Updated vis_gpu API to match vis_cpu

[steven-murray]

This PR is quite large, and should generate a new major version.

Fixes #42
Fixes #14

Changes

It removes the following:

• Ability to use an (l,m) grid for beam interpolation, in both CPU and GPU versions
• Ability to pass anything except UVBeam or AnalyticBeam objects in the beam list.

It adds the following:

• GPU version now performs beam interpolation on a regular theta/phi grid (but can also use direct function evaluation if it's an AnalyticBeam)
• Beam polarization now fully supported in GPU.
• A new CLI script viscpu profile that can run a simulation with arbitrary numbers of freqs, times, sources, antennas and beams and outputs line profiling information
• Updated tests for both CPU and GPU, comparing both against each other and pyuvsim.

It improves the following:

• CPU version made ~10x faster by switching to a matrix-product instead of an einsum.

Performance Measurements

GPU vs. original (einsum) CPU

CPU: einsum vs matprod (new)

CPU: performance vs. Nthreads

Caveats / Things to do

• GPU version has not yet been tested vs nthreads/maximum memory etc.
• CPU version has no "chunking" which would allow it to run on smaller-memory allocations. Since it seems that increasing Ncores doesn't increase performance by much, it would seem that the best way to break up the simulation would be by time and frequency, running a single time and frequency per-core. However, since that memory wouldn't be shared, we'd need a way to be able to run with a low per-core memory.
• Dominant component of the CPU version is the element-wise product and exponentiation of the fringe. There may be further speedups possible here to do with memory layout. Possibly also we could get speedup by threading if we tried hard (which would reduce the need for memory chunking).
• The viscpu profile script works really well for line profiling of the CPU, but for GPU the whole thing needs to be run inside of nvprof which makes it slightly non-uniform. This could be improved.
• viscpu profile always runs with a particular coarse beam, which is not really representative of real problems. We should check the effect of this.

We may not need to do all of the above in this PR, but I thought I'd lay it out for discussion.
*

[steven-murray]

This is currently failing on the single-precision test on GPU. For some reason it's saying

pycuda._driver.LaunchError: cuLaunchKernel failed: too many resources requested for launch

This would seem to indicate that it requires more threads/shared memory/registers than available. But it works perfectly fine for double precision, which makes no sense to me. Anyone with CUDA knowledge, please step in! Maybe @AaronParsons could be of help here...

Other than that, everything seems to be working fine, and is tested at 100% coverage. To get coverage on GPU I set up a self-hosted runner on my own laptop (clearly not sustainable, but I'll transfer this over to our ASU cluster when I can).

[philbull]

Pretty major changes here! Looks like a lot of improvements have been made, which look good to me overall. There are some API breakages that should be documented in the release notes, and I can't really give useful comments on the CUDA code. Only a few minor comments to address, mostly about documentation, plus getting the tests to pass.

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[philbull]

Looks good to me. I think this can be merged (with a version bump) once the tests are passing again.