twepp2015/paper.tex

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\title{A high-throughput readout architecture based on PCI-Express Gen3 and DirectGMA technology}

\author{
  L.~Rota$^a$,
  M.~Vogelgesang$^a$,
  L.E.~Ardila Perez$^a$,
  M.~Balzer$^a$,
  M.~Caselle$^a$,
  S.~Chilingaryan$^a$,
  A.~Kopmann$^a$,
  T.~Dritschler$^a$,
  M.~Weber$^a$\\
  N.~Zilio$^a$,
  \llap{$^a$}Institute for Data Processing and Electronics,\\
    Karlsruhe Institute of Technology (KIT),\\
    Herrmann-von-Helmholtz-Platz 1, Karlsruhe, Germany \\
    E-mail: \email{lorenzo.rota@kit.edu}, \email{matthias.vogelgesang@kit.edu}
}

\abstract{%
  Modern physics experiments have reached multi-GB/s data rates.  Fast data
  links and high performance computing stages are required for continuous data
  acquisition and processing. Because of their intrinsic parallelism and
  computational power, GPUs emerged as an ideal solution to process this data in
  high performance computing applications. In this paper we present a
  high-throughput platform based on direct FPGA-GPU communication. 
  The architecture consists of a
  Direct Memory Access (DMA) engine compatible with the Xilinx PCI-Express core,
  a Linux driver for register access, and high-level software to manage direct
  memory transfers using AMD's DirectGMA technology. Preliminary measurements with a Gen3
  x8 link show a throughput of up to 6.4 GB/s and a latency of 40 \textmu s.
  Our implementation is suitable for real-time DAQ system applications ranging
  from photon science and medical imaging to High Energy Physics (HEP) trigger
  systems.
}
\keywords{FPGA; GPU; PCI-Express; OpenCL; DirectGMA}

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\section{Introduction}

GPU computing has become the main driving force for high performance computing
due to an unprecedented parallelism and a low cost-benefit factor. GPU
acceleration has found its way into numerous applications, ranging from
simulation to image processing. Recent years have also seen an increasing
interest in GPU-based systems for High Energy Physics (HEP) experiments
(\emph{e.g.} ATLAS~\cite{atlas_gpu}, ALICE~\cite{alice_gpu},
Mu3e~\cite{mu3e_gpu}, PANDA~\cite{panda_gpu}). In a typical HEP scenario, data
are acquired by back-end readout systems and then transmitted in short bursts or
in a continuous streaming mode to a computing stage.

With expected data rates of several GB/s, the data transmission link may
partially limit the overall system performance.  In particular, latency becomes
the most stringent requirement for time-deterministic applications, \emph{e.g.}
in Low/High-level trigger systems.  Furthermore, the amount of data produced in
current generation photon science facilities have become comparable to those
traditionally associated with HEP.

Due to its high bandwidth and modularity,
PCIe quickly became the commercial standard for connecting high-throughput
peripherals such as GPUs or solid state disks.Moreover, optical PCIe networks have been demonstrated a decade ago~\cite{optical_pcie}, opening the possibility of using PCIe as a communication bus over long distances.

Several solutions for direct FPGA/GPU communication based on PCIe are reported 
in literature, and all of them are based on NVIDIA's GPUdirect technology.
In the implementation of Bittner and Ruf ~\cite{bittner} the GPU acts as master
during an FPGA-to-GPU data transfer, reading data from the FPGA.  This solution
limits the reported bandwidth and latency to 514 MB/s and 40~\textmu s,
respectively.
%LR: FPGA^2 it's the name of their thing...
When the FPGA is used as a master, a higher throughput can be achieved.  An
example of this approach is the FPGA\textsuperscript{2}
framework by Thoma et~al.\cite{thoma}, which reaches 2454 MB/s using a 8x Gen2.0
data link.
Lonardo et~al.\ achieved low latencies with their NaNet design, an FPGA-based
PCIe network interface card~\cite{lonardo2015nanet}.  The Gbe link however
limits the latency performance of the system to a few tens of \textmu s. If only
the FPGA-to-GPU latency is considered, the measured values span between
1~\textmu s and 6~\textmu s, depending on the datagram size. Moreover, the
bandwidth saturates at 120 MB/s.
Nieto et~al.\ presented a system based on a PXIexpress data link that makes use
of four PCIe 1.0 links~\cite{nieto2015high}.
Their system (as limited by the interconnect) achieves an average throughput of
870 MB/s with 1 KB block transfers.

In order to achieve the best performance in terms of latency and bandwidth,
we developed a high-performance DMA engine based on Xilinx's PCIe Gen3 Core.

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. However, the framework is based on OpenCL, 
and therefore integration with NVIDIA's CUDA functions for GPUDirect technology
is not possible. 

We therefore integrated direct FPGA-to-GPU communication into our processing pipeline
using AMD's DirectGMA technology. In this paper we report the performance of our
DMA engine for FPGA-to-CPU communication and the first preliminary results with 
DirectGMA technology.

\section{Architecture}

As shown in \figref{fig:trad-vs-dgpu} (a), traditional FPGA-GPU systems route
data through system main memory by copying data from the FPGA into intermediate
buffers and then finally into the GPU's main memory.
Thus, the total throughput and latency of the system is limited by the main
memory bandwidth. NVIDIA's GPUDirect and AMD's DirectGMA technologies allow
direct communication between GPUs and auxiliary devices over the PCIe bus.
By combining this technology with DMA data transfers (see \figref{fig:trad-vs-dgpu} (b)),
the overall latency of the system is reduced and total throughput increased.
Moreover, the CPU and main system memory are relieved from processing because
they are not directly involved in the data transfer anymore.

\begin{figure}[t]
  \centering
  \includegraphics[width=1.0\textwidth]{figures/transf}
  \caption{%
    In a traditional DMA architecture (a), data are first written to the main
    system memory and then sent to the GPUs for final processing.  By using
    GPUDirect/DirectGMA technology (b), the DMA engine has direct access to
    GPU's internal memory.
  }
  \label{fig:trad-vs-dgpu}
\end{figure}

\subsection{DMA engine implementation on the FPGA}

We have developed a DMA architecture that minimizes resource utilization while
maintaining the flexibility of a Scatter-Gather memory
policy~\cite{rota2015dma}. The engine is compatible with the Xilinx PCIe Gen2/3
IP-Core~\cite{xilinxgen3} for Xilinx FPGA families 6 and 7. DMA transmissions to
main system memory and GPU memory are both supported. Two FIFOs, with a data
width of 256 bits and operating at 250 MHz, act as user-friendly interfaces with
the custom logic with an input bandwidth of 7.45 GB/s. The user logic and the DMA
engine are configured by the host through PIO registers.

The physical addresses of the host's memory buffers are stored into an internal
memory and are dynamically updated by the driver or user, allowing highly
efficient zero-copy data transfers. The maximum size associated with each
address is 2 GB. The resource utilization
on a Virtex 7 device is reported in \ref{table:utilization}.

% Please add the following required packages to your document preamble:
% \usepackage{booktabs}
\begin{table}[]
\centering
\caption{Resource utilization on}
\label{table:utilization}
\begin{tabular}{@{}llll@{}}
Resource & Utilization & Available & Utilization \% \\\hline
LUT      & 5331        & 433200    & 1.23           \\
LUTRAM   & 56          & 174200    & 0.03           \\
FF       & 5437        & 866400    & 0.63           \\
BRAM     & 20.50       & 1470      & 1.39           \\\hline
\end{tabular}
\end{table}

\subsection{OpenCL management on host side}
\label{sec:host}

On the host side, AMD's DirectGMA technology, an implementation of the
bus-addressable memory extension 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:opencl-setup} 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).
Due to hardware restrictions the largest possible GPU buffer sizes are about 95
MB but larger transfers can be achieved by using a double buffering mechanism.

Because the GPU provides a flat memory address space and our DMA engine allows
multiple destination addresses to be set in advance, we can determine all
addresses before the actual transfers thus keeping the CPU out of the transfer
loop for data sizes less than 95 MB.

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.

\begin{figure}
  \centering
  \includegraphics[width=0.75\textwidth]{figures/opencl-setup}
  \caption{The FPGA writes to GPU memory by mapping the physical address of a
  GPU buffer and initating DMA transfers. Signalling happens in reverse order by
  mapping the FPGA control registers into the address space of the GPU.}
  \label{fig:opencl-setup}
\end{figure}

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 written in C or high-level languages such as Python.

\section{Results}

We carried out performance measurements on a machine with an Intel Xeon E5-1630
at 3.7 GHz, Intel C612 chipset running openSUSE 13.1 with Linux 3.11.10. The
Xilinx VC709 evaluation board was plugged into one of the PCIe 3.0 x8 slots.
In case of FPGA-to-CPU data transfers, the software implementation is the one 
described in~\cite{rota2015dma}.

\begin{figure}
  \centering
  \begin{subfigure}[b]{.49\textwidth}
    \centering
    \includegraphics[width=\textwidth]{figures/throughput}
    \caption{%
      DMA data transfer throughput.
    }
    \label{fig:throughput}
  \end{subfigure}
  \begin{subfigure}[b]{.49\textwidth}
    \includegraphics[width=\textwidth]{figures/latency}
    \caption{%
      Latency distribution.
      % for a single 4 KB packet transferred
      % from FPGA-to-CPU and FPGA-to-GPU.
    }
    \label{fig:latency}
  \end{subfigure}
  \caption{%
    Measured results for data transfers from FPGA to main memory
    (CPU) and from FPGA to the global GPU memory (GPU).
  }
\end{figure}

The measured results for the pure data throughput is shown in
\figref{fig:throughput} for transfers from the FPGA to the system's main memory
as well as to the global memory as explained in \ref{sec:host}. As one can see,
in both cases the write performance is primarily limited by the PCIe bus. Higher
payloads make up for the constant overhead thus increasing the net bandwidth. Up
until 2 MB data transfer size, the throughput to the GPU is approaching slowly
100 MB/s. From there on, the throughput increases up to 6.4 GB/s when PCIe bus
saturation sets in at about 1 GB data size.
The CPU throughput saturates earlier at about 30 MB but the maximum throughput
is limited to about 6 GB/s losing about 6\% write performance.

We repeated the FPGA-to-GPU measurements on a low-end Supermicro X7SPA-HF-D525
system based on an Intel Atom CPU. The results showed no significant difference
compared to the previous setup. Depending on the application and computing
requirements, this result makes smaller acquisition system a cost-effective
alternative to larger workstations.

\begin{figure}
  \includegraphics[width=\textwidth]{figures/intra-copy}
  \caption{%
    Throughput in MB/s for an intra-GPU data transfer of smaller block sizes
    (4KB -- 24 MB) into a larger destination buffer (32 MB -- 128 MB). The lower
    performance for smaller block sizes is caused by the larger amount of
    transfers required to fill the destination buffer. The throughput has been
    estimated using the host side wall clock time. The raw GPU data transfer as
    measured per event profiling is about twice as fast.
  }
  \label{fig:intra-copy}
\end{figure}

In order to write more than the maximum possible transfer size of 95 MB, we
repeatedly wrote to the same sized buffer which is not possible in a real-world
application. As a solution, we motivated the use of multiple copies in Section
\ref{sec:host}. 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. \figref{fig:intra-copy} shows the measured throughput for
three sizes and an increasing block size. At a block size of about 384 KB, the
throughput surpasses the maximum possible PCIe bandwidth, thus making a double
buffering strategy a viable solution for very large data transfers.

For HEP experiments, low latencies are necessary to react in a reasonable time
frame. In order to measure the latency caused by the communication overhead we
conducted the following protocol: 1) the host issues continuous data transfers
of a 4 KB buffer that is initialized with a fixed value to the FPGA using the
\texttt{cl\-Enqueue\-Copy\-Buffer} call. 2) when the FPGA receives data in its
input FIFO it moves it directly to the output FIFO which feeds the outgoing DMA
engine thus pushing back the data to the GPU. 3) At some point, the host
enables generation of data different from initial value which also starts an
internal FPGA counter with 4 ns resolution. 4) When the generated data is
received again at the FPGA, the counter is stopped. 5) The host program reads
out the counter values and computes the round-trip latency. The distribution of
10000 measurements of the one-way latency is shown in \figref{fig:latency}. The
GPU latency has a mean value of 84.38 \textmu s and a standard variation of
6.34 \textmu s. This is 9.73 \% slower than the CPU latency of 76.89 \textmu s
that was measured using the same driver and measuring procedure. The
non-Gaussian distribution with two distinct peaks indicates a systemic influence
that we cannot control and is most likely caused by the non-deterministic
run-time behaviour of the operating system scheduler.

\section{Conclusion and outlook}

We developed a hardware and software solution that enables DMA
transfers between FPGA-based readout boards and GPU computing clusters.
The software solution that we proposed allows seamless multi-GPU
processing of the incoming data, due to the integration in our streamed computing
framework. This allows straightforward integration with different DAQ systems
and introduction of custom data processing algorithms.

The net throughput is primarily limited by the PCIe bus, reaching 6.4 GB/s
for a FPGA-to-GPU data transfer and 6.6 GB/s for a FPGA-to-CPU data transfer.
By writing directly into GPU memory instead of routing data through system
main memory, the overall latency can be reduced, thus allowing close massively
parallel computation on GPUs.
Optimization of the GPU DMA interfacing code is ongoing with the help of
technical support by AMD. With a better understanding of the hardware and 
software aspects of DirectGMA, we expect a significant improvement in the latency
performance.

In order to increase the total throughput, a custom FPGA evaluation board is
currently under development. The board mounts a Virtex-7 chip and features two
fully populated FMC connectors, a 119 Gb/s DDR memory interface and a PCIe Gen3
x16 connection. Two PCIe x8 cores, instantiated on the board, will be mapped as
a single x16 device by using an external PCIe switch. With two cores operating
in parallel, we foresee an increase in the data throughput by a factor of 2 (as
demonstrated in~\cite{rota2015dma}).

Support for NVIDIA's GPUDirect technology is also foreseen in the next months to
lift the limitation of one specific GPU vendor and compare the performance of hardware by
different vendors.
Further improvements are expected by generalizing the transfer mechanism and
include Infiniband support besides the existing PCIe connection.
%% Where do we get this values? Any reference?
%This allows
%speeds of up to 290 Gb/s and latencies as low as 0.5 \textmu s.

Our goal is to develop a unique hybrid solution, based on commercial standards,
that includes fast data transmission protocols and a high performance GPU
computing framework.


\acknowledgments

This work was partially supported by the German-Russian BMBF funding programme,
grant numbers 05K10CKB and 05K10VKE.


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