This repository aims to develop julia equivalents to the c++ code samples provided in this online course on parallel programming by the Aalto University in Espoo, Finland.
The code used in this study is short, simple, and showcases several techniques for optimization on modern x86 CPUs. It is thus a good project to understand certain performance characteristics of Julia, like the usage of SIMD instructions.
The shortcut problem is described here.
We consider several implementations that exploit increasingly sophisticated optimizations (like SIMD or proper register reusage). Put in simple terms, the following version are compared:
v0. Naive and straightforward implementation.
v1. Create a transposed copy of the input so that consecutive memory regions are used for both summands.
v2. Break the sequential dependency in the inner loop by subdividing it in several independent parts.
v3. Exploit AVX instructions to compute 8 Float32 additions and mins in parallel.
v4. Improve register reuse by changing the order of looping.
The lecture notes provide more optimizations (up
to v7), but this repository currently only explores v0 to v4. The corresponding
julia implementations can be found in the file ppc.jl with the names
vx_julia. The C++ implementations, copied from
here, are in ppc.cpp. The included makefile
compiles this file with both g++ and clang to shared libraries ppc-gcc.so
and ppc-clang.so, which are then wrapped in julia (vx_{gcc,clang}) for
uniform benchmarking.
To run the full benchmark (with n = 1000), first compile the gcc and clang
shared libraries via make and then execute julia benchmark.jl. This requires
the packages BenchmarkTools and DelimitedFiles. Besides printing
information, this will also create the file times.dat, which can be visualized
via the included gnuplot script plot.gp
Below are example timings for the desktop CPU AMD Ryzen 7 5800X CPU and laptop CPU Intel Core i5-8250U. In both cases, the official julia 1.8.0 binaries were used.
