Related work: continuation of GPGPU

thesis/chapters/relwork.tex

@@ -12,20 +12,25 @@ The expressions generated by an equation learning algorithm can look like this $
 
 
 \section[GPGPU]{General Purpose Computation on Graphics Processing Units}
-Graphics cards (GPUs) are commonly used to increase the performance of many different applications. Originally they were designed to improve performance and visual quality in games. \textcite{dokken_gpu_2005} first described the usage of GPUs for general purpose programming. They have shown how the graphics pipeline can be used for GPGPU programming. Because this approach also requires the programmer to understand the graphics terminology, this was not a great solution. Therefore, Nvidia released CUDA\footnote{\url{https://developer.nvidia.com/cuda-toolkit}} in 2007 with the goal of allowing developers to program GPUs independent of the graphics pipeline and terminology. A study of the programmability of GPUs with CUDA and the resulting performance has been conducted by \textcite{huang_gpu_2008}. They found that GPGPU programming has potential, even for non-embarassingly parallel problems. Research is also done in making the low level CUDA development simpler. \textcite{han_hicuda_2011} have described a directive-based language to make development simpler and less error-prone, while retaining the performance of handwritten code. To drastically simplify CUDA development \textcite{besard_effective_2019} showed that it is possible to develop with CUDA in the high level programming language Julia\footnote{\url{https://julialang.org/}} while performing similar to CUDA written in C. In a subsequent study \textcite{lin_comparing_2021} found that high performance computing (HPC) on the CPU and GPU in Julia performs similar to HPC development in C. This means that Julia can be a viable alternative to Fortran, C and C++ in the HPC field and has the additional benefit of developer comfort since it is a high level language with modern features such as garbage-collectors. \textcite{besard_rapid_2019} have also shown how the combination of Julia and CUDA help in rapidly developing HPC software. While this section and thesis in general talk about CUDA, as it is a widely used framework for GPGPU programming, there also exist alternatives by AMD called ROCm\footnote{\url{https://www.amd.com/de/products/software/rocm.html}} and a vendor independent alternative called OpenCL\footnote{\url{https://www.khronos.org/opencl/}}.
+Graphics cards (GPUs) are commonly used to increase the performance of many different applications. Originally they were designed to improve performance and visual quality in games. \textcite{dokken_gpu_2005} first described the usage of GPUs for general purpose programming. They have shown how the graphics pipeline can be used for GPGPU programming. Because this approach also requires the programmer to understand the graphics terminology, this was not a great solution. Therefore, Nvidia released CUDA\footnote{\url{https://developer.nvidia.com/cuda-toolkit}} in 2007 with the goal of allowing developers to program GPUs independent of the graphics pipeline and terminology. A study of the programmability of GPUs with CUDA and the resulting performance has been conducted by \textcite{huang_gpu_2008}. They found that GPGPU programming has potential, even for non-embarassingly parallel problems. Research is also done in making the low level CUDA development simpler. \textcite{han_hicuda_2011} have described a directive-based language to make development simpler and less error-prone, while retaining the performance of handwritten code. To drastically simplify CUDA development \textcite{besard_effective_2019} showed that it is possible to develop with CUDA in the high level programming language Julia\footnote{\url{https://julialang.org/}} while performing similar to CUDA written in C. In a subsequent study \textcite{lin_comparing_2021} found that high performance computing (HPC) on the CPU and GPU in Julia performs similar to HPC development in C. This means that Julia can be a viable alternative to Fortran, C and C++ in the HPC field and has the additional benefit of developer comfort since it is a high level language with modern features such as garbage-collectors. \textcite{besard_rapid_2019} have also shown how the combination of Julia and CUDA help in rapidly developing HPC software. While this thesis in general revolves around CUDA, there also exist alternatives by AMD called ROCm\footnote{\url{https://www.amd.com/de/products/software/rocm.html}} and a vendor independent alternative called OpenCL\footnote{\url{https://www.khronos.org/opencl/}}.
 
+While in the early days of GPGPU programming a lot of research has been done to assess if this approach is feasible, it now seems obvious to use GPUs to accelerate algorithms. Weather simulations began using GPUs very early for their models. In 2008 \textcite{michalakes_gpu_2008} proposed a method for simulating weather with the WRF model on a GPU. With their approach, they reached a speed-up of the most compute intensive task of 5 to 20, with very little GPU optimisation effort. They also found that the GPU usages was very low, meaning there are resources and potential for more detailed simulations. Generally, simulations are great candidates for using GPUs, as they can benefit heavily from a high degree of parallelism and data throughput. \textcite{koster_high-performance_2020} have developed a way of using adaptive time steps to improve the performance of time step simulations, while retaining their precision and constraint correctness. Black hole simulations are crucial for science and education for a better understanding of our world. \textcite{verbraeck_interactive_2021} have shown that simulating complex Kerr (rotating) black holes can be done on consumer hardware in a few seconds. Schwarzschild black hole simulations can be performed in real-time with GPUs as described by \textcite{hissbach_overview_2022} which is especially helpful for educational scenarios. While both approaches do not have the same accuracy as detailed simulations on supercomputers, they show how single GPUs can yield similar accuracy at a fraction of the cost. Networking can also heavily benefit from GPU acceleration as shown by \textcite{han_packetshader_2010}, where they achieved a significant increase in throughput than with a CPU only implementation. Finite element structural analysis is an essential tool for many branches of engineering and can also heavily benefit from the usage of GPUs as demonstrated by \textcite{georgescu_gpu_2013}. 
+
+\subsection{Programming GPUs}
+% This part now starts taking about architecture and how to program GPUs
 talk about the fields GPGPU really helped make performance improvements (weather simulations etc). Then describe how it differs from classical programming. talk about architecture (SIMD/SIMT; a lot of "slow" cores).
 
+starting from here I can hopefully incorporate more images to break up these walls of text
+
 \subsection[PTX]{Parallel Thread Execution}
 Describe what PTX is to get a common ground for the implementation chapter. Probably a short section
 
-% Maybe make this instead of what is there below:
-% \section{Compilers}
-% \subsection{Transpilers}
-% \subsection{Interpreters}
 
-\section{GPU Interpretation}
-Different sources on how to do interpretation on the gpu (and maybe interpretation in general too?)
+\section{Compilers}
+brief overview about compilers (just setting the stage for the subsections basically). Talk about register management and these things
 
-\section{Transpiler}
-talk about what transpilers are and how to implement them. If possible also gpu specific transpilation. Also talk about compilation and register management. and probably find a better title
+\subsection{Interpreters}
+What are interpreters; how they work; should mostly contain/reference gpu interpreters
+
+\subsection{Transpilers}
+talk about what transpilers are and how to implement them. If possible also gpu specific transpilation.