Initial commit

.gitignore

@@ -0,0 +1,10 @@
+paper-figure*
+*.aux
+*.auxlock
+*.bbl
+*.blg
+*.fdb_latexmk
+*.fls
+*.log
+*.pdf
+*.hist.txt

Makefile

@@ -0,0 +1,19 @@
+DATA = data/decode/ipecam2.decode.cpu.hist.txt \
+	   data/decode/ipecam2.decode.gpu.hist.txt
+
+.PHONY: all clean
+
+all: idea.pdf paper.pdf
+
+%.hist.txt: %.raw.txt
+	python data/decode/hist.py $< $@
+
+clean:
+	rm idea.pdf paper.bbl
+	latexmk -C
+
+idea.pdf: idea.md
+	pandoc -o $@ $<
+
+paper.pdf: paper.tex $(DATA)
+	latexmk -pdflatex='pdflatex -shell-escape' -pdf $<

data/decode/hist.py

@@ -0,0 +1,15 @@
+import sys
+import numpy as np
+
+data = np.loadtxt(sys.argv[1])
+bias = 7.8
+num_bins = 75
+
+data = data[data < bias]
+hist, bins = np.histogram(data, bins=num_bins)
+hist = hist.reshape((-1, 1))
+bins = bins.reshape((-1, 1))
+
+out = np.hstack((bins[:-1], hist))
+np.savetxt(sys.argv[2], out)
+

data/decode/ipecam2.decode.cpu.raw.txt

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data/transfer/an.py

@@ -0,0 +1,15 @@
+import glob
+import numpy as np
+import humanize
+
+
+for filename in sorted(glob.glob('*.txt')):
+    name, width, height, number, _ = filename.split('.')
+    data = np.loadtxt(filename)
+    width = int(width)
+    height = int(height)
+    number = int(number)
+    size = width * height * 4 * number
+    throughput = size / np.mean(data) / 1024. / 1024.
+    humanized = humanize.naturalsize(size / number, binary=True, gnu=True, format='%.0f')
+    print("{} ({}B, {})".format(name, humanized, throughput))

data/transfer/attic/callback.4096.1024.1000.txt

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data/transfer/attic/callback.4096.2048.1000.txt

@@ -0,0 +1,100 @@
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data/transfer/attic/multi.4096.1.1000.txt

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data/transfer/attic/multi.4096.1024.1000.txt

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data/transfer/attic/multi.4096.128.1000.txt

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data/transfer/attic/multi.4096.16.1000.txt

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data/transfer/attic/multi.4096.2.1000.txt

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data/transfer/attic/multi.4096.2048.1000.txt

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data/transfer/attic/multi.4096.32.1000.txt

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data/transfer/attic/multi.4096.4.1000.txt

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data/transfer/attic/multi.4096.512.1000.txt

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data/transfer/attic/multi.4096.64.1000.txt

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data/transfer/attic/multi.4096.8.1000.txt

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data/transfer/attic/single.4096.1.1000.txt

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data/transfer/attic/single.4096.1024.1000.txt

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data/transfer/attic/single.4096.128.1000.txt

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data/transfer/attic/single.4096.16.1000.txt

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data/transfer/attic/single.4096.2.1000.txt

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data/transfer/attic/single.4096.2048.1000.txt

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data/transfer/attic/single.4096.256.1000.txt

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data/transfer/attic/single.4096.32.1000.txt

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data/transfer/attic/single.4096.4.1000.txt

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data/transfer/attic/single.4096.512.1000.txt

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data/transfer/attic/single.4096.64.1000.txt

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data/transfer/attic/single.4096.8.1000.txt

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data/transfer/multi2.4096.1.1000.txt

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data/transfer/multi2.4096.1024.1000.txt

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data/transfer/multi2.4096.128.1000.txt

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data/transfer/multi2.4096.16.1000.txt

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data/transfer/multi2.4096.2.1000.txt

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data/transfer/multi2.4096.2048.1000.txt

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data/transfer/multi2.4096.256.1000.txt

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data/transfer/multi2.4096.32.1000.txt

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data/transfer/multi2.4096.4.1000.txt

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data/transfer/multi2.4096.512.1000.txt

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data/transfer/multi2.4096.64.1000.txt

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data/transfer/multi2.4096.8.1000.txt

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data/transfer/plot.tex

@@ -0,0 +1,74 @@
+\documentclass[a4paper]{article}
+
+\usepackage{pgfplots}
+
+\pgfplotsset{compat=1.8}
+
+\begin{document}
+  \begin{tikzpicture}
+    \begin{axis}[
+        width=\textwidth,
+        xtick=data,
+        x tick label style={
+          /pgf/number format/1000 sep=,
+          rotate=90,
+        },
+        y tick label style={
+          /pgf/number format/1000 sep=
+        },
+        ylabel={Bandwidth (MB/s)},
+        xlabel={Data size},
+        symbolic x coords={4KB, 16KB, 32KB, 64KB, 128KB, 256KB, 512KB, 1MB, 2MB, 4MB, 8MB, 16MB, 32MB},
+        ymajorgrids=true,
+        major grid style={
+          dotted,
+        },
+        axis x line*=bottom,
+        axis y line*=left,
+        tick align=outside,
+        legend style={
+          at={(0.2, 0.95)},
+          draw=none,
+          fill=none,
+          cells={anchor=west},
+          /tikz/every even column/.append style={
+            column sep=8pt,
+          },
+        },
+      ]
+
+      \addplot coordinates {
+        (16KB, 106.178754056)
+        (32KB, 211.084895305)
+        (64KB, 415.703896443)
+        (128KB, 810.339674944)
+        (256KB, 1547.57365213)
+        (512KB, 2776.37262474)
+        (1MB, 5137.62674525)
+        (2MB, 5915.08598317)
+        (4MB, 6233.33653831)
+        (8MB, 6276.50844112)
+        (16MB, 6305.9174769)
+        (32MB, 6307.81059127)
+      };
+
+      \addplot coordinates {
+        (16KB, 112.769066994)
+        (32KB, 223.614235747)
+        (64KB, 415.094840869)
+        (128KB, 758.692184621)
+        (256KB, 1301.14745592)
+        (512KB, 2000.44858544)
+        (1MB, 2726.52144668)
+        (2MB, 4446.83980882)
+        (4MB, 4908.10674445)
+        (8MB, 5155.21548317)
+        (16MB, 5858.33741922)
+        (32MB, 5945.28752544)
+      };
+
+      \legend{MT, ST}
+
+    \end{axis}
+  \end{tikzpicture}
+\end{document}

data/transfer/single2.4096.1.1000.txt

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data/transfer/single2.4096.1024.1000.txt

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data/transfer/single2.4096.128.1000.txt

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data/transfer/single2.4096.16.1000.txt

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data/transfer/single2.4096.2.1000.txt

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data/transfer/single2.4096.2048.1000.txt

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data/transfer/single2.4096.256.1000.txt

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data/transfer/single2.4096.32.1000.txt

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data/transfer/single2.4096.4.1000.txt

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data/transfer/single2.4096.512.1000.txt

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data/transfer/single2.4096.64.1000.txt

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data/transfer/single2.4096.8.1000.txt

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gotham.sty

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+%%
+%% This is file `gotham.sty',
+%% generated with the docstrip utility.
+%%
+%% The original source files were:
+%%
+%% gotham.dtx  (with options: `package')
+%% 
+%% Copyright (C) 2015 by Matthias Vogelgesang
+%% 
+%% This file may be distributed and/or modified under the
+%% 
+%% conditions of the LaTeX Project Public License, either
+%% version 1.3 of this license or (at your option) any later
+%% version.  The latest version of this license is in:
+%% 
+%%    http://www.latex-project.org/lppl.txt
+%% 
+%% and version 1.3 or later is part of all distributions of
+%% LaTeX version 2005/12/01 or later
+%% 
+
+\NeedsTeXFormat{LaTeX2e}
+\ProvidesPackage{gotham}[2015/11/30 gotham]
+\RequirePackage{pgfplots}
+
+
+\pgfplotsset{
+  gotham/legend/.style={
+    legend style={
+      draw=none,
+      fill=none,
+    }
+  },
+  gotham/plot/.style={
+    gotham/legend,
+    axis x line*=bottom,
+    tick align=outside,
+    x tick label style={
+      /pgf/number format/1000 sep=,
+    },
+    y tick label style={
+      /pgf/number format/1000 sep=,
+    },
+    ymajorgrids=true,
+    xmajorgrids=true,
+    major grid style={
+      dotted,
+    },
+  },
+  gotham/dot plot/.style={
+    gotham/plot,
+    axis y line*=left,
+    only marks,
+  },
+  gotham/line plot/.style={
+    gotham/plot,
+    axis y line*=left,
+  },
+  gotham/bar plot/.style={
+    gotham/plot,
+    axis y line*=none,
+    xmajorgrids=false,
+    ybar,
+    area legend,
+    legend image code/.code={
+      \draw[#1] (0cm,-0.1cm) rectangle (0.15cm,0.1cm);
+    },
+  },
+  gotham/horizontal bar plot/.style={
+    gotham/bar plot,
+    xbar stacked,
+  },
+  gotham/scatter plot/.style={
+    gotham/plot,
+    scatter,
+    axis y line*=left,
+    only marks,
+    mark options={
+      draw opacity=0.9,
+      fill opacity=0.25,
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+  gotham/histogram/.style={
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+
+\endinput
+%%
+%% End of file `gotham.sty'.

paper.tex

@@ -0,0 +1,437 @@
+\documentclass[journal]{IEEEtran}
+
+\usepackage{pgfplots}
+\usepackage{xspace}
+\usepackage{todonotes}
+\usepackage{minted}
+
+\usepackage{gotham}
+
+\usetikzlibrary{arrows}
+\usetikzlibrary{calc}
+\usepgfplotslibrary{statistics}
+
+%{{{ Meta data
+\title{General Gurpose FPGA-GPU Platform for High-Throughput DAQ and Processing}
+
+\author{
+Matthias Vogelgesang
+}
+%}}}
+
+\newcommand{\figref}[1]{Figure~\ref{#1}}
+
+
+\begin{document}
+
+\maketitle
+
+%{{{ Abstract
+\begin{abstract}
+  % Motivation
+
+  % Problem
+  Current generation GPUs are capable of processing TFLOP/s which in turn makes
+  application with large bandwidth requirements and simple algorithms
+  I/O-bound. Applications that receive data from external data sources are hit
+  twice because data first has to be transferred into system main memory before
+  being moved to the GPU in a second transfer.
+  % Solution
+  To remedy this problem, we designed and implemented a system architecture
+  comprising a custom FPGA board with a flexible DMA transfer policy and a
+  heterogeneous compute framework receiving data using AMD's DirectGMA
+  OpenCL extension.
+  % Conclusion
+  With our proposed system architecture we are able to sustain the bandwidth
+  requirements of various applications such as real-time tomographic image
+  reconstruction and signal analysis with a peak FPGA-GPU throughput of XXX GB/s.
+\end{abstract}
+%}}}
+
+\begin{IEEEkeywords}
+  GPGPU
+\end{IEEEkeywords}
+
+
+\section{Introduction}
+
+GPU computing has become a cornerstone for manifold applications that require
+large computional demands and exhibit an algorithmic pattern with a high degree
+of parallelization. This includes signal reconstruction~\cite{ref},
+recognition~\cite{ref} and analysis~\cite{emilyanov2012gpu} as well as
+simulation~\cite{bussmann2013radiative} and deep
+learning~\cite{krizhevsky2012imagenet}. With low costs of purchases and a
+relatively straightforward SIMD programming model, GPUs have
+become mainstream tools in industry and academia to solve the computational
+problems associated with these fields.
+
+Although GPUs harness a memory bandwidth that is far beyond a CPU's access to
+system memory, the data transfer between host and GPU can quickly become the
+main bottleneck for streaming systems and impede peak computation performance by
+not delivering data fast enough. This becomes even worse for systems where data
+does not originate from system memory but an external device. Typical examples
+delivering high data rates include front-end Field Programmable Gate Arrays
+(FPGA) for the digitization of analog signals. In this case, the data crosses
+the PCI express (PCIe) bus twice to reach the GPU: once from FPGA to system
+memory, second from system memory to GPU device memory. Considering
+feedback-driven experiments this data path causes high latencies preventing GPUs
+for certain application. On the other hand, copying data twice effectively
+halves the total system bandwidth thus ...
+
+In the remaining paper, we will introduce a hardware-software platform that
+remedies these issues by decoupling data transfers between FPGA and GPU from the
+host machine which is solely used to set up appropriate memory buffers and
+orchestrates data transfer and kernel execution initiations. The system is
+composed of a custom FPGA with a high performance DMA engine presented in
+\ref{sec:fpga} and a high-level software layer that manages the OpenCL runtime
+and gives users different means of accessessing the system as shown in
+\ref{sec:opencl}. In \ref{sec:use cases}, we outline two example use cases for
+our system, both requiring a high data throughput and present benchmark results.
+We will discuss and conclude this paper in \ref{sec:discussion} and
+\ref{sec:conclusion} respectively.
+
+
+\section{Streamed data architecture}
+\label{sec:architecture}
+
+Besides providing high performance at low power as a co-processor for heavily
+parallel and pipelined algorithms, FPGAs are also suited for custom data
+acquisition (DAQ) applications because of to lower costs and faster development
+time compared to application-specific integrated circuits (ASICs). Data is
+streamed from the FPGA to the host machine using a variety of interconnects,
+however PCIe is the only viable option for a standardized and high throughput
+interconnect~\cite{pci2009specification}.
+
+GPUs typically provide better performance for problems that can be solved using
+Single-Instruction-Multiple-Data (SIMD) operations, in highly parallel but
+non-pipelined fashion. Compared to FPGAs they also exhibit a simpler programming
+model, i.e. algorithm development is much faster. Nevertheless, all data that is
+processed on a GPU must be transferred through the PCIe bus.
+% Redo combination
+Combining those to platforms allow for fast digitization and quick data
+assessment. In the following, we will present a hardware/software stack that
+encompasses an FPGA DMA engine as well as DirectGMA-based data transfers that
+allows us to stream data at peak PCIe bandwidth.
+
+\begin{figure*}
+  \centering
+
+  \begin{tikzpicture}[
+    box/.style={
+      draw,
+      minimum height=6mm,
+      minimum width=16mm,
+      text height=1.5ex,
+      text depth=.25ex,
+    },
+    connection/.style={
+      ->,
+      >=stealth',
+    },
+  ]
+    \node[box] (adc) {ADC};
+    \node[box, right=3mm of adc] (logic) {Logic};
+    \node[box, right=3mm of logic] (fifo) {FIFOs};
+    \node[box, below=3mm of fifo] (regs) {Registers};
+
+    \node[box, right=7cm of fifo] (gpu) {GPU};
+    \node[box, right=2.7cm of regs] (cpu) {Host CPU};
+
+    \draw[connection] (adc) -- (logic);
+    \draw[connection] (logic) -- (fifo);
+    \draw[connection] (cpu) -- node[below] {Set address} (regs);
+    \draw[connection] (cpu) -| node[below] {Prepare buffers} (gpu);
+    \draw[connection, <->] (fifo) -- node[above] {DMA transfers} (gpu);
+    \draw[connection, <->] (logic) |- (regs);
+  \end{tikzpicture}
+
+  \label{fig:architecture}
+  \caption{%
+    Streaming architecture consisting of a PCIe-based FPGA design with
+    custom application logic and subsequent data processing on the GPU.
+  }
+\end{figure*}
+
+
+\subsection{FPGA DMA engine}
+\label{sec:fpga}
+
+We have developed a DMA engine that provides a flexible scatter-gather memory
+policy and minimizes resource utilization to around 3\% of the resources of a
+Virtex-6 device~\cite{rota2015dma}. The engine is compatible with the Xilinx
+PCIe 2.0/3.0 IP-Core for Xilinx FPGA families 6 and 7. Both DMA data transfers
+between main system memory as well as GPU memory are supported. Two FIFOs, each
+256 bits wide operate at 250 MHz and exchange data with the custom application
+logic shown on the left of \figref{fig:architecture}. With this configuration,
+the engine is capable of an input bandwidth of 7.45 GB/s.  The user logic and
+the DMA engine are configured by the host system through 32 bit wide PIO
+registers.
+
+Regardless of the actual source of data, DMA transfers are started by writing
+one or more physical addresses of the destination memory to a specific register.
+The addresses are stored in an internal memory with a size of 4 KB, i.e.
+spanning 1024 32-bit or 512 64-bit addresses. Each address may cover a range of
+up to 2 GB of linear address space. However, due to the virtual addressing of
+current CPU architectures, transfers to the main memory are limited to pages of
+4 KB or 4 MB size. Unlike CPU memory, GPU buffers are flat-addressed and can be
+filled at once. Updating the addresses in a dynamic fashion by either the driver
+or the host application without fixed addresss, allows for efficient zero-copy
+data transfers.
+
+
+\subsection{OpenCL host management}
+\label{sec:opencl}
+
+% Reword copy pasta
+On the host side, AMD's DirectGMA technology, an implementation of the
+bus-addressable memory extension~\cite{amdbusaddressablememory} for OpenCL 1.1
+and later, is used to write from the FPGA to GPU memory and from the GPU to the
+FPGA's control registers. \figref{fig:architecture} illustrates the main mode
+of operation: to write into the GPU, the physical bus addresses of the GPU
+buffers are determined with a call to
+\texttt{clEnqueue\-Make\-Buffers\-Resident\-AMD} and set by the host CPU in a
+control register of the FPGA (1). The FPGA then writes data blocks autonomously
+in DMA fashion (2). To signal events to the FPGA (4), the control registers can
+be mapped into the GPU's address space passing a special AMD-specific flag and
+passing the physical BAR address of the FPGA configuration memory to the
+\texttt{cl\-Create\-Buffer} function. From the GPU, this memory is seen
+transparently as regular GPU memory and can be written accordingly (3). In our
+setup, trigger registers are used to notify the FPGA on successful or failed
+evaluation of the data. Using the \texttt{cl\-Enqueue\-Copy\-Buffer} function
+call it is possible to write entire memory regions in DMA fashion to the FPGA.
+In this case, the GPU acts as bus master and pushes data to the FPGA.
+
+Due to hardware limitations, GPU buffers that are made resident are restricted
+to a hardware-dependent size. For example on AMD's FirePro W9100, the total
+amount of GPU memory that can be allocated that way is about 95 MB. However,
+larger transfers can be achieved by using a double buffering mechanism: data are
+copied from the buffer exposed to the FPGA into a different location in GPU
+memory. To verify that we can keep up with the incoming data throughput using
+this strategy, we measured the data throughput within a GPU by copying data from
+a smaller sized buffer representing the DMA buffer to a larger destination
+buffer. At a block size of about 384 KB the throughput surpasses the maximum
+possible PCIe bandwidth. Block transfers larger than 5 MB saturate the bandwidth
+at 40 GB/s. Double buffering is therefore a viable solution for very large data
+transfers, where throughput performance is favoured over latency. For data sizes
+less than 95 MB, we can determine all addresses before the actual transfers thus
+keeping the CPU out of the transfer loop.
+
+
+\subsection{Heterogenous data processing}
+
+To process the data, we encapsulated the DMA setup and memory mapping in a
+plugin for our scalable GPU processing framework~\cite{vogelgesang2012ufo}.
+This framework allows for an easy construction of streamed data processing on
+heterogeneous multi-GPU systems. For example, to read data from the FPGA, decode
+from its specific data format and run a Fourier transform on the GPU as well as
+writing back the results to disk, one can run the following on the command line:
+
+\begin{verbatim}
+ufo-launch direct-gma ! decode ! fft ! \
+           write filename=out.raw
+\end{verbatim}
+
+The framework takes care of scheduling the tasks and distributing the data items
+to one or more GPUs. High throughput is achieved by the combination of fine- and
+coarse-grained data parallelism, \emph{i.e.} processing a single data item on a
+GPU using thousands of threads and by splitting the data stream and feeding
+individual data items to separate GPUs. None of this requires any user
+intervention and is solely determined by the framework in an automatized
+fashion. A complementary application programming interface allows users to
+develop custom applications in C or high-level languages such as Python. For
+example, with a high-level wrapper module users can express the use case
+presented in \ref{sec:beam monitoring} like this
+
+\begin{minted}{python}
+from ufo import DirectGma, Write
+
+dgma = DirectGma(device='/dev/fpga0')
+write = Write(filename='out.raw')
+
+# Execute and wait to finish
+write(dgma()).run().join()
+\end{minted}
+
+
+\section{Use cases}
+\label{sec:use cases}
+
+% \subsection{Hardware setups}
+
+Based on the architecture covered in Section \ref{sec:architecture} we present
+two example use cases motivating a setup involving FPGA-based DAQ and GPU-based
+processing. Section \ref{sec:image acquisition} outlines a camera system that
+combines frame acquisition with real-time reconstruction of volume data, while
+Section \ref{sec:beam monitoring} uses the GPU to determine bunch parameters in
+synchrotron beam diagnostics. In both examples, we will describe the setup in
+place and subsequently quantify improvements.
+
+We tested the proposed use cases on two different systems representing
+high-powered workstations and low-power, embedded systems. In both cases, we
+used a frontend FPGA board that is based on a Xilinx VC709 (Virtex-7 FPGA and
+PCIe x8 3.0) and an AMD FirePro W9100. System A is based on a Xeon E5-1630 CPU
+with an Intel C612 chipset and 128 GB of main memory. Due to the mainboard
+layout, both the FPGA and the PCIe devices are connected through different root
+complexes (RC). System B is a low-end Supermicro X7SPA-HF-D525 board with an
+Intel Atom D525 Dual Core CPU that is connected to an external Netstor NA255A
+PCIe enclosure. Unlike System A, the FPGA board and GPU share a common RC
+located inside the Netstor box.
+
+
+\subsection{Image acquisition and reconstruction}
+\label{sec:image acquisition}
+
+Custom FPGA logic allows for quick integration of image sensors for application
+requirements ranging from high throughput to high resolution as well as initial
+pre-processing of the image data. For example, we integrated a CMOS image
+sensors such as CMOSIS CMV2000, CMV4000 and CMV20000 on top of the FPGA hardware
+platform presented in Section~\ref{sec:fpga}~\cite{caselle2013ultrafast}. These
+custom cameras are employed in synchrotron X-ray imaging experiments such as
+absorption-based as well as grating-based phase contrast
+tomography~\cite{lytaev2014characterization}.  Besides merely transmitting the
+final frames through PCIe to the host, the FPGA logic is concerned with sensor
+configuration, readout and digitization of the analog photon counts.
+User-oriented sensor configuration (e.g. exposure time, readout window, etc.)
+are mapped to 32-bit registers that are read and written from host.
+% Acquiring and processing 2D image data on the fly is a necessary task for many
+% control applications.
+
+Before the data can be analyzed, the hardware-specific data format needs to be
+decoded. In our case, we have a 10 to 12 bit packed format along with meta
+information about the entire frame and per scan line. As shown in
+\figref{fig:decoding}, an OpenCL kernel that shifts the pixel information and
+discards the meta data is able to decode the frame format efficiently and with a
+throughput X times larger than running SSE-optimized code on a Xeon XXX CPU.
+Thus decoding a frame before any computation is not impeding bottlenecks and in
+fact allows us to process at a lower latency.
+
+\begin{figure}
+  \centering
+
+  \begin{tikzpicture}
+    \begin{axis}[
+        gotham/histogram,
+        width=0.49\textwidth,
+        height=5cm,
+        xlabel={Decoding time in ms},
+        ylabel={Occurence},
+        bar width=1.2pt,
+    ]
+      \addplot file {data/decode/ipecam2.decode.gpu.hist.txt};
+      \addplot file {data/decode/ipecam2.decode.cpu.hist.txt};
+    \end{axis}
+  \end{tikzpicture}
+  \caption{%
+    Decoding a range of frames on an AMD FirePro W9100 and a Xeon
+    XXX.
+  }
+  \label{fig:decoding}
+\end{figure}
+
+The decoded data is then passed to the next stages that filter the rows in
+frequency space and backproject the data into the final volume in real space.
+
+
+\subsection{Beam monitoring}
+\label{sec:beam monitoring}
+
+% Extend motivation
+The characterization of an electron beam in synchrotrons is [...] ... Have a
+system in place that consists of a 1D spectrum analyzer that outputs 256 values
+per acquisition at a frequency of XXX Hz.
+
+The main pipeline consists of background subtraction of previously averaged
+background and modulated signals. and ...
+
+
+\subsection{Results}
+
+\begin{figure}
+  \centering
+  \begin{tikzpicture}
+    \begin{axis}[
+        height=6cm,
+        width=\columnwidth,
+        gotham/line plot,
+        bar width=5pt,
+        xtick=data,
+        x tick label style={
+          rotate=55,
+        },
+        ylabel={Throughput (MB/s)},
+        symbolic x coords={
+          4KB, 16KB, 32KB, 64KB, 128KB, 256KB, 512KB,
+          1MB, 2MB, 4MB, 8MB, 16MB, 32MB
+        },
+        legend style={
+          at={(0.25, 0.95)},
+          cells={
+            anchor=west
+          },
+        },
+      ]
+
+      \addplot coordinates {
+        (16KB, 106.178754056)
+        (32KB, 211.084895305)
+        (64KB, 415.703896443)
+        (128KB, 810.339674944)
+        (256KB, 1547.57365213)
+        (512KB, 2776.37262474)
+        (1MB, 5137.62674525)
+        (2MB, 5915.08598317)
+        (4MB, 6233.33653831)
+        (8MB, 6276.50844112)
+        (16MB, 6305.9174769)
+        (32MB, 6307.81059127)
+      };
+
+      \addplot coordinates {
+        (16KB, 112.769066994)
+        (32KB, 223.614235747)
+        (64KB, 415.094840869)
+        (128KB, 758.692184621)
+        (256KB, 1301.14745592)
+        (512KB, 2000.44858544)
+        (1MB, 2726.52144668)
+        (2MB, 4446.83980882)
+        (4MB, 4908.10674445)
+        (8MB, 5155.21548317)
+        (16MB, 5858.33741922)
+        (32MB, 5945.28752544)
+      };
+
+      \legend{MT, ST}
+    \end{axis}
+  \end{tikzpicture}
+  \caption{Data throughput from FPGA to GPU on a Setup xyz.}
+  \label{fig:throughput}
+\end{figure}
+
+\section{Discussion}
+\label{sec:discussion}
+
+
+\section{Related work}
+
+
+\section{Conclusions}
+\label{sec:conclusion}
+
+In this paper, we presented a complete data acquisition and processing pipeline
+that focuses on low latencies and high throughput. It is based on an FPGA design
+for data readout and DMA transmission to host or GPU memory. On the GPU side we
+use AMDs DirectGMA OpenCL extension to provide the necessary physical memory
+addresses and [we'll see] signaling of data finishes. With this system, we are
+able to achieve data rates that match the PCIe specifications of up to 6.x GB/s
+for a PCIe 3.0 x8 connection.
+
+
+\section*{Acknowledgments}
+
+
+\bibliographystyle{abbrv}
+\bibliography{refs}
+
+
+\end{document}

refs.bib

@@ -0,0 +1,114 @@
+@article{rota2015dma,
+  author      = {Rota, L. and Caselle, M. and Chilingaryan, S. and Kopmann, A. and Weber, M.},
+  journal     = {Nuclear Science, IEEE Transactions on},
+  title       = {A {PCIe} {DMA} Architecture for Multi-Gigabyte Per Second Data Transmission},
+  year        = {2015},
+  month       = {6},
+  volume      = {62},
+  number      = {3},
+  pages       = {972--976},
+  doi         = {10.1109/TNS.2015.2426877},
+  ISSN        = {0018-9499},
+}
+
+@inproceedings{lytaev2014characterization,
+  author      = {Lytaev, Pavel and Hipp, Alexander and Lottermoser, Lars and Herzen,
+    Julia and Greving, Imke and Khokhriakov, Igor and Meyer-Loges, Stephan and
+    Plewka, Jörn and Burmester, Jörg and Caselle, Michele and Vogelgesang,
+    Matthias and Chilingaryan, Suren and Kopmann, Andreas and Balzer, Matthias
+    and Schreyer, Andreas and Beckmann, Felix},
+  title       = {Characterization of the {CCD} and {CMOS} cameras for grating-based phase-contrast tomography},
+  journal     = {Proc. SPIE, Developments in X-Ray Tomography},
+  volume      = {9212},
+  pages       = {921218-921218-10},
+  year        = {2014},
+  doi         = {10.1117/12.2061389},
+  URL         = { http://dx.doi.org/10.1117/12.2061389},
+}
+
+@article{caselle2013ultrafast,
+  author      = {Caselle, M. and Chilingaryan, S. and Herth, A. and Kopmann, A. and Stevanovic, U. and Vogelgesang, M. and Balzer, M. and Weber, M.},
+  doi         = {10.1109/TNS.2013.2252528},
+  issn        = {0018-9499},
+  journal     = {Nuclear Science, IEEE Transactions on},
+  number      = {5},
+  pages       = {3669--3677},
+  title       = {Ultrafast Streaming Camera Platform for Scientific Applications},
+  volume      = {60},
+  year        = {2013}
+}
+
+@inproceedings{bussmann2013radiative,
+  author      = {Bussmann, M. and Burau, H. and Cowan, T. E. and Debus, A. and
+    Huebl, A. and Juckeland, G. and Kluge, T. and Nagel, W. E. and Pausch, R.
+    and Schmitt, F. and Schramm, U. and Schuchart, J. and Widera, R.},
+  title       = {Radiative Signatures of the Relativistic Kelvin-Helmholtz Instability},
+  booktitle   = {Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis},
+  series      = {SC '13},
+  year        = {2013},
+  isbn        = {978-1-4503-2378-9},
+  location    = {Denver, Colorado},
+  pages       = {5:1--5:12},
+  articleno   = {5},
+  numpages    = {12},
+  url         = {http://doi.acm.org/10.1145/2503210.2504564},
+  doi         = {10.1145/2503210.2504564},
+  publisher   = {ACM},
+  address     = {New York, NY, USA},
+}
+
+@article{emilyanov2012gpu,
+  author      = {Emeliyanov, D. and Howard, J.},
+  title       = {{GPU}-Based Tracking Algorithms for the ATLAS High-Level Trigger},
+  journal     = {Journal of Physics: Conference Series},
+  volume      = {396},
+  number      = {1},
+  pages       = {012018},
+  url         = {http://stacks.iop.org/1742-6596/396/i=1/a=012018},
+  year        = {2012},
+}
+
+@inproceedings{vogelgesang2012ufo,
+  author      = {Vogelgesang, Matthias and Chilingaryan, Suren and dos Santos Rolo, Tomy and Kopmann, Andreas},
+  booktitle   = {Proceedings of The 14th IEEE Conference on High Performance Computing and Communication \& The 9th IEEE International Conference on Embedded Software and Systems (HPCC-ICESS)},
+  isbn        = {978-1-4673-2164-8},
+  location    = {Liverpool, UK},
+  month       = {6},
+  pages       = {824--829},
+  publisher   = {IEEE Computer Society},
+  series      = {HPCC '12},
+  title       = {{UFO}: A Scalable {GPU}-based Image Processing Framework for On-line Monitoring},
+  year        = {2012}
+}
+
+@incollection{krizhevsky2012imagenet,
+  title       = {ImageNet Classification with Deep Convolutional Neural Networks},
+  author      = {Alex Krizhevsky and Sutskever, Ilya and Geoffrey E. Hinton},
+  booktitle   = {Advances in Neural Information Processing Systems 25},
+  editor      = {F. Pereira and C.J.C. Burges and L. Bottou and K.Q. Weinberger},
+  pages       = {1097--1105},
+  year        = {2012},
+  publisher   = {Curran Associates, Inc.},
+  url         = {http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf}
+}
+
+@techreport{amdbusaddressablememory,
+  title       = {cl\_amd\_bus\_addressable\_memory},
+  author      = {Boudier, Pierre and Sellers, Graham and Kessler, Benedikt and
+    Rosenberg, Ofer},
+  type        = {OpenCL platform extension},
+  number      = {25},
+  year        = {2011},
+  month       = {11},
+  publisher   = {Khronos Group},
+  url         = {https://www.khronos.org/registry/cl/extensions/amd/cl_amd_bus_addressable_memory.txt},
+}
+
+@techreport{pci2009specification,
+  title       = {{PCI} {E}xpress Base Specification 2.1},
+  author      = {PCI-SIG},
+  type        = {Specification},
+  year        = {2009},
+  publisher   = {PCI-SIG},
+  url         = {http://www.pcisig.com/specifications/pciexpress/base2/}
+}