In J80x, there's no deriv_jcalculus_ because deriv_jcalculus_ is a
replacement for the d. primitive. So, in J807, you can just use that
primitive.
I think the idea was that by making it a library routine instead of a
primitive it would be easier for motivated J programmers to enhance it
with additional functionality. (But these things take time, and
thought...)
Thanks,
--
Raul
On Mon, May 24, 2021 at 10:11 PM Thomas McGuire <tmcguir...@gmail.com>
wrote:
For those interested in Benchmarks I have split my original benchmark
routine into 2 scripts. One the Kernighan/Van Wyk benchmarks and an
advanced benchmarks script. The advanced benchmarks were taken from some of
the functionality that the Scheme language benchmarks for which I found J
implementations.
The deriv benchmark uses Raul’s suggestion with the polynomial taken
from the data from the Scheme implementation. Itwon’t be a direct
comparison to Scheme since Scheme does a full symbolic differentiation
allowing undefined variable coefficients. In this respect the Scheme
benchmark tests parsing nested S-expressions.
I added a QR decomposition benchmark from the QR essay and compare it to
the foreign 128!:0. I also include a Schwarz-Rutishauser QR decamp
implementation. This is translated from python code and uses for loops so
on large matrices it is very slow. I comment out the test in the test
harness.
Finally I added a matrix multiplication test given the recent discussion
on Matrix multiplication on the programming forum. The test takes the Q and
R matrices and multiplies them for timing. I added a match check on the
original matrix since the data is randomly produced.
The scripts are available at:
https://github.com/tmcguirefl/JBenchmarks
J902 on MacBook Pro Intel I7 quad core 2.9GHz
ADVbmarks ''
tak: 0.045548 17344
fibN: 1e_6 13376
rsum: 0.005007 5.02125e6
mbrot: 0.875056 199360
deriv: 2.7e_5 9728
fftw: 0.000489 1.5751e6
ifftw: 0.000717 2.09894e6
ft: 3.4e_5 6528
fftws: 1.8e_5 5696
QRdata size: 700 400
QRrec: 0.079073 3.62602e7
QRfor: 0.090745 6.08386e7
MMult: 0.176499 3.3557e7
Match: 1 (not a timing, checks orig matrix matches the mult. QxR)
Of note to the matrix multiply discussion is that a 700x400 matrix takes
less than a second on macOS 64 bit in J902. Granted the R matrix is upper
triangular but if optimizations are taking place it is under the covers,
since this is a straight J +/ . * implementation.
On J807 I took out the deriv and the rsum due to errors (my 806 doesn’t
have the calculus lib and rsum had a stack error I assume 902 has improved
recursion) and the results:
ADVbmarks ''
tak: 0.069502 36032
fibN: 2e_6 8512
mbrot: 1.02267 200192
fftw: 0.000867 1.57523e6
ifftw: 0.000806 2.09907e6
ft: 5e_5 6848
fftws: 3.2e_5 7104
QRdata size: 700 400
QRrec: 0.08946 4.45858e7
QRfor: 0.100718 6.13482e7
MMult: 0.167292 4.29949e7
Match: 1 (not a timing, checks orig matrix matches the mult. QxR)
There appears to be an across the board speed up from 806 to 902 in
everything but matrix multiplication. That may be due to differences in the
random matrix generated for each test.
On May 19, 2021, at 5:22 AM, Raul Miller <rauldmil...@gmail.com>
wrote:
deriv_jcalculus_ does symbolic differentiation on tacit expressions.
For example:
load'math/calculus'
(3+*:) deriv_jcalculus_ 1
0"0 + +:
3 0 1&p. deriv_jcalculus_ 1
0 2&p.
(3+*:) deriv_jcalculus_ 1 i. 5
0 2 4 6 8
3 0 1&p. deriv_jcalculus_ 1 i. 5
0 2 4 6 8
(The right argument to deriv_jcalculus_ is how many times to take the
derivative, a negative argument does symbolic integration instead,
with an integration constant of 0. But integration is a bit fussier
than derivation, so it has less support for variant expressions.)
FYI,
--
Raul
On Wed, May 19, 2021 at 4:17 AM Thomas McGuire <tmcguir...@gmail.com
<mailto:tmcguir...@gmail.com>> wrote:
Thanks Raul,
I added your ‘tail' implementation to the code and it works without
issue. The KJV text is listed in reverse line order in the output file.
benchmarks ''
sumloop: 0.227858 8.39149e6
sumj: 0.000171 1.04986e6
ack: 2e_6 721472
array1: 5.6e_5 1.05107e6
array1t: 4.8e_5 1.05107e6
string1: 0.003969 2.88634e6
cat: 0.01474 2.51685e7
wc: 0.295076 1.58152e8
tail: 0.065197 3.77133e7
sum1: 0.12709 4.04989e7
sum1j: 0.000547 1.18086e6
I have placed the code on GitHub:
https://github.com/tmcguirefl/JBenchmarks
The scheme folks have put together a large set of benchmarks at:
https://www.ccs.neu.edu/home/will/Twobit/benchmarksAboutR6.html
When time permits I will go through them and implement the ones that
would be J appropriate (some do symbolic differentiation which would be
nontrivial for me to code into J).
Tom McGuire
On May 18, 2021, at 9:04 PM, Raul Miller <rauldmil...@gmail.com>
wrote:
Here's the tail benchmark written in J:
tail=: 4 : 0
(;|.<;.2 freads x) fwrites y
)
This is based on my reading of the C implementation of the tail
benchmark vs the cat benchmark, and on the cat benchmark in this J
implementation, except that I have failed to reproduce a bug in the C
implementation which shows up when the file contains very long lines
and instead I have assumed that the file ends in a newline (which is
a
valid assumption for the gutenberg kjv bible).
Let me know if you think I have made a mistake here.
Thanks,
--
Raul
On Tue, May 18, 2021 at 7:02 PM Thomas McGuire <tmcguir...@gmail.com
<mailto:tmcguir...@gmail.com><mailto:tmcguir...@gmail.com <mailto:
tmcguir...@gmail.com>>> wrote:
I was going over Devon McCormick's notes from the latest NYCJUG
meeting. There was a discussion about Benchmarks with an example of the
Kernighan/Van Wyk benchmarks in LISP/Scheme. Devon had made the comment
that for most things people use J for that correctness overshadowed
performance.
The K/VW benchmarks were originally made for C and seem to test
looping controls and memory allocation which at a time of low memory
minicomputers and PCs, coupled with multiple available C compilers, made
the benchmarks a nice way of comparing compiler implementations as well as
new processor speed.
However it seemed like a simple project to exercise my J
programming skills with so I have attached the program to this email.
Note: you will need to download the King James Version of the Bible
from project Guttenburg (not included with the attachment) to run the full
benchmarks. I also created my own file of floating point numbers to sum
(routine to create a file of random floats is included). I have added some
routines to accomplish similar functionality in the way I would do it in J
(at least for my mere mortal understanding of J).
Running on my mac book pro (2016 with 2.9 Ghz Quad core I7) the
results are:
benchmarks ''
sumloop: 0.214383 8.39149e6
sumj: 0.000162 1.04986e6
ack: 4e_6 721472
array1: 0.000578 1.05107e6
array1t: 2.5e_5 1.05107e6
string1: 0.003237 2.88634e6
cat: 0.014234 2.51685e7
wc: 0.302383 1.58152e8
sum1: 0.124006 4.04989e7
sum1j: 0.000956 1.18086e6
thought I would share
Tom McGuire
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