3 ms·

The main thing missing from Numba is user defined structures, the jit classes are a performance penalty usually. That said, in terms of composability you can j

The main thing missing from Numba is user defined structures, the jit classes are a performance penalty usually. That said, in terms of composability you can jit over the closure to achieve a lot of what you might want, e.g. def make_loop(f): @jit def fn(x): for i in range(x.shape[0]): x[i] = f(x[i]) return fn for any jit'd function f will be just as fast as if you had inlined the body of f. For introspection and simplicity, I think, in high performance, you simply have to choose two of fast, simple and generic. Julia clearly chooses fast and generic.

krastanov 6y ago
Could you elaborate on why you are saying julia is not as simple? Certainly, there are performance tips one needs to be aware of, but that is the case with python too. I have used python for relatively high-performance numerics for research work for the last decade, and now that I am exploring Julia, it certainly addresses all of the concerns I mentioned above with code which is at least as legible/simple and much more introspective. It has its warts, but those warts are on the roadmap and I have seen significant improvement between v1.3 and v1.6 (e.g. compilation latency and debuggability).
- marmaduke 6y ago
  Compiler errors can be on par with heavily templates C++, with similarly difficult to read code bases. It’s of course easier to work with than C++ but not the panacea that seems to be implied in a lot of the comments. In terms of deployment, you essentially have to have Julia installed wherever you want to run, which frequently ok, and if not there’s always PackageCompiler except it’s not that easy to get a working shared lib. There are other things I find complex but probably because I’ve used Python too long so Julia is different etc. I think Julia is a great choice for HPC generally except for challenging SIMD codes where it’s hard to vectorize without an explicit ILP model.

krastanov 6y ago

Could you elaborate on why you are saying julia is not as simple? Certainly, there are performance tips one needs to be aware of, but that is the case with python too. I have used python for relatively high-performance numerics for research work for the last decade, and now that I am exploring Julia, it certainly addresses all of the concerns I mentioned above with code which is at least as legible/simple and much more introspective. It has its warts, but those warts are on the roadmap and I have seen significant improvement between v1.3 and v1.6 (e.g. compilation latency and debuggability).

marmaduke 6y ago
Compiler errors can be on par with heavily templates C++, with similarly difficult to read code bases. It’s of course easier to work with than C++ but not the panacea that seems to be implied in a lot of the comments. In terms of deployment, you essentially have to have Julia installed wherever you want to run, which frequently ok, and if not there’s always PackageCompiler except it’s not that easy to get a working shared lib. There are other things I find complex but probably because I’ve used Python too long so Julia is different etc. I think Julia is a great choice for HPC generally except for challenging SIMD codes where it’s hard to vectorize without an explicit ILP model.