readme tweaks

README.md

@@ -128,43 +128,37 @@ $ pytest
 
 ## performance
 
-*(NOTE: this guide is not great because I personally spend a lot of my time in Python land and don't have an amazing understanding of a lot of these features and flags. If someone does and is willing to help document and briefly describe some of these and their tradeoffs, I'd welcome a PR)*
+There are many ways to potentially speed up this code depending on your system. Have a look at the [Makefile](Makefile), which contains a lot of notes. The `make run` command currently uses the `-O3` optimization by default, i.e.:
-
-There are many ways to potentially speed up this code depending on your system. Here we document a few together with a high-level guide on what they do. Here's again the default way to compile, but using -O3:
 
 ```bash
 gcc -O3 -o run run.c -lm
 ```
 
--O3 includes optimizations that are expensive in terms of compile time and memory usage. Including vectorization, loop unrolling, and predicting branches. Here's a few more to try.
+-O3 includes optimizations that are expensive in terms of compile time and memory usage. Including vectorization, loop unrolling, and predicting branches.
 
-`-Ofast` Run additional optimizations which may break compliance with the C/IEEE specifications, in addition to `-O3`. See [the GCC docs](https://gcc.gnu.org/onlinedocs/gcc/Optimize-Options.html) for more information.
+To get a much better performance, try to compile with `make runfast`. This turns on the `-Ofast` flag, which includes additional optimizations that may break compliance with the C/IEEE specifications, in addition to `-O3`. See [the GCC docs](https://gcc.gnu.org/onlinedocs/gcc/Optimize-Options.html) for more information.
 
-`-march=native` Compile the program to use the architecture of the machine you're compiling on rather than a more generic CPU. This may enable additional optimizations and hardware-specific tuning such as improved vector instructions/width.
+Try `-march=native` to compile the program to use the architecture of the machine you're compiling on rather than a more generic CPU. This may enable additional optimizations and hardware-specific tuning such as improved vector instructions/width.
 
-The fastest throughput I saw so far on my MacBook Air (M1) is with:
+The fastest throughput I saw so far on my MacBook Air (M1) so far is with `make runfast`. 
-
-```bash
-gcc -Ofast -o run run.c -lm
-```
 
 You can also experiment with replacing `gcc` with `clang`.
 
 ### OpenMP
 Big improvements can also be achieved by compiling with OpenMP, which "activates" the `#pragma omp parallel for` inside the matmul and attention, allowing the work in the loops to be split up over multiple processors.
-You'll need to install the OpenMP library and the clang compiler first (e.g. `apt install clang libomp-dev` on ubuntu). Then you can compile e.g. like so:
+You'll need to install the OpenMP library and the clang compiler first (e.g. `apt install clang libomp-dev` on ubuntu). I was not able to get improvements from OpenMP on my MacBook, though. Then you can compile with `make runomp`, which does:
 
 ```bash
 clang -Ofast -fopenmp -march=native run.c  -lm  -o run
 ```
 
-You can try swapping clang/gcc, and may try to leave out -march=native. However, when you run inference make sure to use OpenMP flags to set the number of threads, e.g.:
+When you run inference make sure to use OpenMP flags to set the number of threads, e.g.:
 
 ```bash
 OMP_NUM_THREADS=4 ./run out/model.bin
 ```
 
-Depending on your system resources you may want to tweak these hyperparameters. (TODO: I am not intimately familiar with OpenMP and its configuration, if someone would like to flesh out this section I would welcome a PR).
+Depending on your system resources you may want to tweak these hyperparameters and use more threads. But more is not always better, usually this is a bit U shaped.
 
 ## platforms