Straightforward use of `svdvals`

on a freshly restarted Julia session:

```
using BenchmarkTools
B=randn(100,100);
@btime svdvals($B);
versioninfo()
```

Here’s the result.

```
10.423 ms (11 allocations: 138.20 KiB)
Julia Version 0.6.2
Commit d386e40c17 (2017-12-13 18:08 UTC)
Platform Info:
OS: macOS (x86_64-apple-darwin14.5.0)
CPU: Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz
WORD_SIZE: 64
BLAS: libopenblas (USE64BITINT DYNAMIC_ARCH NO_AFFINITY Haswell)
LAPACK: libopenblas64_
LIBM: libopenlibm
LLVM: libLLVM-3.9.1 (ORCJIT, haswell)
```

Run seconds later in MATLAB:

```
>> B=randn(100);
>> tic, for k=1:10000, svd(B); end, t=toc/10000
t =
5.541419457000000e-04
```

(The results don’t change much for different random matrices.) So Julia is taking nearly 20x as long to compute the singular values. Have I overlooked something?

– Toby