Update dependencies to achieve better performance

[Alexsandruss]

Checklist

• Used a personal fork of the feedstock to propose changes
• Bumped the build number (if the version is unchanged)
• Reset the build number to 0 (if the version changed)
• Re-rendered with the latest conda-smithy (Use the phrase @conda-forge-admin, please rerender in a comment in this PR for automated rerendering)
• Ensured the license file is being packaged.

Target of this pull request is to change dependencies from GNU OpenMP and OpenBLAS to LLVM OpenMP and MKL on Linux to improve Faiss performance like it's done in original "pytorch" conda channel recipe.

Small performance comparison of dependencies:

Number of neighbors	Number of rows	Number of columns	searching time (stock: gomp + openblas), s	searching time (llvm-omp + mkl), s	Speedup with llvm-omp + mkl
5	10000	16	0.407	0.202	2.01
5	10000	64	0.427	0.178	2.40
5	20000	16	1.262	0.567	2.23
5	20000	64	1.262	0.572	2.20
5	50000	16	7.371	3.521	2.09
5	50000	64	7.438	3.444	2.16
32	10000	16	0.504	0.253	1.99
32	10000	64	0.485	0.255	1.90
32	20000	16	1.509	0.726	2.08
32	20000	64	1.484	0.730	2.03
32	50000	16	7.767	3.693	2.10
32	50000	64	8.058	3.773	2.14

OS: CentOS Linux 7
SW: Python 3.8.6, numpy 1.19.5, faiss 1.6.5, MKL 2020.4, LLVM-OpenMP 11.0.1, GNU OpenMP 9.3.0, OpenBLAS 3.9.0.
HW: Intel(R) Xeon(R) Platinum 8280L CPU @ 2.70GHz, 2 sockets X 28 cores per socket, 376 GB of RAM.

Benchmark script:

import numpy as np
import faiss
from timeit import default_timer as timer

def measure_function(f, *args):
    times = []
    for i in range(5):
        t0 = timer()
        f(*args)
        t1 = timer()
        times.append(t1 - t0)
    return sum(times) / len(times)

rows_range = [10000, 20000, 50000]
columns_range = [16, 64]
k_range = [5, 32]

print("k,rows,cols,time[s]")
for k in k_range:
    for rows in rows_range:
        for cols in columns_range:
            data = np.random.uniform(size=(rows * 2, cols)).astype(np.float32)
            train_data, test_data = data[:rows,:], data[rows:,:]
            index = faiss.IndexFlatL2(cols)
            index.add(train_data)
            time = measure_function(index.search, test_data, k)
            print("{},{},{},{}".format(k, rows, cols, round(time, 6)))

[conda-forge-linter]

Hi! This is the friendly automated conda-forge-linting service.

I just wanted to let you know that I linted all conda-recipes in your PR (recipe) and found it was in an excellent condition.

[isuruf]

MKL can be selected at install time. See https://conda-forge.org/docs/maintainer/knowledge_base.html#switching-blas-implementation

[h-vetinari]

I'd be fine with either of:

• using track_features to weigh down non-MKL
• prioritizing mkl via buildnumber bump like e.g. here
• adding a post-link script (c.f. e.g. here) with a message that recommends installing the mkl-version of blas/lapack.
  @jakirkham also suggested this a while ago, but I haven't gotten around to it yet.

Don't think it's possible to switch the openmp-implementation in the environment like for blas, right @isuruf?

[jakirkham]

OpenMP can also be selected: https://conda-forge.org/docs/maintainer/knowledge_base.html#switching-openmp-implementation

[jakirkham]

As conda-forge already allows users to choose between different BLAS and OpenMP implementations, I'm not sure I see the need to restrict this on a feedstock basis. Instead we should focus on educating users on these options they have available and help them make informed choices about those options based on their needs

[h-vetinari]

@Alexsandruss
Thanks again for trying to improve the packaging here! 🙃

Would you like to try any of the options I suggested? "Pinning" blas/openmp directly is not the best approach, as explained by the various comments in this thread.