From 59de4b0bdb00f652c2a7c8cc5f53e2f382bb1891 Mon Sep 17 00:00:00 2001 From: couturie Date: Sat, 1 Dec 2012 06:38:45 +0100 Subject: [PATCH] ch14 --- BookGPU/BookGPU.tex | 1 + BookGPU/Chapters/chapter14/ch14.tex | 1169 +++++++++++++++++ .../chapter14/figures/Ludwig_two_graphs.pdf | Bin 0 -> 202453 bytes .../chapter14/figures/basiclattice.pdf | Bin 0 -> 111941 bytes BookGPU/Chapters/chapter14/figures/bbl.pdf | Bin 0 -> 64459 bytes .../chapter14/figures/colloid_new.pdf | Bin 0 -> 8419 bytes .../Chapters/chapter14/figures/decomphalo.pdf | Bin 0 -> 1594878 bytes .../chapter14/figures/length_scale.pdf | Bin 0 -> 357312 bytes .../chapter14/figures/particlemove.pdf | Bin 0 -> 57133 bytes .../chapter14/figures/two_graphs_ud.pdf | Bin 0 -> 83066 bytes BookGPU/Makefile | 1 + 11 files changed, 1171 insertions(+) create mode 100755 BookGPU/Chapters/chapter14/ch14.tex create mode 100644 BookGPU/Chapters/chapter14/figures/Ludwig_two_graphs.pdf create mode 100644 BookGPU/Chapters/chapter14/figures/basiclattice.pdf create mode 100644 BookGPU/Chapters/chapter14/figures/bbl.pdf create mode 100644 BookGPU/Chapters/chapter14/figures/colloid_new.pdf create mode 100644 BookGPU/Chapters/chapter14/figures/decomphalo.pdf create mode 100644 BookGPU/Chapters/chapter14/figures/length_scale.pdf create mode 100644 BookGPU/Chapters/chapter14/figures/particlemove.pdf create mode 100644 BookGPU/Chapters/chapter14/figures/two_graphs_ud.pdf diff --git a/BookGPU/BookGPU.tex b/BookGPU/BookGPU.tex index a2908c0..a06c9c9 100755 --- a/BookGPU/BookGPU.tex +++ b/BookGPU/BookGPU.tex @@ -107,6 +107,7 @@ \include{Chapters/chapter1/ch1} \include{Chapters/chapter2/ch2} \include{Chapters/chapter11/ch11} +\include{Chapters/chapter14/ch14} \bibliographystyle{hep} %%%\bibliography{biblio} diff --git a/BookGPU/Chapters/chapter14/ch14.tex b/BookGPU/Chapters/chapter14/ch14.tex new file mode 100755 index 0000000..38f7f6f --- /dev/null +++ b/BookGPU/Chapters/chapter14/ch14.tex @@ -0,0 +1,1169 @@ +\chapterauthor{Alan Gray and Kevin Stratford}{EPCC, The University of Edinburgh} + +\chapter{Ludwig: multiple GPUs for a complex fluid lattice Boltzmann +application} + +%\putbib[biblio] + + +\section{Introduction} +The lattice Boltzmann (LB) method (for an overview see, e.g., +\cite{succi-book}) has become a popular approach to a variety of fluid +dynamics problems. It provides a way to solve the incompressible, +isothermal Navier-Stokes equations and has the attractive features of +being both explicit in time and local in space. This makes the LB +method well-suited to parallel computation. Many efficient parallel +implementations of the LB method have been undertaken, typically using +a combination of distributed domain decomposition and the Message +Passing Interface (MPI). However, the potential +performance benefits offered by GPUs has motivated a new `mixed-mode' +approach to address very large problems. Here, fine-grained +parallelism is implemented on the GPU, while MPI is reserved for +larger-scale parallelism. This mixed mode is of increasing interest +to application programmers at a time when many supercomputing services +are moving +toward clusters of GPU accelerated nodes. The design questions which +arise when developing a lattice Boltzmann code for this type of +heterogeneous system are therefore worth studying. Furthermore, similar +questions also recur in many other types of stencil-based algorithms. + +The first applications of LB on GPUs were to achieve fluid-like +effects in computer animation, rather than scientific applications per +se. These early works include simple fluids~\cite{wei2004}, miscible +two-component flow~\cite{zhu2006}, and various image processing tasks +based on the use of partial differential equations~\cite{zhao2007}. +While these early works used relatively low level graphics APIs, the +first CUDA runtime interface implementation was a two-dimensional +simple fluid problem~\cite{toelke2010}. Following pioneering work on +clusters of GPUs coupled via MPI to study air +pollution~\cite{fan2004}, more recent work has included mixed OpenMP +and CUDA~\cite{myre2011}, Posix threads and CUDA~\cite{obrecht2011}, +and MPI and CUDA for increasingly large GPU clusters +\cite{bernaschi2010,xian2011,feichtinger2011}. The heterogeneous +nature of these systems has also spurred interest in approaches +including automatic code generation \cite{walshsaar2012} and auto-tuning +\cite{williams2011} to aid application performance. + +Many of these authors make use of +another attractive feature of LB: the ability to include fixed +solid-fluid boundary conditions as a straightforward addition to +the algorithm to study, for example, flow in +porous media. This points to an important +application area for LB methods: that of complex fluids. +Complex fluids include mixtures, surfactants, liquid crystals, +and particle suspensions, and typically require additional physics +beyond the bare Navier-Stokes equations to provide a full +description~\cite{aidun2010}. The representation of this extra +physics raises additional design questions for the application +programmer. Here, we consider the \textit{Ludwig} code \cite{desplat}, +an LB application developed specifically for complex fluids +(\textit{Ludwig} was named for Boltzmann, 1844--1906). +We will present the steps +required to allow \textit{Ludwig} to exploit efficiently both a single +GPU, and also many GPUs in parallel. We show that +\textit{Ludwig} scales excellently to at least the one thousand GPU level +(the largest resource available at the time of writing) with +indications that the code will scale to much larger systems as +they become available. In addition, we consider the steps required +to represent fully-resolved moving solid particles (usually referred +to as colloids in the context of complex fluids). Such particles +need to have their surface resolved on the lattice scale in order +to include relevant surface physics, and must be able to move: e.g., +to execute Brownian motion in response to random forces from the +fluid. Standard methods are available to represent such particles +(e.g., \cite{ladd1994,nguyen2002}) which are amenable to effective +domain decomposition and message passing \cite{stratford2008}. +We describe below the additional considerations which arise +for such moving particles when developing an +implementation on a GPU. + +In the following section we provide a brief overview of the lattice +Boltzmann method, and mention some of the general issues which can +influence performance in the CPU context. +In Section \ref{ch14:sec:singlegpu}, we then describe the alterations +which are required to exploit the GPU architecture effectively, and +highlight how the resulting code differs from the CPU version. In +Section \ref{ch14:sec:parallelgpu}, we extend this description to +include the steps required to allow exploitation of many GPUs in +parallel while retaining effective scaling. We also present results for +a typical benchmark for a fluid-only problem in Section +\ref{ch14:sec:parallelgpu} to demonstrate the success of the approach. +In Section \ref{ch14:sec:particles}, we describe the design choices +which are relevant to a GPU implementation of moving particles. Finally, +we include a number of general observations on software +engineering and maintenance which arise from our experience. +A summary is provided in Section~\ref{ch14:sec:summary}. + + +\section{Background}\label{ch14:sec:background} + +%\begin{figure}[!t] +%\centering +%\includegraphics[width=12cm]{Chapters/chapter14/figures/basiclattice} +%\caption{The lattice Boltzmann approach discritises space into a 3D lattice (left). The fluid is represented at each lattice site (right), by the {\it distribution} vector: each component corresponding to a specific velocity direction. Here we are interested in the {\it D3Q19} model, in which there are 3 spatial directions and 19 velocity components (including a zero component).} +%\label{ch14:fig:basiclattice} +%\end{figure} + + +For a general complex fluid problem the starting point is the +fluid velocity field $\mathbf{u}(\mathbf{r})$, whose evolution +obeys the Navier-Stokes equations describing the conservation of mass +(or density $\rho$), and momentum: +\begin{equation} +\rho [ \partial_t \mathbf{u} + (\mathbf{u}.\mathbf{\nabla})\mathbf{u} ] += -\nabla p + \eta \nabla^2 \mathbf{u} + \mathbf{f}(\mathbf{r}), +\end{equation} +where $p$ is the isotropic pressure and $\eta$ is the viscosity. +A local force $\mathbf{f}(\mathbf{r})$ provides a means for coupling +to other complex fluid constituents, e.g., it might represent the force +exerted on the fluid by a curved interface between different phases or +components. + +The LB approach makes use of a regular three-dimensional +lattice (see Figure \ref{ch14:fig:decomphalo}) with discrete spacing +$\Delta r$. It also makes use of a +discrete velocity space $\mathbf{c}_i$, where the $\mathbf{c}_i$ +are chosen to capture the correct symmetries of the Navier-Stokes +equations. A typical choice, used here, is the so-called D3Q19 +basis in three dimensions where there is one velocity such that +$\mathbf{c} \Delta t$ is zero, along with six extending to the nearest +neighbour +lattice sites, and twelve extending to the next-nearest neighbour sites +($\Delta t$ being the discrete time step). The fundamental object +in LB is then the distribution function $f_i (\mathbf{r};t)$ whose +moments are related to the local hydrodynamic quantities: the fluid +density, momentum, and stress. The time evolution of the distribution +function is described by a discrete Boltzmann equation +\begin{equation} +f_i(\mathbf{r} + \mathbf{c}_i \Delta t; t) - f_i(\mathbf{r}; t) += - {\cal L}_{ij} f_j(\mathbf{r};t). +\end{equation} +It is convenient to think of this in two stages. First, the right hand +side represents the action of a collision operator ${\cal L}_{ij}$, +which is local to each lattice site and relaxes the distribution toward +a local equilibrium at a rate ultimately related to the fluid viscosity. +Second, the left hand side represents a propagation step (sometimes referred +to as streaming step), in which each element $i$ of the distribution is +displaced $\mathbf{c}_i \Delta t$, i.e., one lattice spacing in the +appropriate direction per discrete time step. + +More specifically, we store a vector of 19 double precision floating +point values at each lattice site for the distribution function +$f_i(\mathbf{r};t)$. +The collision operation ${\cal L}_{ij}$, which is local at each lattice +site, may be thought of as follows. A matrix-vector multiplication +${\cal M}_{ij}f_j$ is used to transform the distributions into the +hydrodynamic quantities, where ${\cal M}_{ij}$ is a constant 19x19 +matrix related to the choice of +$\mathbf{c}_i$. The non-conserved hydrodynamic quantities are then +relaxed toward their (known) equilibrium values, and are transformed +back to new post-collision distributions via the inverse transformation +${\cal M}^{-1}_{ij}$. This gives rise to the need for a minimum of 2x19$^2$ +floating point multiplications per lattice site. (Additional operations are +required to implement, for example, the force $\mathbf{f}(\mathbf{r})$.) +In contrast, the +propagation stage consists solely of moving the distribution values +between lattice sites, and involves no floating point operations. + +In the CPU version, the \textit{Ludwig} implementation stores one time +level of distribution values $f_i(\mathbf{r}; t)$. This distribution +is stored as a flat array of C data type \texttt{double}, and laid out +so that all elements of the velocity are contiguous at a given site +(often referred to as ``array-of-structures'' order). This is +appropriate for sums over the distribution required locally at the +collision stage as illustrated schematically in +Listing~\ref{ch14:listing1}: the fact that consecutive loads are from +consecutive memory addresses allows the prefetcher to engage fully. +(The temporary scalar \texttt{a\_tmp} allows caching of the +intermediate accumulated value in the innermost loop.) A variety of +standard sequential optimisations are relevant for the collision stage +(loop unrolling, inclusion of SIMD operations, and so on +\cite{wellein2006}). For example, the collision stage in +\textit{Ludwig} includes explicit SIMD code, which is useful if the +compiler on a given platform cannot identify it. The propagation +stage is separate, and is organised as a ``pull'' (again, various +optimisation have been considered, e.g., +\cite{pohl2003,mattila2007,wittmann2012}). No further optimisation is +done here beyond ensuring that the ordering of the discrete velocities +allows memory access to be as efficient as possible. While these +optimisations are important, it should be remembered that for some +complex fluid problems, the hydrodynamics embodied in the LB +calculation is a relatively small part of the total computational cost +(at the 10\%-level in some cases). This means optimisation effort may +be better concentrated elsewhere. + +\begin{lstlisting}[float, label=ch14:listing1, +caption = Collision schematic for CPU.] +/* loop over lattice sites */ +for (is = 0; is < nsites; is++) { + ... + /* load distribution from ``array-of-structures'' */ + for (i = 0; i < 19; i++) + f[i] = f_aos[19*is + i] + ... + /* perform matrix-vector multiplication */ + for (i = 0; i < 19; i++) { + a_tmp = 0.0; + for (j = 0; j < 19; j++) { + a_tmp += f[j]*M[i][j]; + } + a[i] = a_tmp; + } + ... +} +\end{lstlisting} + + +\begin{figure}[!t] +\centering +\includegraphics[width=12cm]{Chapters/chapter14/figures/decomphalo} +\caption{Left: the lattice is decomposed between MPI tasks. For + clarity we show a 2D decomposition of a 3D lattice, but in practice + we decompose in all 3 dimensions. Halo cells are added to each + sub-domain (as shown on the upper right for a single slice) which store + data retrieved from remote neighbours in the halo exchange. Lower + right: the D3Q19 velocity set resident on a lattice site; + highlighted are the 5 ``outgoing'' elements to be transferred in a + specific direction.} +\label{ch14:fig:decomphalo} +\end{figure} + + +The regular 3D decomposition is illustrated in Fig.~\ref{ch14:fig:decomphalo}. +Each local sub-domain is surrounded by a halo, or ghost, region of width one +lattice site. While the collision is local, elements of the distribution +must be exchanged at the edges of the domains to facilitate the propagation. +To achieve the full 3D halo exchange, the standard approach of shifting the +relevant data in each co-ordinate direction in turn is adopted. This +requires appropriate synchronisation, i.e., a receive in the the first +co-ordinate direction must be complete before a send in the second direction +involving relevant data can take place, and so on. We note that only +``outgoing'' elements of the distribution need to be sent at each edge. +For the D3Q19 model, this reduces the volume of data traffic from 19 to +5 of the $f_i(\mathbf{r};t)$ per lattice site at each edge. In the CPU +version, the necessary transfers are implemented in place using +a vector of appropriately strided MPI datatypes for each direction. + + +\section{Single GPU Implementation}\label{ch14:sec:singlegpu} + + +In this section we describe the steps taken to enable \textit{Ludwig} +for the GPU. There are a number of crucial issues: first, the +minimisation of data traffic between host and device; second, the +optimal mapping of available parallelism onto the architecture and +third, the issue of memory coalescing. We discuss each of these in +turn. + +While the most important section of the LB in terms of +floating-point performance is the collision stage, this cannot be the +only consideration for a GPU implementation. It is essential to +offload all computational activity which involves the main data +structures (such as the distribution) to the GPU. This includes +kernels with relatively low computational demand, such as the +propagation stage. All relevant data then remain resident on the GPU, +to avoid expensive host-device data transfer at each iteration of +the algorithm. Such transfers would negate any benefit of GPU +acceleration. We note that for a complex fluid code, this requirement +can extend to a considerable number of kernels, although we limit the +discussion to collision and propagation for brevity. + +To achieve optimal performance, it is vital to exploit fully the +parallelism inherent in the GPU architecture, particularly for +those matrix-vector operations within the collision kernel. +The GPU architecture features a hierarchy of parallelism. At the +lowest level, groups of 32 threads (warps) operate in +lock-step on different data elements: this is SIMD style vector-level +parallelism. Multiple warps are combined into a thread block (in which +communication and synchronisation are possible), and multiple blocks can +run concurrently across the streaming multiprocessors in the GPU +(with no communication or +synchronisation possible across blocks). To decompose on the GPU, we +must choose which part of the collision to assign to which level of +parallelism. + +While there exists parallelism within the matrix-vector operation, the +length of each vector (here, 19) is smaller than the warp size and typical +thread block sizes. So we simply decompose the loop over lattice sites +to all levels of parallelism, i.e., we use a separate CUDA thread for +every lattice site, and each thread performs the full matrix-vector +operation. We find that a block size of 256 performs well on current +devices: we therefore decompose +the lattice into groups of 256 sites, and assign each group to a block +of CUDA threads. As the matrix ${\cal M}_{ij}$ is constant, it is +assigned to the fast {\it constant} on-chip device memory. + +For the propagation stage, the GPU implementation adds a second time +level of distribution values. The data-dependencies inherent in the +propagation mean that the in-place propagation of the CPU version +cannot be parallelised effectively without the additional time level. +As both time levels may remain resident on the GPU, this is not a +significant overhead. + +An architectural +constraint of GPUs means that optimal global memory bandwidth +is only achieved when data are structured such that threads within a +{\it half-warp} (a group of 16 threads) load data from the same memory +segment in a single transaction: this is memory coalescing. It can be +seen that the array-of-structures ordering used for the distribution +in the CPU code would not be suitable for coalescing; in fact, it would +result in serialised memory accesses and relative poor performance. +To meet the coalescing criteria and allow consecutive threads to read +consecutive memory addresses on the GPU, we transpose the layout of the +distribution so that, for each velocity component, consecutive sites +are contiguous in memory (``structure-of-arrays'' order). A schematic of +the GPU collision code is given in Listing~\ref{ch14:listing2}. + +\begin{lstlisting}[float, label=ch14:listing2, +caption = Collision schematic for GPU.] +/* compute current site index 'is' from CUDA thread and block */ +/* indices and load distribution from "structure-of-arrays" */ + +for (i = 0; i < 19; i++) + f[i] = f_soa[nsites*i + is] + +/* perform matrix-vector multiplication as before */ +... +\end{lstlisting} + + +\textit{Ludwig} was modified to allow a choice of distribution data layout +at compilation time depending on the target architecture: CPU or GPU. We +defer some further comments on software engineering aspects of the code to +the summary. + + +\section{Multiple GPU Implementation}\label{ch14:sec:parallelgpu} + +To execute on multiple GPUs, we use the same domain decomposition +and message passing framework as the CPU version. Within each +sub-domain (allocated to one MPI task) the GPU implementation +proceeds as described in the previous section. The only additional +complication is that halo transfers between GPUs must be staged +through the host (in future, direct GPU to GPU data transfers via +MPI may be possible, obviating the need for these steps). This +means host MPI sends must be preceded by appropriate device to host +transfers and host MPI receives must be followed by corresponding host +to device transfers. + +%To execute on multiple GPUs in parallel we decompose the problem using +%the existing high-level parallelisaton framework: each MPI task acts +%as a host to a separate GPU, with each GPU kernel operating on the +%subset of the lattice volume corresponding to the partition assigned +%to that task. As described in Section \ref{ch14:sec:background}, halo +%data exchanges are required at each timestep. Significant +%developments are made for the GPU adaptation of this communication +%phase to achieve good inter-GPU communication performance and allow +%scaling to many GPUs. + +%The CPU code uses strided MPI datatypes to define the six +%planes of lattice data to be sent or received, and performs the +%communications {\it in-place}, i.e. there is no explicit buffering of +%data in the application. For the GPU adaptation, the halo planes must +%be exchanged between GPUs, on which MPI functionality is not +%available, so transfers must be staged through the host CPUs. Buffer +%packing/unpacking of the halo planes is explicitly performed using +%CUDA kernels, and CUDA memory copy calls are used to transfer data +%from a device to its host (using pinned host memory) through the PCI-e +%bus (and vice versa). MPI calls are used to transfer the buffers +%between hosts. The CPU version of Ludwig has a mechanism, using MPI built-in +%data types, to reduce the amount of data communicated by around a +%factor of four by only sending those elements of the distribution +%propagating outward from a local domain. For the GPU adaptation, this +%filtering of data is explicitly performed (again since MPI +%functionality is not available on the GPU) in the buffer +%packing/unpacking kernels. Special care had to be taken for those +%lattice sites on the corners of the halo planes, which had to be sent +%in more than one direction (since the propagation includes accesses to +%diagonal neighbours). + +In practice, this data movement requires additional GPU kernels to +pack and unpack the relevant data before and after corresponding MPI +calls. However, the standard shift algorithm, in which each co-ordinate +direction is treated in turn, does provide some scope for the +overlapping of different operations. +For example, after the data for the first co-ordinate direction have been +retrieved by the host, these can be exchanged using MPI between +hosts at the same time as kernels for packing and retrieving of +data for the second co-ordinate direction are +executed. This +overlapping must respect the synchronisation required to ensure +that data values at the corners of the sub-domain are transferred +correctly. +We use a separate CUDA stream for each co-ordinate direction: +this allows some of the host-device communication time to be +effectively ``hidden'' behind the host-host MPI communication, +resulting in an overall speedup. +The improvement is more pronounced for the smaller local lattice size, +perhaps because of less CPU memory bandwidth contention. The +overlapping is then a particularly valuable aid to strong scaling, +where the local system size decreases as the number of GPUs increases. + + +%\section{Performance}\label{ch14:sec:performance} + +To demonstrate the effectiveness of our approach, we compare the +performance of both CPU and GPU versions of \textit{Ludwig}. To +test the complex fluid nature of the code, the problem is +actually an immiscible fluid mixture which uses a second distribution +function to introduce a composition variable. The interested reader +is referred to \cite{ch14:stratford-jsp2005} for further details. The +largest total problem size used is $2548\times 1764\times 1568$. +The CPU system features a Cray XE6 architecture with 2 16-core AMD Opteron +CPUs per node, and with nodes interconnected using Cray Gemini technology. +For GPU results, a Cray XK6 system is used: this is very similar to the +XE6, but has one CPU per node replaced with an NVIDIA X2090 GPU. Each +node in the GPU system therefore features a single Opteron CPU acting +as a host to a single GPU. The inter-node interconnect architecture is +the same as for the Cray XE6. The GPU performance tests use a prototype +Cray XK6 system with 936 nodes (the largest available at the time of writing). +To provide a fair comparison, we compare scaling on a \textit{per node} +basis. That is, we compare 1 fully occupied 32-core CPU node +(running 32 MPI tasks) with 1 GPU node (host running 1 MPI task, +and 1 device). We believe this is representative of the true ``cost'' of +a simulation in terms of accounting budgets, or electricity. + + +%\begin{figure}[!t] +%\centering +%\includegraphics[width=10cm]{Chapters/chapter14/figures/length_scale} +%\caption{The dependence of the characteristic length scale of a binary +% fluid mixture on the simulation timestep number. Superimposed are +% three snapshots showing increased mixture separation with time: +% these are obtained through the order parameter and each show one +% eighth of the total simulation size, which is $1548\times 1764\times 1568$. }\label{ch14:fig:length_scale} +%\end{figure} + +\begin{figure}[!t] +\centering +\includegraphics[width=10cm]{Chapters/chapter14/figures/two_graphs_ud} +\caption{The weak (top) and strong (bottom) scaling of \textit{Ludwig}. Closed + shapes denote results using the CPU version run on the Cray + XE6 (using two 16-core AMD Interlagos CPUs per node), while open + shapes denote results using the GPU version on the Cray XK6 (using a + single NVIDIA X2090 GPU per node). Top: the benchmark time is shown + where the problem size per node is constant. Bottom: performance is + shown where, for each of the four problem sizes presented, the + results are scaled by the lattice volume, and all results are + normalized by the same arbitrary constant. } +\label{ch14:fig:scaling} +\end{figure} + + +%The top of Figure \ref{ch14:fig:scaling} shows the benchmark time, +%where the problem size is increased with the number of nodes. This +%shows weak scaling: the time taken by a perfectly scaling code would +%remain constant. The figure plots the time against the number of {\it +% nodes}: for the GPU version we utilize the one GPU per Cray XK6 node +%whilst for the CPU version we fully utilize two 16-core CPUs +%per Cray XE6 node, i.e. one GPU is compared with 32 CPU cores. We use +%a single MPI task per Cray XK6 node to host the GPU, and 32 MPI tasks +%to fully utilize the CPUs in the Cray XE6 node. Note that our CPU +%version is highly optimised: in particular it has been tuned for +%optimal utilisation of the SIMD vector units in each CPU core. + +Figure \ref{ch14:fig:scaling} shows the results of both weak and +strong scaling tests (top and bottom panels, respectively). For +weak scaling, where the local sub-domain size is kept fixed +(here a relatively large 196$^3$ lattice), the time taken by +an ideal code would remain constant. It can be seen that while +scaling of the CPU version shows a pronounced upward slope, the +GPU version scales almost perfectly. The advantage of the GPU +version over the CPU version is the fact that the GPU version +has a factor of 32 fewer MPI tasks per node, so communications +require a significantly smaller number of larger data transfers. +The performance +advantage of the GPU version ranges from a factor of around 1.5 to +around 1.8. Careful study by other authors \cite{williams2011} have +found the absolute performance (in terms of floating point +operations per second, or floating point operations per Watt) to +be remarkably similar between architectures. + +Perhaps of more interest is the strong scaling picture (lower +panel in Figure \ref{ch14:fig:scaling}) where the performance +as a function of the number of nodes is measured for fixed problem +size. We consider four different fixed problem sizes on both CPU +(up to 512 nodes shown) and GPU (up to 768 nodes). To allow comparison, +the results are scaled by total system size in each case. For strong +scaling, the disparity in the number of MPI tasks is clearly revealed +in the failure of the CPU version to provide any significant benefit +beyond a modest number of nodes as communication overheads dominate. +In contrast, the GPU version shows +reasonably robust scaling even for the smaller system sizes and good +scaling for the larger systems. + +\section{Moving Solid Particles}\label{ch14:sec:particles} + +\begin{figure}[t] +\centering +%\includegraphics[width=10cm]{Chapters/chapter14/figures/bbl} +\includegraphics[width=10cm]{Chapters/chapter14/figures/colloid_new} +\caption{ +A two-dimensional schematic picture of spherical particles on the lattice. +Left: a particle is allowed +to move continuously across the lattice, and the position of the +surface defines fluid lattice sites (light blue) and solid lattice +sites (dark red). The discrete surface is defined by links +where propagation would intersect the surface (arrows). Note the +discrete shape of the two particles is different. Right: post-collision +distributions are reversed at the surface by the process of bounce-back +on links, which replaces the propagation. +%A 2D illustration of colloidal particles (grey circles) +% interacting with the lattice, where each lattice point is +% represented by a cross. Left: each particle spans multiple sites. +% Right: the particle-facing distribution components are moved inside +% the particle with the direction reversed, such that the ``bounce +% back'' will be completed when the fluid propagates. +} +\label{ch14:fig:bbl} +\end{figure} + + +%\begin{figure}[t] +%\centering +%\includegraphics[width=6cm]{Chapters/chapter14/figures/particlemove} +%\caption{A 2D illustration of a colloidal particle moving across the +% lattice. The old and new positions are represented by the open and +% filled circles respectively.} +%\label{ch14:fig:particlemove} +%\end{figure} + +% BEING NEW SOLID TEXT + +The introduction of moving solid particles poses an additional hurdle +to efficient GPU implementation of an LB code such as \textit{Ludwig}. +In this section, we give a brief overview of the essential features +of the CPU implementation, and how the considerations raised in the +previous sections --- maximising parallelism and minimising both +host to device and GPU to GPU data movement --- shape the design decisions +for a GPU implementation. We restrict our discussion to a simple fluid +in this section; additional solid-fluid boundary conditions (e.g., +wetting at a fluid-fluid-solid contact line) usually arise elsewhere +in the calculation and broadly independent of the hydrodynamic boundary +conditions which are described in what follows. + +Moving solid particles (here, spheres) are defined by a centre position +which is allowed to move continuously across the space of the lattice, +and a fixed radius which is typically a few lattice spacings $\Delta r$. +The surface of the particle is defined by a series of \textit{links} +where a discrete velocity propagation $\mathbf{c}_i \Delta t$ would +intercept or cut the spherical shell (see Fig.~\ref{ch14:fig:bbl}). +Hydrodynamic boundary conditions are then implemented via the standard +approach of bounce-back on links \cite{ladd1994, nguyen2002}, where the +relevant post-collision distribution values are reversed at the +propagation stage with an appropriate correction to allow for the +solid body motion. The exchange of momentum at each link must then be +accumulated around the entire particle surface to provide the net +hydrodynamic force +and torque on the sphere. The particle motion can then be updated in a +molecular dynamics-like step. + +For the CPU implementation, a number of additional MPI communications +are required: (1) to exchange the centre position, radius, etc of each +particle as it moves and (2), to allow the accumulation of the force +and torque for each particle (the links of which may be distributed +between up to 8 MPI tasks in three dimensions). Appropriate marshaling +of these data can provide an effective parallelisation for relatively +dense particle suspensions of up to 40\% solid volume fraction +\cite{stratford2008}. As a final consideration, fluid distributions +must be removed and replaced consistently as a given particle moves +across the lattice and changes its discrete shape. + +With these features in mind, we can identify a number of competing +concerns which are relevant to a GPU implementation: +\begin{enumerate} +\item +Minimisation of host-device data transfer would argue for moving the +entire particle code to the GPU. However, the code in question involves +largely conditional logic (e.g., identifying cut surface links) and +irregular memory accesses (e.g., access to distribution elements around +a spherical particle). These operations seem poorly suited to effective +parallelisation on the GPU. As an additional complication, the sums +required over the particle surface would involve potentially tricky +and inefficient reductions in GPU memory. +\item +The alternative is to retain the relevant code on the CPU. While the +transfer of the entire distribution $f_i(\mathbf{r};t)$ between host +and device at each time step is unconscionable owing to PCIe bus bandwidth +considerations, the transfer of only relevant distribution information +to allow bounce-back on links is possible. At modest solid volumes +only a very small fraction of the distribution data is +involved in bounce-back (e.g., a 2\% solid volume fraction of particles +radius 2.3$\Delta r$ would involve approximately 1\% of the distribution +data). This option also has the advantage that no further host-device +data transfers are necessary to allow the MPI exchanges required for +particle information. +\end{enumerate} + + +We have implemented the second option as follows. For each sub-domain, +a list of boundary-cutting links is assembled on the CPU which includes +the identity of the relevant element of the distribution. This list, +together with the particle information required to compute the correct +bounce-back term, are transferred to the GPU. The updates to the relevant +elements of the distribution can then take place on the GPU. The +corresponding information to compute the update of the particle dynamics +is returned +to the CPU, where the reduction over the surface links is computed. +The change of particle shape may be dealt with in a similar manner: +the relatively small number of updates required at any one time step +(or however frequently the particle position is updated) can be +marshaled to the GPU as necessary. This preliminary implementation +is found to be effective on problems involving up to 4\% +solid volume fraction. + +We note that the CPU version actually avoids the collision calculation +at solid lattice points by consulting a look-up table of solid/fluid +status. On the GPU, it is perhaps preferable to perform the collision +stage everywhere instead of moving the look-up table to the GPU and +introducing the associated logic. +Ultimately, the GPU might favour other boundary methods which treat solid and +fluid on a somewhat more equal basis, for example, the immersed boundary +method \cite{ch14:immersed,ch14:immersed-lb} or smoothed profile method +\cite{ch14:spm}. +However, the approach adopted here allows us to exploit +the GPU for the intensive fluid simulation whilst maintaining the complex +code required for particles on the CPU. Overheads of CPU-GPU transfer are +minimised by transferring only those data relevant to the hydrodynamic +interaction implemented via bounce-back on links. + +% END NEW SOLID TEXT + +%It is of interest, in many situations, to include colloidal particles +%in the simulation **more from Kevin***. These are by represented by +%moving spheres that interact with the fluid through transfer of +%momenta: fluid coming into contact with a particle will bounce off it, +%giving the particle a kick in the process. As illustrated on the left +%of Figure \ref{ch14:fig:bbl} (which shows 2 dimensions only for +%clarity - in practice, the particles will be represented by spheres on +%a 3D lattice, and will be much larger relative to the lattice +%spacing), typically each particle spans multiple lattice sites, and +%therefore ``intercepts'' multiple inter-site links. Ludwig stores +%particle information using nested linked lists: a list of particles, +%each with a list of all the links intercepted by that particle (plus +%other quantities). The fluid-particle interaction proceeds using the +%so-called {\it bounce back on links} procedure during each timestep. As +%illustrated in the right of Figure \ref{ch14:fig:bbl}, for each of the +%intercepting links, those velocity components of the distribution that +%face inwards to each particle are moved from the site +%outside the particle to the site inside the particle, and +%reversed in direction. This is done before the propagation stage, +%which then propagates these same velocity components back out of the +%particles: the velocity bounces back from the particles. The +%momenta transfer from fluid to particles is also determined +%from the distribution values. + +%A simplified schematic of the implementation on the CPU is as follows, +%noting that multiplicative coefficients are required: +%{\footnotesize +%\begin{verbatim} +%For each particle: +% For each link: +% Calculate coefficient +% Bounce back fluid using coefficient +% Update particle momentum +%\end{verbatim} +%} +%This process is relatively inexpensive on the CPU, since the total +%number of particles (and intercepted links) is always relatively +%small, but it does require access to distribution data which we want +%to keep resident on the GPU throughout the simulation. The overhead of +%transferring the full distribution to the CPU and back every timestep +%would be too high. But on the other hand, the full codebase required +%to manage particles is quite complex and comprehensive: completely +%porting it to the GPU, such that the particles effectively also remain +%resident on the GPU, would be a large effort and would cause a major +%diversion of CPU and GPU codebases. Further complications would arise +%when running on multiple GPUs due to the fact that particles can move +%between parallel subdomains. Our solution, which minimises overhead, +%is to keep the particles resident on CPU, but offload only their +%interaction with the fluid to the GPU, as we describe below. + +%On the CPU, the code in the above listing can be restructured into +%three distinct stages as (introducing arrays to temporrily store +%indices and data): +%{\footnotesize +%\begin{verbatim} +%1) For each particle, for each link: +% Store site and velocity indices associated with link +% Calculate and store coefficient +%2) For each particle, for each link: +% Update velocity data using stored values +% Calculate and store particle momenta updates +%3) For each particle, for each link: +% Update particle momentum using stored values +%\end{verbatim} +%} +%Stage 2 is only one that that accesses fluid data: this can therefore +%be moved to the GPU, while stages 1 and 3 remain on the CPU. Stage 2 +%therefore becomes, for the GPU implementation +%{\footnotesize +%\begin{verbatim} +%Transfer coefficient and link index arrays to GPU +%Run CUDA kernel: +% Update fluid data using coefficient and link index arrays +% Calculate and store particle momenta updates from fluid data +%Transfer particle momenta updates array back to CPU +%\end{verbatim} +%} +%Since the total number of links involves is relatively small, these +%partial data transfers have minimal overhead. A complication arises +%due to the fact that the particles move through the lattice, albeit +%relatively slowly, and therefore the list of links that they intercept +%occasionally changes. When a +%particle moves off a lattice site, the fluid on that site must be +%reconstructed. Similarly, the fluid on the newly obstructed site must +%be removed. For the same reasons given above, we want to avoid moving +%all the code dealing with this complication to the GPU, whilst +%minimising overheads. Since, on any one timestep, the number of +%lattice sites affected is relatively low, we implement functionality +%to perform partial data transfers of the distribution. Only those +%sites affected are packed into a buffer on the GPU, transferred to the +%CPU and unpacked into the CPU version of the distribution. Then, the +%existing code can be used to update those affected sites, before the +%reverse partial data transfer is performed to update the GPU version +%of data. + +It is perhaps interesting at this point to make some more general +observations on the software engineering challenge presented when +extending an existing CPU code to the GPU. The already complex +task of maintaining the code in a portable fashion while also +maintaining performance is currently formidable. To help this process, +we have followed a number of basic principles. First, +in order to port to the GPU in an incremental fashion, +we have tried to maintain the modular structure of the CPU where +possible. For each data structure, such as the distribution, a separate +analogue is maintained in both the CPU and GPU memory spaces. However, +the GPU copy does not include the complete CPU structure: in +particular, non-intrinsic datatypes such as MPI datatypes are not +required on the GPU. Functions to marshal data between CPU and GPU +are provided for each data structure, abstracting the underlying +CUDA implementation. (This reasonably lightweight abstraction layer +could also allow an OpenCL implementation to be developed.) +This makes it easy to switch between the CPU and GPU for different +components in the code, which is useful in development +and testing. GPU functionality can be added incrementally while +retaining a code that runs correctly (albeit slowly due to data +transfer overheads). Once all relevant components are moved to +the GPU, it becomes possible to remove such data transfers and +keep the entire problem resident on the device. + +%To ensure that \textit{Ludwig} remains suitable for CPU-based systems, +%we augment rather than modify the original code. All new GPU-related +%functionality are implemented in an additive manner, mirroring the +%modular structure of the original code. The new GPU modules have +%interfaces similar to the original CPU functions, and the ability to +%switch between versions is achieved at compile-time via C +%pre-processing directives. All GPU compute kernels are implemented using +%CUDA. Wrapper routines are developed to specify the +%decomposition, invoke the CUDA kernels and perform the necessary data +%management operations: the latter are facilitated through newly +%developed modules for creation and destruction of device memory +%structures, the copying of data between host and device, and where +%necessary the packing and buffering of data. + + + + +\section{Summary} +\label{ch14:sec:summary} + +We have described the steps take to implement, on both single and +multiple GPUs, the \textit{Ludwig} code which was originally +designed for complex fluid problems on a conventional CPU +architecture. We have added the necessary functionality using +NVIDIA CUDA and discussed the important changes to the main +data structures. By retaining domain decomposition and message +passing via MPI, we have demonstrated it is possible to scale +complex fluid problems to large numbers of GPUs in parallel. +By following the key design criteria of maximising parallelism +and minimising host-device data transfers, we have confirmed +the mixed MPI-GPU approach is viable for scientific applications. +For the more intricate problem of moving solid particles, we find +it is possible to retain the more serial elements related to +particle link operations on the CPU, while offloading only the +parallel lattice-based operations involving the LB distribution +to the GPU. Again, this minimises host-device movement of data. + +From the software engineering viewpoint, some duplication of code +to allow efficient implementation on both host and device is +currently required. This issue might be addressed by approaches +such as automatic kernel generation, but may also be addressed +naturally in time as GPU and CPU hardware converge. Portable +abstractions and APIs, perhaps based on approaches such as OpenCL, +will also facilitate the development and maintenance of portable +codes which also exhibit portable performance (perhaps in conjunction +with automatic tuning approaches). + +So, while the challenges in designing portable and efficient scientific +applications remain very real, this work provides some hope the large +clusters of GPU machines can be used effectively for a wide range of +complex fluid problems. + + +%The \textit{Ludwig} LB application, which specializes +%in complex fluid problems, has undergone the developments required to +%use large-scale parallel GPU accelerated supercomputers effectively. +%The new functionality augments the original code, so that +%\textit{Ludwig} can run on either CPU-based or GPU-accelerated systems. +%Using the NVIDIA CUDA programming language, all the computational +%kernels were offloaded to the GPU. This included not only those +%responsible for the majority of the compute time, but any that +%accessed the relevant data structures. This allows data to be kept +%resident on the GPU and avoids expensive data transfers. We described +%steps taken to optimise the performance on the GPU architecture by +%reducing off-chip memory accesses and restructuring the data layout +%in memory to ensure the available memory bandwidth was exploited fully. + +%A new halo-exchange communication phase for the code was developed to +%allow efficient parallel scaling to many GPUs. The CPU version +%relies on MPI datatype functionality for CPU to CPU communication; we +%have explicitly written buffer packing/unpacking and transfer +%operations to for GPU to GPU data exchange, staged through the host +%CPUs. This includes the filtering of velocity components to only +%transfer those required in a specific direction, hence reducing the +%overall volume of data transferred. We used CUDA stream functionality +%to overlap separate stages within the communication phase, and reduce +%the overall communication time. The effect was measured to be more +%profound for smaller local system sizes, i.e. it is especially useful +%in aiding strong scaling. + +%We described work to enable colloidal particles in the simulation with +%minimal overhead. We implemented these in such a way that we avoid a +%major diversion of the CPU and GPU codebases, whilst minimising data +%transfers between the CPU and GPU at each timestep. We keep the +%majority of the (relatively inexpensive) particle related code on the +%CPU, while offloading only those parts responsible for the interaction +%with the fluid to the GPU (where the fluid data resides). + +%Our resulting package is able to exploit the largest of GPU +%accelerated architectures to address a wide range of complex problems. + + + +% An example of a floating figure using the graphicx package. +% Note that \label must occur AFTER (or within) \caption. +% For figures, \caption should occur after the \includegraphics. +% Note that IEEEtran v1.7 and later has special internal code that +% is designed to preserve the operation of \label within \caption +% even when the captionsoff option is in effect. However, because +% of issues like this, it may be the safest practice to put all your +% \label just after \caption rather than within \caption{}. +% +% Reminder: the "draftcls" or "draftclsnofoot", not "draft", class +% option should be used if it is desired that the figures are to be +% displayed while in draft mode. +% +%\begin{figure}[!t] +%\centering +%\includegraphics[width=2.5in]{myfigure} +% where an .eps filename suffix will be assumed under latex, +% and a .pdf suffix will be assumed for pdflatex; or what has been declared +% via \DeclareGraphicsExtensions. +%\caption{Simulation Results} +%\label{fig_sim} +%\end{figure} + +% Note that IEEE typically puts floats only at the top, even when this +% results in a large percentage of a column being occupied by floats. + + +% An example of a double column floating figure using two subfigures. +% (The subfig.sty package must be loaded for this to work.) +% The subfigure \label commands are set within each subfloat command, the +% \label for the overall figure must come after \caption. +% \hfil must be used as a separator to get equal spacing. +% The subfigure.sty package works much the same way, except \subfigure is +% used instead of \subfloat. +% +%\begin{figure*}[!t] +%\centerline{\subfloat[Case I]\includegraphics[width=2.5in]{subfigcase1}% +%\label{fig_first_case}} +%\hfil +%\subfloat[Case II]{\includegraphics[width=2.5in]{subfigcase2}% +%\label{fig_second_case}}} +%\caption{Simulation results} +%\label{fig_sim} +%\end{figure*} +% +% Note that often IEEE papers with subfigures do not employ subfigure +% captions (using the optional argument to \subfloat), but instead will +% reference/describe all of them (a), (b), etc., within the main caption. + + +% An example of a floating table. Note that, for IEEE style tables, the +% \caption command should come BEFORE the table. Table text will default to +% \footnotesize as IEEE normally uses this smaller font for tables. +% The \label must come after \caption as always. +% +%\begin{table}[!t] +%% increase table row spacing, adjust to taste +%\renewcommand{\arraystretch}{1.3} +% if using array.sty, it might be a good idea to tweak the value of +% \extrarowheight as needed to properly center the text within the cells +%\caption{An Example of a Table} +%\label{table_example} +%\centering +%% Some packages, such as MDW tools, offer better commands for making tables +%% than the plain LaTeX2e tabular which is used here. +%\begin{tabular}{|c||c|} +%\hline +%One & Two\\ +%\hline +%Three & Four\\ +%\hline +%\end{tabular} +%\end{table} + + +% Note that IEEE does not put floats in the very first column - or typically +% anywhere on the first page for that matter. Also, in-text middle ("here") +% positioning is not used. Most IEEE journals/conferences use top floats +% exclusively. Note that, LaTeX2e, unlike IEEE journals/conferences, places +% footnotes above bottom floats. This can be corrected via the \fnbelowfloat +% command of the stfloats package. + + +% conference papers do not normally have an appendix + + +% use section* for acknowledgement +\section*{Acknowledgments} +AG was supported by the CRESTA project, which has received +funding from the European Community's Seventh Framework Programme +(ICT-2011.9.13) under Grant Agreement no. 287703. KS was supported +by UK EPSRC under grant EV/J007404/1. + + +% trigger a \newpage just before the given reference +% number - used to balance the columns on the last page +% adjust value as needed - may need to be readjusted if +% the document is modified later +%\IEEEtriggeratref{8} +% The "triggered" command can be changed if desired: +%\IEEEtriggercmd{\enlargethispage{-5in}} + +% references section + +% can use a bibliography generated by BibTeX as a .bbl file +% BibTeX documentation can be easily obtained at: +% http://www.ctan.org/tex-archive/biblio/bibtex/contrib/doc/ +% The IEEEtran BibTeX style support page is at: +% http://www.michaelshell.org/tex/ieeetran/bibtex/ +%\bibliographystyle{IEEEtran} +% argument is your BibTeX string definitions and bibliography database(s) +%\bibliography{IEEEabrv,../bib/paper} +% +% manually copy in the resultant .bbl file +% set second argument of \begin to the number of references +% (used to reserve space for the reference number labels box) + + +\begin{thebibliography}{1} + +%\bibitem{IEEEhowto:kopka} +%H.~Kopka and P.~W. 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Phys.}, \textbf{195} 602--628 (2004). + +\bibitem{ch14:spm} +Y. Nakayama and R. Yammamoto, +Simulation method to resolve hydrodynamic interactions in colloidal +dispersions, +\textit{Phys. Rev. E}, \textbf{71} 036707 (2005). + + +\end{thebibliography} + + + + +% that's all folks +\end{document} + + + +% LocalWords: CRESTA EPSRC Succi Desplat Pagonabarraga diff --git a/BookGPU/Chapters/chapter14/figures/Ludwig_two_graphs.pdf b/BookGPU/Chapters/chapter14/figures/Ludwig_two_graphs.pdf new file mode 100644 index 0000000000000000000000000000000000000000..56254ed0fdcefeffc715a1a6f0f66989ecfd8b4e GIT binary patch literal 202453 zcmagGcRbd6{|D~WY0wZEr7}}8Gs~74vR6W~vI&=cp+SWdvX#AMgeZlRl2x`)Mv9Cg zBO>efyy~3qeeV15`2BwVxbJf~F4y&bzuvFcbG<+8n(~S~{JcUG?Db#oq)`YnA7^$p zx1*4d;5%dGWKFPP7QuJ4`4nv(2v)Ale2NZc1S@$f3uj9!DJcp!f~%F8BZb#JF5Tm8 zg1X1aPI277*PO{@Sr~r)wO-Bzrwa~g)|===1cZcp1a*#g37!xUD7oDB>iV8;Itt!p z<15BnDJEp7$qJhG1X(#*{{3?JC(>IW{o!B#FG}(EcZB})=5)O;S@CI_SzGa`Sy|ee z$vS&68y&}wPaGFw7T^~#p}-Gat(*wVf~2=k!KZEI=IrijVdaJors?Wzp<_ib;=@O0 z=2LdWtH|LO6ubyZIs|+?X3^ijb5_IL&dP$o%ujk@Z3=u|J~?M60$$aPnY2#7zV-K8 zBmJ4*>&DDSdT-3X-kcjVpQ^2;8?zAw>8)#1m@wlN-0`mXNw4Z6#R> 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