#+title: Matrix multiplication with SYCL, yay

This project serves as a sample demonstration of SYCL syntax and offers a
straightforward program as an illustration.

Its primary objective is to function as a benchmark for executing matrix
multiplication on a single CPU core while using SYCL for both OpenMP and GPU
parallelization. Subsequently, we will record and analyze the execution times.

At this stage, the project showcases how to transfer and manipulate data on the
GPU using +the Unified Shared Memory (USM) model with explicit data movement+ an
abstract view to the host and device memory using buffers and accessors. I will
not attend to implement those functions using Unified Shared Memory.

For more detailed information about the implementation and how specific
functions are used, as well as explanations for the reasoning behind certain
design choices, I recommend referring to the source code itself. The source code
typically contains comments that provide insights into the code's functionality
and rationale.

* Prerequisites

To use the project, you'll need the following prerequisites:

** Mandatory Prerequisites

- A functional SYCL compiler. You can choose from options like Intel's oneAPI or
  AdaptiveCpp.

- The "xxhash" library.

** Optional Prerequisite

- CMake (for generating build files)

* Compilation

Regrettably, integrating Intel's oneAPI with the AMD GPU plugin proves to be
quite challenging on Arch Linux, primarily due to the plugin's dependency on an
older version of ROCm than what's available in the official repositories. While
I could have chosen to compile my own ROCm/hip version, I opted for a more
convenient solution and turned to the [[https://github.com/AdaptiveCpp/AdaptiveCpp/tree/develop][AdaptiveCpp]] compiler, which offers both
CPU and GPU acceleration through CUDA and ROCm support. You can find a version
of AdaptiveCpp compatible with AMD GPUs on the AUR (Arch User Repository).

If your goal is to run benchmarks on an AMD GPU alongside AdaptiveCpp, I
recommend using [[https://github.com/sobc/pkgbuilds/tree/master/hipsycl-rocm-git][this]] specific PKGBUILD. Other versions that rely on ROCm might
not build correctly at the moment. I've already raised an issue with the
responsible maintainer of the PKGBUILDs to address this compatibility issu

Currently, I can only utilize CMake for generating makefiles when working with
AdaptiveCpp. However, I intend to add CMake support for Intel's oneAPI as soon
as I have a working version of the compiler.

To generate Makefiles for AdaptiveCpp, you can follow these steps:

#+BEGIN_SRC bash
# Create a build directory and navigate to it
mkdir build && cd build

# Adjust the path to AdaptiveCpp and your target devices according to your system
cmake .. -DAdaptiveCpp_DIR=/opt/AdaptiveCpp/ROCm/lib/cmake/AdaptiveCpp -DACPP_TARGETS="omp.accelerated;hip.integrated-multipass;gfx90c"
#+END_SRC

You can find more information about =ACPP_TARGETS= and the compilation process in
the documentation [[https://github.com/AdaptiveCpp/AdaptiveCpp/blob/develop/doc/compilation.md][here]].

Once your Makefiles are generated, you can build the project using the following
command:

#+BEGIN_SRC bash
make -j$(nproc)
#+END_SRC

The compiled executable can be found in the =build/src= directory.

* Data

I provide 6 different matrices with 3 different sizes:

- =sma*.txt= are matrices with the size of 16x16
- =med*.txt= are matrices with the size of 2048x2048
- =big*.txt= are matrices with the size of 8192x8192

All matrices are stored in text files under =data=.

*Warning*: If you're about to run the benchmark with the big matrices, please
disable the benchmark on one single CPU core, unless you want to sit and wait
forever. Do this by calling cmake with =-DSEQ_BENCH=OFF= and recompile the
executable.

Below you will find the combination of all multiplication of all matrices and
their checksum. Let me now if you encounter other checksums.

| Matrix A   | Matrix B   | Checksum     |
|------------+------------+--------------|
| =sma1.txt= | =sma1.txt= | =0xe6134d8e= |
| =sma2.txt= | =sma2.txt= | =0xf1ba0ac6= |
| =sma1.txt= | =sma2.txt= | =0xe71fdf1e= |
| =sma2.txt= | =sma1.txt= | =0x36b44d2c= |
|------------+------------+--------------|
| =med1.txt= | =med1.txt= | =0xd92eb6d6= |
| =med2.txt= | =med2.txt= | =0x9f0e1206= |
| =med1.txt= | =med2.txt= | =0x4cf45b91= |
| =med2.txt= | =med1.txt= | =0xfdeb52bf= |
|------------+------------+--------------|
| =big1.txt= | =big1.txt= | =0xde9b4c0d= |
| =big2.txt= | =big2.txt= | =0x5365fc1=  |
| =big1.txt= | =big2.txt= | =0xb185e6c1= |
| =big2.txt= | =big1.txt= | =0x59f5ffef= |