nstream-numba: get good performance by pulling 0th iteration call out of loop

[hattom]

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New PRK implementation checklist

Which kernels are implemented?

• [ ] synch_p2p (p2p)
• [ ] stencil
• [ ] transpose
• [x] nstream
• [ ] dgemm
• [ ] reduce
• [ ] sparse
• [ ] branch
• [ ] random
• [ ] refcount
• [ ] synch_global
• [ ] PIC
• [ ] AMR

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Before:

python PYTHON/nstream-numba.py 100 100000
Python version =  3.7
Numpy version  =  1.16.4
Parallel Research Kernels version 
Python Numpy STREAM triad: A = B + scalar * C
Number of iterations =  100
Vector length        =  100000
Solution validates
Rate (MB/s):  1787.9912007142032  Avg time (s):  0.0017897179800000008

After:

python PYTHON/nstream-numba.py 100 100000
Python version =  3.7
Numpy version  =  1.16.4
Parallel Research Kernels version 
Python Numpy STREAM triad: A = B + scalar * C
Number of iterations =  100
Vector length        =  100000
Solution validates
Rate (MB/s):  35320.83292264001  Avg time (s):  9.059808999999142e-05

[hattom]

Now that I'm looking at it, I think the same would have been achieved by changing if k<0: t0 = timer() to if k==1: t0 = timer(). I was thinking something weirder was going on.

[jeffhammond]

LGTM

haven't verified because i am still trying to install Numba on my new laptop but that isn't critical