3 ms·

Even the latest CPUs have a 2:1 fp64:fp32 performance ratio - plus the effects of 2x the data size in cache and bandwidth use mean you can often get greater tha

• adgjlsfhk1 6mo ago
  > languages that care about performance to default to fp32 What do you mean by this? In C 1.0 is a double.
  • kimixa 6mo ago
    But the "float" typename is generally fp32 - if we assume the "most generically named type" is the "default". Though this is a bit of an inconsistency with C - the type name "double" surely implies it's double the expected baseline while, as you mentioned, constants and much of libm default to 'double'.
    • adrian_b 6mo ago
      The C keywords "float" and "double" are based on the tradition established a decade earlier by IBM System/360 of calling FP32 as "single-precision" and FP64 as "double-precision". This IBM convention has been inherited by the IBM programming languages FORTRAN IV and PL/I and from these 2 languages it has spread everywhere. The C language has taken several keywords and operators from IBM PL/I, which was one of the three main inspiration sources for C (which were CPL/BCPL, PL/I and ALGOL 68). So "float" and "double" are really inherited by C from PL/I. A feature that is specific to C is that it has changed the default format for constants and for intermediate values to double-precision, instead of the single-precision that was the default in earlier programming languages. This was done with the intention of protecting naive users from making mistakes, because if you compute with FP32 it is very easy to obtain erroneous results, unless you analyze very carefully the propagation of errors. Except in applications where errors matter very little, e.g. graphics and ML/AI, the use of FP32 is more suitable for experts, while bigger formats are recommended for normal users.
• pixelesque 6mo ago
  > Even the latest CPUs have a 2:1 fp64:fp32 performance ratio Not completely - for basic operations (and ignoring byte size for things like cache hit ratios and memory bandwidth) if you look at (say Agner Fog's optimisation PDFs of instruction latency) the basic SSE/AVX latency for basic add/sub/mult/div (yes, even divides these days), the latency between float and double is almost always the same on the most recent AMD/Intel CPUs (and normally execution ports can do both now). Where it differs is gather/scatter and some shuffle instructions (larger size to work on), and maths routines like transcendentals - sqrt(), sin(), etc, where the backing algorithms (whether on the processor in some cases or in libm or equivalent) obviously have to do more work (often more iterations of refinement) to calculate the value to greater precision for f64.
  • kimixa 6mo ago
    > ... if you look at (say Agner Fog's optimisation PDFs of instruction latency) ... That.... doesn't seem true? At least for most architectures I looked at? While true the latency for ADDPS and ADDPD are the same latency, using the zen4 example at least, the double variant only calculates 4 fp64 values compared to the single-precision's 8 fp32. Which was my point? If each double precision instruction processes a smaller number of inputs, it needs to be lower latency to keep the same operation rate. And DIV also has a significntly lower throughput for fp32 vs fp64 on zen4, 5clk/op vs 3, while also processing half the values? Sure, if you're doing scalar fp32/fp64 instructions it's not much of a difference (though DIV still has a lower throughput) - but then you're already leaving significant peak flops on the table I'm not sure it's a particularly useful comparison. It's just the truism of "if you're not performance limited you don't need to think about performance" - which has always been the case. So yes, they do at least have a 2:1 difference in throughput on zen4 - even higher for DIV.
    • adgjlsfhk1 6mo ago
      This depends largely on your operations. There is lots of performance critical code that doesn't vectorize smoothly, and for those operations, 64 bit is just as fast.