There are many things you can do to improve speed in R. Byte compiling
is just one of them.
This chapter in Hadley Wickham's excellent Advanced R book covers both
profiling and byte compiling.
http://adv-r.had.co.nz/Profiling.html
I've gotten some stunning improvements in speed through profiling and
careful thought: 3 days to 3 seconds, even.
Sarah
On Thu, Jun 21, 2018 at 6:28 AM, akshay kulkarni <akshay_e4 at
hotmail.com> wrote:> dear members,
> I a Day Trader based in INDIA. I use R for my
research. I have a function ygusa(snlq,snlcqn) which takes 208 stocks and
returns 4 best stocks for the next day(snlq is the list of 208 stocks and snlcqn
is their names). However, the execution time is around 2 hrs, making it hard for
me.
>
> I recently read in the internet that you can precompile the code in R to
make it run faster. Also that you can enable JIT(just in time compilation) from
your R session automatically. I came to know that R 3.4.x has JIT enabled in it
by default. Is it true? Is it also true that even after enabling JIT in R 3.4.x,
the first run of a function is not Byte compiled?
>
> So when I start my R session, download the data, and run
ygusa(snlq,snlcqn), it is not byte compiled and therefore it is very slow? Will
including the following lines in ygusa solve my problem:
>> require(compiler)
>> enableJIT(3)
>
> ?
>
> Also, instead of compiling the function ygusa every time I run it, can I
compile it once and store it, and run that compiled file instead of
ygusa(snlq,snlcqn)?
>
> Can you point me to some online resources that can help on this issue?
>
> very many thanks for your time and effort...
> Yours sincerely,
> AKSHAY M KULKARNI
>
--
Sarah Goslee
http://www.functionaldiversity.org