harder than the same data as a blob in a hex editor. In fact, thinking about it now, I bet Wireshark would be awesome for that.
- otabdeveloper3 7y ago
  "Human readable" means "forward-compatible and debugabble without specialized tools". Humans aren't actually reading these things.
- renox 7y ago
  ??? Even integers are difficult to read in an hex editors thanks to little endianness..
  - taneq 7y ago
    Eh, once you're used to reading integers in hexadecimal, byte order isn't that important. If I told you that 1234 was going to be written right to left, you'd know how big 4321 was, right?
fluffything 7y ago
> that could be addressed with a simple 4 or 8 byte memcpy. This won't account for endianness.
- jgtrosh 7y ago
  Floating point formatting deals only with the default endianness of the hardware though. Afaik if you need to deal with that you have to do it yourself regardless (with bswap functions or similar).
- pedrocr 7y ago
  In a serialization format you should explicitly define the endianness to be used and then convert to and from it explicitly in the serialize/deserialize code. So memcpy is wrong but the grandparent point still stands. Defining a serialization format for float/double that's just 4/8 bytes in little-endian might be a better idea than the mess we sometimes have right now because IEE-754 is more of a standard than anything else in the space.
- userbinator 7y ago
  Fortunately much of the world has already settled on an endianness, and the one that happens to be most logical to process (byte offset n has weight 256^n, much like bit x has weight 2^x.) In the future, hopefully big-endian machines will be as uncommon as bytes that are not 8 bits, and the only protocols still using it are the ones which were designed long ago.
lelf 7y ago
“Something in the middle” could be an option. E. g. (in C) > printf ("%a\n", M_PI); 0x1.921fb54442d18p+1 > printf ("%a\n", 42.0); 0x1.5p+5
- 0815test 7y ago
  This should be a lot higher. Hexfloats are not just a C/C++ thing, plenty of languages and software packages support them by now. And they can very much be used as part of a text input format, to avoid the sizeable overhead of "human-friendly" float parsing. If you maintain a text-based format that has any chance of being a CPU/compute bottleneck in this way, you should definitely make sure that hexfloats are supported! Edit: someone else mentioned that roundtrip accuracy could also be an issue. Theoretically true, I guess, but really, if you want to use decimal-based float input you should just use an algorithm that roundtrips correctly, and eat that overhead. Anything else has a potential to impact reproducibility of results, etc. It's not worth it silently corrupting your data just for that saving in compute cost.
- brandmeyer 7y ago
  Hear, hear! Sometimes you do need to reliably communicate a floating-point number across a text channel. Hex float is low-risk in the sense that both the encoding and decoding process are much simpler and therefore easier to verify than something that round-trips through decimal. Yes, there are some correct implementations of the decimal<->binary algorithms that round-trip correctly. But there also remain a number of incorrect implementations.
  - cbsmith 7y ago
    To be clear: human readable formats when you have humans reading the data makes sense. What is insane is having machines reading & writing human readable formats to talk to each other.
tomxor 7y ago
It also creates a lot of misconceptions of how accurate floating point math is. An interesting aspect of decimal formatting is how it frequently masks representation error (i.e encoding of 0.1 etc), because the error is symmetrical in the formatter/parser it makes such non-representable fractions appear to be stored perfectly to unsuspecting users. This can be quite deceptive, if more users were aware of just how many of the simple rational decimals they input were converted into imprecise representations they probably wouldn't trust computers as much as they do. To confuse things more, when operating upon periodic representations the result often matches the representation error of the the equivalent accurate decimal value encoded directly (i.e there were errors, but everything canceled out through formatting) - when they occasionally do not (e.g 0.1 + 0.2) it makes the problem appear all the more elusive. I think this detail is often lost in explanations of 0.1 + 0.2, that is: representation error is extremely common, 0.1 + 0.2 is merely one of the cases where it both persists through the formatter AND you notice it because the inputs were short decimals, and it's so obvious that the output should be a non-periodic decimal.. TL;DR formatting floats to decimals makes us trust floating point math far more than we should - it's healthy to remember that the formatting process is necessarily imprecise and that you are merely looking at a proxy for the underlying value. Remember that next time you look at _seemingly_ non-periodic decimal output.
- bunderbunder 7y ago
  Or perhaps it makes us mistrust more than we should? How often are we working on problems where the difference between 0.1 and 0.100000000000000006 is of any practical importance? When I format a float out to 5 decimal places, I'm sort of making a statement that anything beyond that doesn't matter to me.
  - tomxor 7y ago
    > When I format a float out to 5 decimal places, I'm sort of making a statement that anything beyond that doesn't matter to me. Yes, it's true the vast majority of the time it doesn't actually matter. However decimal formatting does such a good job at giving us the impression that these errors are merely edge-cases, and calculators automatically formatting to 10sf etc further that illusion. If people are not aware it's only an illusion (or just how far the rabbit hole goes) it can be dangerous when they go on to create or use things where that fact matters.
TorKlingberg 7y ago
The "obsession" came about from all the problems with binary formats. Byte ordering. Unspecified signedness. Memcpy:ing structs with padding. Programmers micro-optimizing by choosing the smallest possible field size, and then you can't change it when it turns out to be too small. Format specs using WORD as if that's a well-defined size. Using int, long, etc, and assuming they are the same size everywhere. It's a trade-off of CPU usage versus flexibility. It's not surprising that textual serialization formats took off together with managed memory languages.
- datenwolf 7y ago
  Byte ordering issues are easily addressed by still emitting text, but not converting to a base that differs in a prime factor. Just emit the mantissa and exponent in – say – hexadecimal form and the negative sign bit as a `-`. It avoids all the endianes issues and is trivially converted.
- IshKebab 7y ago
  Rubbish. It's possible to have poorly defined text formats just as easily as binary formats. Remind me how to read CSV again? How do I store 64-bit integers in JSON? Text formats are popular because you don't have to use a special tool to view them. That's the only reason.
  - cbsmith 7y ago
    > Text formats are popular because you don't have to use a special tool to view them. That's the only reason. But you need a special tool to get a computer to read them. Seems like not a win. Throw in compression & encryption... and suddenly you need a special tool anyway.
- cbsmith 7y ago
  Fortunately with text you can't have issues with byte ordering, unspecified signedness, or padding... oh wait. ;-)
bunderbunder 7y ago
I'm also not convinced they help human readability that much. e.g., when I've done my own binary formats, I typically also create a "dump" utility that converts the file to a readable text dump. I find the ergonomics of this are just fine for my purposes. less somefile.json isn't that much easier to type than dumpdat somefile.dat | less That said, different purposes are different. I'm not so worried about human-readability for short-lived stuff, but, if you're talking about data that may be sitting around for decades, then the human readability question becomes something you might better characterize as "future comprehensibility": If someone's trying to dust off 50-year-old business data from deep in the archives, they're going to have a much higher chance of success if it's CSV files than if it's in some custom binary format whose documentation was lost 40 years ago.
roel_v 7y ago
A few years ago I wrote a naive Java string-to-floating point conversion routine that only converted 'simple' numbers, i.e. no scientific notation, only one (fixed) decimal separator, etc. It was the first Java code I had written in 10 years and the approach was probably not very Java-esque (I imitated what I'd do in C++, using (IIRC) java.nio.ByteBuffer or something similar). Anyway this simple implementation was ~10 times faster than the 'officially' suggested way, using formatted string conversion routines, even after asking around (on SO and similar) on how to do high performance string-to-float conversion. There are probably similar issues in other programming languages, this is not to rag on Java; my point is that when you don't need the 'fancy' floating point formatting stuff, and you need high performance (in my case, I had to convert many billions of floating-points-as-strings and the conversion took ~50% of my program's runtime, which was measured in days), it can pay off to write a custom version of something as seemingly mundane as converting a string to a number.
- buckminster 7y ago
  I had the same experience the other way. I halved the runtime of an ETL program by writing a custom float to string function.
- chrchang523 7y ago
  The biggest win, in my experience, is simply recognizing when you don’t need all ~16 significant digits provided by the standard implementations. If you’re only printing e.g. six digits, and can afford to round in the wrong direction 0.00000001% of the time, you can skip a ton of work. And as you mention, it gets even easier when you don’t have to support scientific notation. The standard library functions occupy an awkward middle ground between this type of much faster, slightly sloppy float-to-string converter, and direct storage of the binary representation; in most applications where float serialization performance is actually relevant, at least one of these two alternatives is better.
  - Vindicis 7y ago
    Agreed. I deal with financial data which is in well-defined formats for the various asset classes. You can save a lot of time by just writing some parsing functions to deal with your exact needs and avoid needless work. And, hopefully avoid using the built-in functions for conversions as those will almost always be A LOT slower due to what they're required to handle. I prefer to store that type of data in csv files because there are times when you need to read through the data to find errors, adhoc analysis, etc....If you're loading those types of files very frequently, there are better formats of course. But, when the parsing time is peanuts compared to the runtime of your program, it's nice to be able to load the file into other programs when you need without having to convert it first.
forgottenpass 7y ago
>You're burning up a lot of CPU power here This is easy to write off as micro-optimizing, but it's no joke. I recently profiled some hard real-time software and was surprised to find that >50% of busy processor time was spent somewhere in the fcvt() family of functions. It's fine for the use case in the medium term (we're within deadline and not starving for more cycles) so I'm avoiding the serialization format transition headache, but it did send me down the rabbit hole of floating point conversion techniques for half a day.
FabHK 7y ago
Sometimes I wish one could configure REPLs, for example, to print floats not as the shortest input which maps to that float ("0.1"), but the actual number represented by the float ("0.1000000000000000055511151231257827021181583404541015625") by default. Somewhat annoying, but might avoid a lot of confusion ("Is floating point math broken?", https://stackoverflow.com/questions/588004/is-floating-point-math-broken https://stackoverflow.com/questions/588004/is-floating-point... ).
- klodolph 7y ago
  Would you like 1e-100 to print as 1.00000000000000001999189980260288361964776078853415942018260300593659569925554346761767628861329298958274607481091185079852827053974965402226843604196126360835628314127871794272492894246908066589163059300043457860230145025079449986855914338755579873208034769049845635890960693359375e-100?
  - FabHK 7y ago
    Nice example! Makes sense that it can get quite long, though it was not present in my mind. But, hey, that's what the number represented actually is. As I said, make it optional...
- firethief 7y ago
  IMO hex should be the default representation of binary fp. Printing hex gives the exact value, without giving the illusion of having precision in the hundreds of digits, and can be done way faster than conversion to decimal. >>> 0.1.hex() 0x1.999999999999ap-4 >>> 1e-100.hex() 0x1.bff2ee48e0530p-333
userbinator 7y ago
Indeed, "human readable" really means "in something similar to English" much of the time, and is a vague and quite subjective term; would someone who knows only English think Chinese is not "human readable"? Personally, I consider binary formats to be just as readable (from the hexdump, or sometimes even the ASCII directly) and in some ways even easier to parse without ambiguity --- it just requires a bit (pun intended) of learning, like any language. I've worked with someone who could "read" TCP/IP; and I can read much of Z80 and some x86 Asm as well as a few other binary formats.

taneq 7y ago

The thing is, "human readable" files aren't, once they get to a nontrivial size. Picking apart a megabyte XML file in a text editor is arguably harder than the same data as a blob in a hex editor. In fact, thinking about it now, I bet Wireshark would be awesome for that.

otabdeveloper3 7y ago
"Human readable" means "forward-compatible and debugabble without specialized tools". Humans aren't actually reading these things.
renox 7y ago
??? Even integers are difficult to read in an hex editors thanks to little endianness..
- taneq 7y ago
  Eh, once you're used to reading integers in hexadecimal, byte order isn't that important. If I told you that 1234 was going to be written right to left, you'd know how big 4321 was, right?

fluffything 7y ago

> that could be addressed with a simple 4 or 8 byte memcpy. This won't account for endianness.

jgtrosh 7y ago
Floating point formatting deals only with the default endianness of the hardware though. Afaik if you need to deal with that you have to do it yourself regardless (with bswap functions or similar).
pedrocr 7y ago
In a serialization format you should explicitly define the endianness to be used and then convert to and from it explicitly in the serialize/deserialize code. So memcpy is wrong but the grandparent point still stands. Defining a serialization format for float/double that's just 4/8 bytes in little-endian might be a better idea than the mess we sometimes have right now because IEE-754 is more of a standard than anything else in the space.
userbinator 7y ago
Fortunately much of the world has already settled on an endianness, and the one that happens to be most logical to process (byte offset n has weight 256^n, much like bit x has weight 2^x.) In the future, hopefully big-endian machines will be as uncommon as bytes that are not 8 bits, and the only protocols still using it are the ones which were designed long ago.

lelf 7y ago

“Something in the middle” could be an option. E. g. (in C) > printf ("%a\n", M_PI); 0x1.921fb54442d18p+1 > printf ("%a\n", 42.0); 0x1.5p+5

0815test 7y ago
This should be a lot higher. Hexfloats are not just a C/C++ thing, plenty of languages and software packages support them by now. And they can very much be used as part of a text input format, to avoid the sizeable overhead of "human-friendly" float parsing. If you maintain a text-based format that has any chance of being a CPU/compute bottleneck in this way, you should definitely make sure that hexfloats are supported! Edit: someone else mentioned that roundtrip accuracy could also be an issue. Theoretically true, I guess, but really, if you want to use decimal-based float input you should just use an algorithm that roundtrips correctly, and eat that overhead. Anything else has a potential to impact reproducibility of results, etc. It's not worth it silently corrupting your data just for that saving in compute cost.
brandmeyer 7y ago
Hear, hear! Sometimes you do need to reliably communicate a floating-point number across a text channel. Hex float is low-risk in the sense that both the encoding and decoding process are much simpler and therefore easier to verify than something that round-trips through decimal. Yes, there are some correct implementations of the decimal<->binary algorithms that round-trip correctly. But there also remain a number of incorrect implementations.
- cbsmith 7y ago
  To be clear: human readable formats when you have humans reading the data makes sense. What is insane is having machines reading & writing human readable formats to talk to each other.

tomxor 7y ago

It also creates a lot of misconceptions of how accurate floating point math is. An interesting aspect of decimal formatting is how it frequently masks representation error (i.e encoding of 0.1 etc), because the error is symmetrical in the formatter/parser it makes such non-representable fractions appear to be stored perfectly to unsuspecting users. This can be quite deceptive, if more users were aware of just how many of the simple rational decimals they input were converted into imprecise representations they probably wouldn't trust computers as much as they do. To confuse things more, when operating upon periodic representations the result often matches the representation error of the the equivalent accurate decimal value encoded directly (i.e there were errors, but everything canceled out through formatting) - when they occasionally do not (e.g 0.1 + 0.2) it makes the problem appear all the more elusive. I think this detail is often lost in explanations of 0.1 + 0.2, that is: representation error is extremely common, 0.1 + 0.2 is merely one of the cases where it both persists through the formatter AND you notice it because the inputs were short decimals, and it's so obvious that the output should be a non-periodic decimal.. TL;DR formatting floats to decimals makes us trust floating point math far more than we should - it's healthy to remember that the formatting process is necessarily imprecise and that you are merely looking at a proxy for the underlying value. Remember that next time you look at _seemingly_ non-periodic decimal output.

bunderbunder 7y ago
Or perhaps it makes us mistrust more than we should? How often are we working on problems where the difference between 0.1 and 0.100000000000000006 is of any practical importance? When I format a float out to 5 decimal places, I'm sort of making a statement that anything beyond that doesn't matter to me.
- tomxor 7y ago
  > When I format a float out to 5 decimal places, I'm sort of making a statement that anything beyond that doesn't matter to me. Yes, it's true the vast majority of the time it doesn't actually matter. However decimal formatting does such a good job at giving us the impression that these errors are merely edge-cases, and calculators automatically formatting to 10sf etc further that illusion. If people are not aware it's only an illusion (or just how far the rabbit hole goes) it can be dangerous when they go on to create or use things where that fact matters.

TorKlingberg 7y ago

The "obsession" came about from all the problems with binary formats. Byte ordering. Unspecified signedness. Memcpy:ing structs with padding. Programmers micro-optimizing by choosing the smallest possible field size, and then you can't change it when it turns out to be too small. Format specs using WORD as if that's a well-defined size. Using int, long, etc, and assuming they are the same size everywhere. It's a trade-off of CPU usage versus flexibility. It's not surprising that textual serialization formats took off together with managed memory languages.

datenwolf 7y ago
Byte ordering issues are easily addressed by still emitting text, but not converting to a base that differs in a prime factor. Just emit the mantissa and exponent in – say – hexadecimal form and the negative sign bit as a `-`. It avoids all the endianes issues and is trivially converted.
IshKebab 7y ago
Rubbish. It's possible to have poorly defined text formats just as easily as binary formats. Remind me how to read CSV again? How do I store 64-bit integers in JSON? Text formats are popular because you don't have to use a special tool to view them. That's the only reason.
- cbsmith 7y ago
  > Text formats are popular because you don't have to use a special tool to view them. That's the only reason. But you need a special tool to get a computer to read them. Seems like not a win. Throw in compression & encryption... and suddenly you need a special tool anyway.
cbsmith 7y ago
Fortunately with text you can't have issues with byte ordering, unspecified signedness, or padding... oh wait. ;-)

bunderbunder 7y ago

I'm also not convinced they help human readability that much. e.g., when I've done my own binary formats, I typically also create a "dump" utility that converts the file to a readable text dump. I find the ergonomics of this are just fine for my purposes. less somefile.json isn't that much easier to type than dumpdat somefile.dat | less That said, different purposes are different. I'm not so worried about human-readability for short-lived stuff, but, if you're talking about data that may be sitting around for decades, then the human readability question becomes something you might better characterize as "future comprehensibility": If someone's trying to dust off 50-year-old business data from deep in the archives, they're going to have a much higher chance of success if it's CSV files than if it's in some custom binary format whose documentation was lost 40 years ago.

roel_v 7y ago

A few years ago I wrote a naive Java string-to-floating point conversion routine that only converted 'simple' numbers, i.e. no scientific notation, only one (fixed) decimal separator, etc. It was the first Java code I had written in 10 years and the approach was probably not very Java-esque (I imitated what I'd do in C++, using (IIRC) java.nio.ByteBuffer or something similar). Anyway this simple implementation was ~10 times faster than the 'officially' suggested way, using formatted string conversion routines, even after asking around (on SO and similar) on how to do high performance string-to-float conversion. There are probably similar issues in other programming languages, this is not to rag on Java; my point is that when you don't need the 'fancy' floating point formatting stuff, and you need high performance (in my case, I had to convert many billions of floating-points-as-strings and the conversion took ~50% of my program's runtime, which was measured in days), it can pay off to write a custom version of something as seemingly mundane as converting a string to a number.

buckminster 7y ago
I had the same experience the other way. I halved the runtime of an ETL program by writing a custom float to string function.
chrchang523 7y ago
The biggest win, in my experience, is simply recognizing when you don’t need all ~16 significant digits provided by the standard implementations. If you’re only printing e.g. six digits, and can afford to round in the wrong direction 0.00000001% of the time, you can skip a ton of work. And as you mention, it gets even easier when you don’t have to support scientific notation. The standard library functions occupy an awkward middle ground between this type of much faster, slightly sloppy float-to-string converter, and direct storage of the binary representation; in most applications where float serialization performance is actually relevant, at least one of these two alternatives is better.
- Vindicis 7y ago
  Agreed. I deal with financial data which is in well-defined formats for the various asset classes. You can save a lot of time by just writing some parsing functions to deal with your exact needs and avoid needless work. And, hopefully avoid using the built-in functions for conversions as those will almost always be A LOT slower due to what they're required to handle. I prefer to store that type of data in csv files because there are times when you need to read through the data to find errors, adhoc analysis, etc....If you're loading those types of files very frequently, there are better formats of course. But, when the parsing time is peanuts compared to the runtime of your program, it's nice to be able to load the file into other programs when you need without having to convert it first.

forgottenpass 7y ago

>You're burning up a lot of CPU power here This is easy to write off as micro-optimizing, but it's no joke. I recently profiled some hard real-time software and was surprised to find that >50% of busy processor time was spent somewhere in the fcvt() family of functions. It's fine for the use case in the medium term (we're within deadline and not starving for more cycles) so I'm avoiding the serialization format transition headache, but it did send me down the rabbit hole of floating point conversion techniques for half a day.

FabHK 7y ago

Sometimes I wish one could configure REPLs, for example, to print floats not as the shortest input which maps to that float ("0.1"), but the actual number represented by the float ("0.1000000000000000055511151231257827021181583404541015625") by default. Somewhat annoying, but might avoid a lot of confusion ("Is floating point math broken?", https://stackoverflow.com/questions/588004/is-floating-point-math-broken https://stackoverflow.com/questions/588004/is-floating-point... ).

klodolph 7y ago
Would you like 1e-100 to print as 1.00000000000000001999189980260288361964776078853415942018260300593659569925554346761767628861329298958274607481091185079852827053974965402226843604196126360835628314127871794272492894246908066589163059300043457860230145025079449986855914338755579873208034769049845635890960693359375e-100?
- FabHK 7y ago
  Nice example! Makes sense that it can get quite long, though it was not present in my mind. But, hey, that's what the number represented actually is. As I said, make it optional...
firethief 7y ago
IMO hex should be the default representation of binary fp. Printing hex gives the exact value, without giving the illusion of having precision in the hundreds of digits, and can be done way faster than conversion to decimal. >>> 0.1.hex() 0x1.999999999999ap-4 >>> 1e-100.hex() 0x1.bff2ee48e0530p-333

userbinator 7y ago

Indeed, "human readable" really means "in something similar to English" much of the time, and is a vague and quite subjective term; would someone who knows only English think Chinese is not "human readable"? Personally, I consider binary formats to be just as readable (from the hexdump, or sometimes even the ASCII directly) and in some ways even easier to parse without ambiguity --- it just requires a bit (pun intended) of learning, like any language. I've worked with someone who could "read" TCP/IP; and I can read much of Z80 and some x86 Asm as well as a few other binary formats.