From f45a6347ea524164cc812fad80c9db1f4f94e17f Mon Sep 17 00:00:00 2001 From: Andrea Zanelli Date: Fri, 22 May 2020 11:20:06 +0200 Subject: [PATCH] implementing @giaf's remarks on README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index f0e0e15..eba83dd 100644 --- a/README.md +++ b/README.md @@ -24,7 +24,7 @@ the Python run-time library. ### performance -Since prometeo programs transpile to pure C code that calls the high performance linear algebra library BLASFEO ([https://arxiv.org/abs/1704.02457](https://arxiv.org/abs/1704.02457)), execution time can be comparable to hand-written high-performance code. The figure below shows a comparison of the CPU time necessary to carry out a Riccati factorization using highly optimized hand-written C code with calls to BLASFEO and the ones obtained with prometeo transpiled code from [this example](https://github.com/zanellia/prometeo/blob/master/examples/riccati_example/riccati_mass_spring.py). The computation times obtained with NumPy and Julia are added too for comparison - notice however that these last two implementations of the Riccati factorization are **not as easily embeddable** as the C code generated by prometeo and the hand-coded C implementation. All the benchmarks have been run on a Dell XPS-9360 equipped with an i7-7560U CPU running at 2.30 GHz (to avoid frequency fluctuations due to overheating). +Since prometeo programs transpile to pure C code that calls the high performance linear algebra library BLASFEO (publication: [https://arxiv.org/abs/1704.02457](https://arxiv.org/abs/1704.02457), code: [https://github.com/giaf/blasfeo](https://github.com/giaf/blasfeo)), execution time can be comparable to hand-written high-performance code. The figure below shows a comparison of the CPU time necessary to carry out a Riccati factorization using highly optimized hand-written C code with calls to BLASFEO and the ones obtained with prometeo transpiled code from [this example](https://github.com/zanellia/prometeo/blob/master/examples/riccati_example/riccati_mass_spring.py). The computation times obtained with NumPy and Julia are added too for comparison - notice however that these last two implementations of the Riccati factorization are **not as easily embeddable** as the C code generated by prometeo and the hand-coded C implementation. All the benchmarks have been run on a Dell XPS-9360 equipped with an i7-7560U CPU running at 2.30 GHz (to avoid frequency fluctuations due to thermal throttling).

@@ -32,7 +32,7 @@ Since prometeo programs transpile to pure C code that calls the high performance ### PyPI installation -prometeo can be installed through PyPI with `pip install prometeo-dsl`. +prometeo can be installed through PyPI with `pip install prometeo-dsl`. Notice that, since prometeo makes extensive use of [type hints](https://docs.python.org/3.6/library/typing.html) to equip Python code with static typing information, the minimum Python version required is 3.6. ### manual installation If you want to install prometeo building the sources on your local machine you can proceed as follows: