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z;o!q|5Jr&;NnpW9ge1sy*scx~Sun~$`ZR!{k|F}UNTo$|F7(rE|$PDA&o(7hWDjNM+43j-aez&pNASH0Ut3M0z;*gc4**sLh}!6b!;V5(eXZBoQEi^Z}4Lk(knACTp|lA^@TH zgG313N)%xtyr4y`n{_HP#>HHP^sE4zn}$pkO8|rmg+vIS=SQ86`Ah<}Zf3eX=O9q^ zp_qoZ1*pArnBAj94YO|4LNYiiBw;8IQBr_v6D58aO(Sj&Gc9L1IK~PFMu-j{aN|}n z_Vk2rDo+O%1FTPD4v` q+cq3t6R#%m|6gJazhTGi>u2lZ$NFp?5f4v$iHo?Ur8Nz-xc?98N}NIf diff --git a/Documentation/FinalPresentation/Presentation.tex b/Documentation/FinalPresentation/Presentation.tex index 7829776b..248bd043 100644 --- a/Documentation/FinalPresentation/Presentation.tex +++ b/Documentation/FinalPresentation/Presentation.tex @@ -41,16 +41,16 @@ \section{Introduction} \subsection{Brief Project Overview} \begin{frame} \frametitle{Brief Project Overview} -Take an existing particle simulation toolkit - Geant4 - and have some functions run on a GPU device to improve performance. +Take an existing particle simulation toolkit - Geant4 - and have some functions run on a GPU device to improve performance \end{frame} \subsection{Explanation of Terms} \begin{frame} \frametitle{What is Geant4?} \begin{itemize} -\item Geant4 is a toolkit that is meant to simulate the passage of particles through matter. -\item It has been developed over the years through collaborative effort of many different institutions and individuals. -\item Geant4's diverse particle simulation library has a wide variety of applications including +\item Geant4 is a toolkit that is meant to simulate the passage of particles through matter +\item It has been developed over the years through collaborative effort of many different institutions and individuals +\item Geant4's diverse particle simulation library has a wide variety of applications including: \begin{itemize} \item High energy physics simulations \item Space and radiation simulations @@ -81,38 +81,28 @@ \subsection{Scope} \begin{frame} \begin{itemize} \frametitle{Scope} -\item Make current CPU functions available for use on GPU +\item Make current CPU methods available for use on GPU \begin{itemize} -\item Add appropriate prefixes to function definitions -\item Make use of multiple parallel threads to execute each function +\item Update build system to support compiling and linking with GPU code +\item Rewrite algorithms in parallel fashion to run on GPU \end{itemize} -\item Ensure correctness of each GPU available function by matching results to the corresponding CPU function -\item Compare performance of GPU available functions to CPU functions +\item Ensure correctness of each GPU-available method by matching results to the corresponding CPU method +\item Compare performance of GPU-available methods to CPU methods \end{itemize} \end{frame} \begin{frame} -\frametitle{Possible Implementations} -There were initially five possible implementations to reach -a solution: +\frametitle{Design Phase -- Possible Implementations} +There were initially two different implementation approaches: \begin{itemize} -\item Port Geant4 to the GPU such that each particle runs in parallel -\item Port all the functions of some class(es) to the GPU, with those functions privatized to the GPU -\item Port some functions of some class(es) to the GPU, memory stored on host, passing mem to device as necessary -\item Port some functions of some class(es) to the GPU, memory stored and updated on host and device -\item Port some functions of some class(es) to the GPU, data divided between host and device, passing mem as necessary -\end{itemize} -\end{frame} - -\begin{frame} -\frametitle{Solution Choice} +\item Port much of Geant4 to the GPU such that each particle runs in parallel \begin{itemize} -\item Implementation 1 was believed to be unreasonable given schedule/resource limitations -\item Implementation 5 was found to be most suitable +\item Unreasonable given schedule/resource limitations +\end{itemize} +\item Port all methods in some modules to the GPU, storing all relevant data in GPU memory \begin{itemize} -\item Easy to switch between CPU \& GPU versions -\item Less memory usage than other implementations -\item Least redundancy in computation +\item Easy to switch between CPU \& GPU implementations +\item Supports splitting up work by module and by method, and working incrementally \end{itemize} \end{itemize} \end{frame} @@ -120,9 +110,9 @@ \subsection{Scope} \subsection{Purpose} \begin{frame} \begin{itemize} -\item Determine if target functions are suitable to parallelization -\item Increase performance of functions when run on GPU -\item Decrease time required to run simulations involving ported functions +\item Determine if target methods are suitable to parallelization +\item Increase performance of methods when run on GPU +\item Decrease time required to run simulations involving ported methods \end{itemize} \frametitle{Purpose} \end{frame} @@ -131,6 +121,7 @@ \subsection{Purpose} \section{Features} \begin{frame} \frametitle{Features} +Overview of Main Features: \begin{itemize} \item GPU acceleration available on an ``opt-in'' basis \item Easy to enable/disable GPU acceleration @@ -158,10 +149,7 @@ \subsection{Easily Enable/Disable GPU Acceleration} \begin{frame}[fragile] \frametitle{Easily Enable/Disable GPU Acceleration} -Method calls to \texttt{G4ParticleHPVector} forwarded to GPU-based implementation -\begin{itemize} -\item This decision is made at compile time based on \texttt{cmake} flag -\end{itemize} +Methods with GPU versions forwarded to GPU-based implementation at compile time \begin{block}{Example of Forwarding Method Calls} \begin{lstlisting} @@ -187,9 +175,10 @@ \subsection{Easily Enable/Disable GPU Acceleration} \end{frame} \begin{frame} -\frametitle{Why \texttt{G4ParticleHPVector}?} +\frametitle{\texttt{G4ParticleHPVector} Overview} +Represents empirically-found probabilities of collisions for different particles based on their energy + \begin{itemize} -\item Represents empirically-found probabilities of collisions for different particles based on their energy \item Identified as starting point by relevant stakeholders \begin{itemize} \item Used heavily in simulations run by stakeholders @@ -224,17 +213,17 @@ \subsection{Easily Enable/Disable GPU Acceleration} \subsection{Impl. 1: Existing Module in GPU Memory} \begin{frame} \frametitle{Impl. 1: Existing Module in GPU Memory} -Calls to \texttt{G4ParticleHPVector} forwarded to new GPU-based class\\~\\ % hack to get new line in Beamer +Calls to \texttt{G4ParticleHPVector} forwarded to new GPU-based class\\~\\ \textbf{Pros:} \begin{itemize} -\pro Do not have to maintain a copy of the vector on the CPU -\pro Do not have to maintain a hashed vector -\pro Reduces how much is being copied to the GPU +\pro Do not have to maintain a copy of the vector on the CPU\footnote{CPU cache was implemented later} +\pro Data rarely copied to GPU memory \end{itemize} \textbf{Cons:} \begin{itemize} -\con All methods are run on the GPU +\con All methods are run on the GPU, even if not well-suited to parallelism +\con Return values must be copied from GPU memory to CPU memory (slow) \end{itemize} \end{frame} @@ -249,14 +238,15 @@ \subsection{Impl. 1: Existing Module in GPU Memory} \end{frame} \begin{frame}[fragile] -\frametitle{memcpy optimization} +\frametitle{Caching Data Vector in CPU Memory} +To improve data-copying performance, maintain cache of data in CPU memory as well \begin{itemize} -\item Keep track of which version of the data is most up-to-date -\item Only copy data when it has been modified +\item Only updated when necessary +\item For methods that are not parallelizable, can run on CPU using cached data \end{itemize} \begin{block}{CopyToCpuIfDirty} \begin{lstlisting} -if(isDataDirtyHost){ +if (isDataDirtyHost) { cudaMemcpy(h_theData, d_theData, nEntries); isDataDirtyHost = false; } @@ -268,6 +258,7 @@ \subsection{Impl. 1: Existing Module in GPU Memory} \subsubsection{Implementation of Select Methods on GPU} \begin{frame}[fragile] \frametitle{Impl. 1 -- \texttt{Times}} +Multiplies each element in data vector by factor \begin{block}{Times\_CUDA} \begin{lstlisting} int tid = blockDim.x * blockIdx.x + threadIdx.x; @@ -279,14 +270,15 @@ \subsubsection{Implementation of Select Methods on GPU} \begin{frame}[fragile] \frametitle{Impl. 1 -- \texttt{GetXSec}} +Returns y-value of first point with energy at least \texttt{e} parameter \begin{block}{GetXSec\_CUDA} \begin{lstlisting} -int start = (blockDim.x * blockIdx.x + threadIdx.x); -for (int i = start; i < nEntries; i += numThreads) +int start = blockDim.x * blockIdx.x + threadIdx.x; +for (int i = start; i < nEntries; i += numThreads) if (theData[i].energy >= e) { resultIndex = Min(resultIndex, i); return; - } + } \end{lstlisting} \end{block} \end{frame} @@ -295,10 +287,10 @@ \subsubsection{Impl. 1: Performance} \begin{frame} \frametitle{Impl. 1: Performance Results Summary} \begin{itemize} -\item Most methods slower on GPU until \textapprox 10,000 entries in data vector +\item Methods generally slower on GPU until \textapprox 10,000 entries in data vector \item Most \emph{commonly-used} methods significantly slower on GPU, even with large data vector \begin{itemize} -\item Lots of data accesses +\item Lots of data copying \end{itemize} \item Many problems in vector class not well-suited to parallelism \end{itemize} @@ -306,9 +298,7 @@ \subsubsection{Impl. 1: Performance} \begin{frame} \frametitle{Impl. 1: Performance Results -- \texttt{Times}} -\begin{itemize} -\item Multiplies each point in vector by factor -\end{itemize} +Multiplies each point in vector by factor \begin{figure} \centering \includegraphics[width=0.8\textwidth]{images/times_line.png} @@ -318,6 +308,7 @@ \subsubsection{Impl. 1: Performance} \begin{frame} \frametitle{Impl. 1: Performance Results -- \texttt{GetXSec}} +Returns y-value of first point with energy at least `\texttt{e}' \begin{figure} \centering \includegraphics[width=0.8\textwidth]{images/getxsec_e_line.png} @@ -327,6 +318,7 @@ \subsubsection{Impl. 1: Performance} \begin{frame} \frametitle{Impl. 1: Performance Results -- \texttt{SampleLin}} +Interpolates between two random, consecutive points and their corresponding integrals \begin{figure} \centering \includegraphics[width=0.8\textwidth]{images/samplelin_line.png} @@ -343,22 +335,9 @@ \subsubsection{Impl. 1: Performance} \bf CPU Time&\bf GPU Time&\bf Speedup of GPU\\\midrule 54.55s&72.08s&-1.32$\times$\\\bottomrule \end{tabular} - \caption{Performance - Water, 2000 events} + \caption{Performance - Water, 500 events} \end{table} System Test \#2: -\begin{table} - \begin{tabular}{lll} - \toprule - \bf CPU Time&\bf GPU Time&\bf Speedup of GPU\\\midrule - 54.55s&72.08s&-1.32$\times$\\\bottomrule - \end{tabular} - \caption{Performance - Uranium, 2000 events} -\end{table} -\end{frame} - -\begin{frame} -\frametitle{Impl. 1: Performance Results -- System Tests (Cont.)} -System Test \#3: \begin{table} \begin{tabular}{lll} \toprule @@ -367,15 +346,7 @@ \subsubsection{Impl. 1: Performance} \end{tabular} \caption{Performance - Water, 2000 events} \end{table} -System Test \#4: -\begin{table} - \begin{tabular}{lll} - \toprule - \bf CPU Time&\bf GPU Time&\bf Speedup of GPU\\\midrule - 54.55s&72.08s&-1.32$\times$\\\bottomrule - \end{tabular} - \caption{Performance - Uranium, 2000 events} -\end{table}\end{frame} +\end{frame} \begin{frame} \frametitle{Impl. 1: Performance Discussion} @@ -383,7 +354,7 @@ \subsubsection{Impl. 1: Performance} \item Simple ``getters'' and ``setters'' now require copy from GPU to CPU memory \item Current code calling \texttt{G4ParticleHPVector} more data-oriented than computation-oriented \item Low \texttt{GetXSec} performance due to lack of \texttt{Hash} object on GPU to accelerate finding min index -\item Although some functions faster, rarely used in practice +\item Although some methods faster, rarely used in practice \end{itemize} \end{frame} @@ -395,18 +366,18 @@ \subsection{Impl. 2: Add New GPU-Accelerated Methods to Interface} \textbf{Pros:} \begin{itemize} \pro Only methods that run faster on the GPU are implemented -\pro Not forced to run methods that run slowly on GPU \end{itemize} \textbf{Cons:} \begin{itemize} -\con Will have to maintain two copies of the vector -\con More copying the vector to and from the GPU +\con Will have to maintain two copies of the data vector +\con Will need to copy the vector to the GPU whenever method called \end{itemize} \end{frame} \begin{frame} \frametitle{Impl. 2: \texttt{GetXSecList}} +\texttt{GetXSecList} Overview \begin{itemize} \item Fill an array of energies for which we want the cross section values for \item Send the array to the GPU to work on @@ -419,10 +390,9 @@ \subsection{Impl. 2: Add New GPU-Accelerated Methods to Interface} \begin{block}{GetXSecList} \begin{lstlisting}[language=C++,basicstyle=\ttfamily,keywordstyle=\color{red}] stepSize = sqrt(nEntries); -i = 0; e = queryList[threadID]; -for (i = 0; i < nEntries; i += stepSize) +for (int i = 0; i < nEntries; i += stepSize) if (d_theData[i].energy >= e) break; \end{lstlisting} @@ -430,7 +400,7 @@ \subsection{Impl. 2: Add New GPU-Accelerated Methods to Interface} \end{frame} \begin{frame}[fragile] -\frametitle{Implementation -- \texttt{GetXSecList -- cont}} +\frametitle{Implementation -- \texttt{GetXSecList} Cont.} \begin{block}{GetXSecList -- cont} \begin{lstlisting}[language=C++, basicstyle=\ttfamily, keywordstyle=\color{red}] i = i - (stepSize - 1); @@ -449,10 +419,10 @@ \subsubsection{Impl. 2: Performance} \begin{frame} \frametitle{Impl. 2: Performance Results Summary} Performance of implementation 2 also proved slower than -original Geant4 implementations of ParticleHPVector +similar CPU-based method \begin{itemize} -\item Buffered implementation begins to taper off, but at a -much slower rate than the original +\item Performance on GPU linear to number of elements in data array +\item Performance on CPU not affected by number of elements after point, due to saved hashes \end{itemize} \end{frame} @@ -468,9 +438,9 @@ \subsubsection{Impl. 2: Performance} \begin{frame} \frametitle{Impl. 2: Performance Discussion} \begin{itemize} -\item CPU implemenation makes use of \texttt{Hash} to quickly find minimum index +\item CPU implementation makes use of \texttt{Hash} to quickly find minimum index \item Finding first element satisfying predicate not well-suited to parallelism -\item If one thread finds element, must wait for all other threads (blocked ifs) +\item If one thread finds element, must wait for all other threads (blocking divergence) \end{itemize} \end{frame} @@ -478,30 +448,33 @@ \subsection{Accuracy / Testing} \begin{frame} \frametitle{Accuracy} \begin{itemize} -\item All modified functions except SampleLin and Sample +\item All modified methods except \texttt{SampleLin} and \texttt{Sample} yield results that precisely match original implementations -\end{itemize} \begin{itemize} -\item Some functions fell extremely close in accuracy to -the original, and were considered to 'pass' -\item The average of 1000 SampleLin tests deviated from the -average of 1000 tests of the original with an error of 0.01 +\item Some methods fell extremely close in accuracy to +the original, and were considered to `pass' +\item For \texttt{Sample} and \texttt{SampleLin}, the average of 1000 tests was compared, with a relative error tolerance of 0.01 \end{itemize} + +\item The system test results differ with more than 500 events \begin{itemize} -\item The system tests differ if the number of nentries is -greater than 500; if not however the results of the system -test conform. +\item \texttt{Sample} and \texttt{SampleLin} only called if more than 500 events +\end{itemize} \end{itemize} \end{frame} \begin{frame} \frametitle{Accuracy Discussion} \begin{itemize} -\item The deviations in SampleLin and Sample can be -attributed to the functions use of random numbers -\item The negligable deviations in other ported functions -are likely attributed to differences in CPU and GPU -arithmetic, leading to different round-off errors +\item The deviations in \texttt{SampleLin} and \texttt{Sample} can be +attributed to their use of random numbers +\begin{itemize} +\item CUDA random number generator will have different results than \texttt{rand()} +\item Both methods take random point and interpolate it and its neighbour, so values differ significantly based on random number +\end{itemize} +\item The negligible deviations in other ported methods +are attributed to small differences in CPU and GPU +arithmetic round-off errors (\texttt{log}, \texttt{exp}, etc.) \end{itemize} \end{frame} @@ -514,16 +487,15 @@ \subsection{Accuracy / Testing} \item \texttt{GenerateTestResults:} Run unit tests and save results to file \item \texttt{AnalyzeTestResults:} Compare results from CPU and GPU \end{enumerate} -\item Run \texttt{GenerateTestResults} once for GPU acceleration enabled, once with it disabled +\item Run \texttt{GenerateTestResults} once with GPU acceleration enabled, once with it disabled \end{itemize} \end{frame} \begin{frame}[fragile] \frametitle{\texttt{GenerateTestResults} Details} \begin{itemize} -\item Includes testing version number in results file for analysis stage \item Outputs simple results directly to results file -\item For vectors, calculates hash for vector and output it +\item For arrays, calculates hash for array and output it \item Outputs timing data to separate file \end{itemize} \begin{block}{Example: Snippet of Generated Test Results} @@ -538,13 +510,6 @@ \subsection{Accuracy / Testing} \end{block} \end{frame} -\begin{frame} -\frametitle{Demonstration} -\begin{center} -\emph{Demonstration -- Generating Test Results} -\end{center} -\end{frame} - \begin{frame} \frametitle{\texttt{AnalyzeTestResults} Details} Two main functions: @@ -552,11 +517,11 @@ \subsection{Accuracy / Testing} \item Compare results for each test case, printing status to \texttt{stdout} \begin{itemize} \item If test failed, output differing values -\item Summarize test results at the end with number passed +\item Summarize test results at the end \end{itemize} \item Generate \texttt{.csv} file from timing data \begin{itemize} -\item One row per unique method call, columns show CPU time, GPU time, method name and parameters +\item One row per unique method call comparing CPU and GPU times \item Can use Excel to analyze performance results \end{itemize} \end{enumerate} @@ -574,40 +539,32 @@ \subsection{Summary of Results} \begin{frame} \frametitle{Summary of Results} \begin{itemize} -\item Both Implementations are on average slower than the CPU -\item Most methods slower on GPU until ~10,000 entires in data vector -\item Most commonly-used methods significantly slower on GPU, even with large data vector -\begin{itemize} -\item Lots of data accesses -\end{itemize} -\item SampleLin has accuracy issues due to random number generation +\item Impl. 1 was about 1.3X slower on average in system tests +\item Impl. 2 was about 4X slower in unit tests +\item Most commonly-used methods not well-suited to parallelism +\item \texttt{Sample} and \texttt{SampleLin} have accuracy issues due to random number generation \end{itemize} \end{frame} \subsection{Recommendations} \begin{frame} -\frametitle{Recommendations} -For further work with regards to ParticleHPVector: +For further work parallelizing Geant4: \begin{itemize} -\item Abstact further up the Geant4 system, parallelizing -components that make reference to NeutronHPVector -\item This will decrease the frequency of data transfer -between the host and device -\item Up-to-date work can be found on out github, along -with instructions for installing and testing +\item Use Geant4-GPU project as framework for parallelizing other modules +\item Look for modules storing large amounts of structured data +\item Methods with nested loops are prime candidates for parallelization +\item Probabilistic methods and getter/setter methods won't have considerable benefits +\item Methods with extensive conditional branching may cause difficulties in parallelizing \end{itemize} \end{frame} +\subsection{Final Thoughts} \begin{frame} -For further work with regards to parallelizing Geant4: +\frametitle{Conclusion} \begin{itemize} -\item Try parallelizing other commonly use components in similar style -\begin{itemize} -\item Look for classes manipulating list-style data structures -\item Classes with functions that have nested loops or are heavy in computing are prime candidates -\item Probabilistic functions and getter/setter functions won't have considerable benefits -\item Functions with conditional branching may cause bottlenecks in parallelization -\end{itemize} +\item All project collaborators have gained a lot of experience +\item Lack of speedup disappointing, but lets us know \texttt{G4ParticleHPVector} not perfect candidate +\item Parallelization of existing software is hard \end{itemize} \end{frame}