On 2/24/23 00:39, Jane He wrote:
Hi all,
I am currently compiling R to RISC-V64 CPU and I think I have discovered a
NA propagation failure.
How R implements NA (not available) and NaN (not-a-number) is explained in
detail here:
https://stat.ethz.ch/pipermail/r-devel/2014-February/068380.html.
In short, according to my understanding of R's convention, any calculation
involving NA but no NaN should result in NA (called NA propagation), and
any calculation involving NaN but no NA should result in NaN. Calculations
involving both NA and NaN can result in either value.
While many R functions handle this logic in their source codes, basic
arithmetic operations such as +-/* throw it to the hardware to handle.
However, the RISC-V64 CPU does not behave as expected, at least the CPU I
am using (Starfive JH7100-7110).
Here are the relevant bit patterns. From my understanding, as IEEE only
regulates the bit patterns of NaN, R picks one of the bit patterns (ending
with 07a2) and declares it as NA.
# print_hex is a function to print the bit pattern in hex
print_hex(0.1)
3fba # same for RISC-V
print_hex(NaN)
7ff8 # same for RISC-V
print_hex(NA)
7ff007a2 # same for RISC-V
print_hex(NA+1)
7ff807a2 # 7ff8 in RISC-V
print_hex(NA*1)
7ff807a2 # 7ff8 in RISC-V
print_hex(NaN*1)
7ff8 # same for RISC-V
Therefore, in RISC-V64, all basic arithmetic operations involving NA give
NaN.
NA+1
[1] NaN
This failure in NA propagation may cause many R packages like mice to not
work properly, and results in the `make check` test in the `stats` package
to fail. Example from the make check test:
xn <- 1:4
yn <- c(1,NA,3:4)
xout <- (1:9)/2
data.frame(approx(xn,yn, xout, na.rm=FALSE, rule = 2)) # failure, some
values should be NA but it turns out NaN
I am reaching out to the R community looking for help in solving this
problem. Does anyone around here have any hints or ideas on how to solve
this issue?
Currently, my hacky implementation is to stop the NA operand before it goes
to the hardware and directly return NA as the output. However, this
solution may penalize performance significantly, so I am looking for any
alternative idea.
Thank you for your time and consideration!
Please see this bug report for a related discussion:
https://bugs.r-project.org/show_bug.cgi?id=18416
This is a known problem and has been discussed a number of times before
RISC-V. Still, RISC-V is a bit unique architecture in that it
deliberately does not propagate NaN payloads at all and it cannot be
enabled. R's distinction of NA vs NaN however depends on that propagation.
The propagation of NAs through computations in R is not reliable even on
other current platforms (due to what the hardware does, but also what
the compilers and libraries do, see e.g. ?NaN) and technically it is not
guaranteed and R packages are advised not to depend on it, but various
package checks and existing software adjusted to how the propagation
happens to work on widely used platforms, now probably mostly x86_64.
64-bit ARM is even a bit more friendly to R than that (when the CPU is
switched to such mode, which R does). If RISC-V supported such mode, R
would enable it as well, but it deliberately does not support it.
Good defensive portable code would not depend on the propagation to
happen in hardware/compilers but add checks in software, if propagation
was needed. I doubt that the performance overhead would be too high if
done well, particularly for vectorized computations, but it will be
noticeable and in some cases may be high. This would have to include
special handling of data including NAs when they are passed to external
numerical libraries, which would be a lot of work to implement and
maintain, it seems unrealistic. At the same time there is no strong
incentive to do so: people who want the propagation seem to be
reasonably happy with how it happens to work on major platforms.
For the current R to work well on RISC-V, really, the platform would
have to support the propagation, at least optionally, perhaps like
64-bit ARM. Unless major platforms in the future stop supporting the
propagation, I am skeptical anything major could change on the R side.
Best
Tomas
Best regards,
Jane He
_
University of California, Irvine
Student of Master of Software Engineering
2022-2023 cohort
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