kev-inn opened a new pull request, #1643:
URL: https://github.com/apache/systemds/pull/1643
# `DoubleVector` replacement for matrix multiply
JDK 17 adds `Vector` classes to use SIMD instructions. This PR replaces the
basic dense dense matrix multiply with an equivalent `DoubleVector`
implementation. It is necessary to use JDK 17, therefore we should not merge
this yet, but keep it in staging for future reference.
As an experiment we check a simple matrix multiply:
`Z = X %*% Y`, $X\in \mathbb{R}^{n\times k}, Y\in \mathbb{R}^{k\times m}$
The experiment script performs 10 matrix multiplications and saves the time
of the last 5 to give the JVM some time to optimize.
## Vary rows n, m fixed at 1000
### Alpha Node
| $k = 1000$ | $k = 10000$ |
| --------------- | --------------- |
|
|
|
### Lima Node
| $k = 1000$ | $k = 10000$ |
| --------------- | --------------- |
|
|
|
## Vary cols m, n fixed at 1000
### Alpha Node
| $k = 1000$ | $k = 10000$ |
| --------------- | --------------- |
|
|
|
### Lima Node
| $k = 1000$ | $k = 10000$ |
| --------------- | --------------- |
|
|
|
## Conclusion
The implementation seems to boost the performance in most cases. The case
where we vary the number of columns n on the alpha node needs some more
exploration, but it seems we are never worse than the current implementation.
## Experiment Script
```R
X = read($Xfname);
Y = read($Yfname);
lim = 10;
R = matrix(0, rows=lim, cols=1);
for (i in 1:lim) {
t1 = time();
Z = X %*% Y;
t2 = time();
R[i,1] = (t2-t1)/1000000;
}
print(sum(Z));
res = R[5:lim,];
write(res, $fname, format="csv", sep="\t");
```
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