Pre-submission

Makefile

@@ -2,8 +2,10 @@ FIGURES=figures/intra-copy.pdf \
 		figures/opencl-setup.pdf \
 		figures/transf.pdf \
 		figures/throughput.pdf \
-		figures/latency.pdf \
-		figures/latency-hist.pdf \
+		figures/latency-cpu.pdf \
+		figures/latency-gpu.pdf \
+		figures/latency-hist-cpu.pdf \
+		figures/latency-hist-gpu.pdf \
 		figures/fpga-arch.pdf
 
 .PHONY: clean figures
@@ -17,11 +19,17 @@ paper.pdf: paper.tex literature.bib $(FIGURES)
 figures/intra-copy.pdf:
 	@cd data && python intra-copy.py ../figures/intra-copy.pdf
 
-figures/latency.pdf: data/latency/ipedirectgma.cpu.txt data/latency/ipedirectgma.gpu.txt data/latency/plot.py
-	@cd data/latency && python plot.py && cp latency.pdf ../../figures
+figures/latency-cpu.pdf: data/latency/ipedirectgma.cpu.txt data/latency/plot-cpu.py
+	@cd data/latency && python plot-cpu.py && cp latency-cpu.pdf ../../figures
 
-figures/latency-hist.pdf: data/latency/ipedirectgma.1024.cpu.txt data/latency/ipedirectgma.1024.gpu.txt data/latency/latency-hist.py
-	@cd data/latency && python latency-hist.py && cp latency-hist.pdf ../../figures
+figures/latency-gpu.pdf: data/latency/ipedirectgma.cpu.txt data/latency/plot-gpu.py
+	@cd data/latency && python plot-gpu.py && cp latency-gpu.pdf ../../figures
+
+figures/latency-hist-cpu.pdf: data/latency/ipedirectgma.1024.cpu.txt data/latency/ipedirectgma.1024.gpu.txt data/latency/latency-hist-cpu.py
+	@cd data/latency && python latency-hist-cpu.py && cp latency-hist-cpu.pdf ../../figures
+
+figures/latency-hist-gpu.pdf: data/latency/ipedirectgma.1024.cpu.txt data/latency/ipedirectgma.1024.gpu.txt data/latency/latency-hist-gpu.py
+	@cd data/latency && python latency-hist-gpu.py && cp latency-hist-gpu.pdf ../../figures
 
 figures/throughput.pdf: data/throughput.cpu data/throughput.gpu data/throughput.py
 	@cd data && python throughput.py && cp throughput.pdf ../figures

data/latency/ipecamera2.1024.cpu.txt

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data/latency/latency-hist-cpu.py

@@ -0,0 +1,41 @@
+import sys
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid.axislines import Subplot
+
+
+c = np.loadtxt('ipecamera2.1024.cpu.txt')
+g = np.loadtxt('ipedirectgma.1024.cpu.txt')
+cpu_data = c[:,1]
+gpu_data = g[:,1]
+
+cpu_data = cpu_data[cpu_data < 5]
+gpu_data = gpu_data[gpu_data < 5]
+
+
+
+#plt.rc('font', **dict(family='serif'))
+
+fig = plt.figure(1, (4,3.2))
+ax = Subplot(fig, 111)
+fig.add_subplot(ax)
+
+cpu_weights = np.ones_like(cpu_data)/float(len(cpu_data))
+gpu_weights = np.ones_like(gpu_data)/float(len(gpu_data))
+
+# divide by 2 for one-way latency
+# plt.ylim(0.1, 10000)
+# plt.hist(gpu_data, bins=200, label='GPU', log=True)
+# plt.hist(cpu_data, bins=200, label='CPU', log=True)
+ax.hist(gpu_data, weights=gpu_weights, bins=50, color='#028e2c', label='Setup 1', linewidth=0)
+ax.hist(cpu_data, weights=cpu_weights, bins=50, color='#77DD77', label='Setup 2', linewidth=0)
+plt.xticks([2.0, 3.0, 4.0, 5.0])
+plt.yticks([0,0.25,0.5])
+ax.axis["right"].set_visible(False)
+ax.axis["top"].set_visible(False)
+# plt.semilogy()
+
+plt.xlabel(u'Latency in \u00b5s')
+plt.ylabel('Frequency')
+plt.legend(loc='upper right',frameon=False)
+plt.savefig('latency-hist-cpu.pdf', dpi=300, bbox_inches='tight')

data/latency/latency-hist-gpu.py

@@ -0,0 +1,41 @@
+import sys
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid.axislines import Subplot
+
+
+c = np.loadtxt('ipecamera2.1024.gpu.txt')
+g = np.loadtxt('ipedirectgma.1024.gpu.txt')
+cpu_data = c[:,1]
+gpu_data = g[:,1]
+
+cpu_data = cpu_data[cpu_data < 5]
+gpu_data = gpu_data[gpu_data < 5]
+
+
+
+#plt.rc('font', **dict(family='serif'))
+
+fig = plt.figure(1, (4,3.2))
+ax = Subplot(fig, 111)
+fig.add_subplot(ax)
+
+cpu_weights = np.ones_like(cpu_data)/float(len(cpu_data))
+gpu_weights = np.ones_like(gpu_data)/float(len(gpu_data))
+
+# divide by 2 for one-way latency
+# plt.ylim(0.1, 10000)
+# plt.hist(gpu_data, bins=200, label='GPU', log=True)
+# plt.hist(cpu_data, bins=200, label='CPU', log=True)
+ax.hist(gpu_data, weights=gpu_weights, bins=50, color='#3b5b92', label='Setup 1', linewidth=0)
+ax.hist(cpu_data, weights=cpu_weights, bins=50, color='#aec6cf', label='Setup 2', linewidth=0)
+plt.xticks([2.0, 3.0, 4.0, 5.0])
+plt.yticks([0,0.25,0.5])
+ax.axis["right"].set_visible(False)
+ax.axis["top"].set_visible(False)
+# plt.semilogy()
+
+plt.xlabel(u'Latency in \u00b5s')
+plt.ylabel('Frequency')
+plt.legend(loc='upper right',frameon=False)
+plt.savefig('latency-hist-gpu.pdf', dpi=300, bbox_inches='tight')

data/latency/plot-cpu.py

@@ -0,0 +1,70 @@
+# -*- coding: utf-8 -*-
+
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid.axislines import Subplot
+
+
+def get_mean_std(data, size):
+    s = data[np.where(data[:,0] == size)][:,1]
+
+    # remove 2x std outliers
+    std = np.std(s)
+    outliers = s[np.abs(s - np.mean(s)) > 4 * np.std(s)]
+    s = s[np.abs(s - np.mean(s)) < 4 * np.std(s)]
+    return np.mean(s), np.std(s)
+
+
+def read_data(fname):
+    data = np.loadtxt(fname)
+    ymean = []
+    ystd = []
+
+    for i in range(128, 4096 + 128, 128):
+        m, s = get_mean_std(data, i)
+        ymean.append(m)
+        ystd.append(s)
+
+    return ymean, ystd
+
+
+xs = np.arange(128, 4096 + 128, 128)
+dgma_cpu_mean, dgma_cpu_std = read_data('ipedirectgma.cpu.txt')
+#dgma_gpu_mean, dgma_gpu_std = read_data('ipedirectgma.gpu.txt')
+cam2_cpu_mean, cam2_cpu_std = read_data('ipecamera2.cpu.txt')
+#cam2_gpu_mean, cam2_gpu_std = read_data('ipecamera2.gpu.txt')
+
+print dgma_cpu_mean[8]
+print cam2_cpu_mean[8]
+
+#plt.rc('font', **dict(family='serif'))
+
+fig = plt.figure(1, (4,3))
+ax = Subplot(fig, 111)
+fig.add_subplot(ax)
+
+plt.xlabel('Packet size (B)')
+plt.ylabel('Latency (us)')
+
+#ax.plot(xs, dgma_gpu_mean, 'x-', label='GPU memory (embedded)', color='#3b5b92')
+
+#ax.plot(xs, cam2_gpu_mean, '.-', label='GPU memory (workstation)', color='#3b5b92')
+ax.plot(xs, cam2_cpu_mean, '.-', markersize=4, label='Setup1', color='#028e2c')
+ax.plot(xs, dgma_cpu_mean, 'x-', markersize=4, label='Setup2', color='#77DD77')
+
+plt.xticks([128, 1024, 2048, 2048+1024, 4096])
+# plt.yticks([2,4,6,8])
+plt.xlim(0, 4200)
+ax.axis["right"].set_visible(False)
+ax.axis["top"].set_visible(False)
+plt.legend(loc='upper left', frameon=False)
+plt.savefig('latency-cpu.pdf', dpi=300, bbox_inches='tight')
+
+
+# A = np.vstack([xs, np.ones(len(xs))]).T
+# mc, cc = np.linalg.lstsq(A, yscm)[0]
+
+# mg, cg = np.linalg.lstsq(A, ysgm)[0]
+
+# print 100000000/ (100000000 * mc + cc)
+# print 100000000/ (100000000 * mg + cg)

data/latency/plot-gpu.py

@@ -0,0 +1,68 @@
+# -*- coding: utf-8 -*-
+
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid.axislines import Subplot
+
+
+def get_mean_std(data, size):
+    s = data[np.where(data[:,0] == size)][:,1]
+
+    # remove 2x std outliers
+    std = np.std(s)
+    outliers = s[np.abs(s - np.mean(s)) > 4 * np.std(s)]
+    s = s[np.abs(s - np.mean(s)) < 4 * np.std(s)]
+    return np.mean(s), np.std(s)
+
+
+def read_data(fname):
+    data = np.loadtxt(fname)
+    ymean = []
+    ystd = []
+
+    for i in range(128, 4096 + 128, 128):
+        m, s = get_mean_std(data, i)
+        ymean.append(m)
+        ystd.append(s)
+
+    return ymean, ystd
+
+
+xs = np.arange(128, 4096 + 128, 128)
+dgma_cpu_mean, dgma_cpu_std = read_data('ipedirectgma.cpu.txt')
+dgma_gpu_mean, dgma_gpu_std = read_data('ipedirectgma.gpu.txt')
+cam2_cpu_mean, cam2_cpu_std = read_data('ipecamera2.cpu.txt')
+cam2_gpu_mean, cam2_gpu_std = read_data('ipecamera2.gpu.txt')
+
+
+#plt.rc('font', **dict(family='serif'))
+
+fig = plt.figure(1, (4,3))
+ax = Subplot(fig, 111)
+fig.add_subplot(ax)
+
+plt.xlabel('Packet size (B)')
+plt.ylabel('Latency (us)')
+
+ax.plot(xs, cam2_gpu_mean, '.-', label='Setup 1', color='#3b5b92')
+ax.plot(xs, dgma_gpu_mean, 'x-', label='Setup 2', color='#aec6cf')
+#ax.plot(xs, dgma_cpu_mean, 'o-', markersize=4, label='Main memory (embedded)', color='#77DD77')
+
+#ax.plot(xs, cam2_cpu_mean, '*-', markersize=4, label='Main memory (workstation)', color='#77DD77')
+
+plt.xticks([128, 1024, 2048, 2048+1024, 4096])
+# plt.yticks([2,4,6,8])
+plt.xlim(0, 4200)
+ax.axis["right"].set_visible(False)
+ax.axis["top"].set_visible(False)
+plt.legend(loc='upper left', frameon=False)
+plt.savefig('latency-gpu.pdf', dpi=300, bbox_inches='tight')
+
+
+# A = np.vstack([xs, np.ones(len(xs))]).T
+# mc, cc = np.linalg.lstsq(A, yscm)[0]
+
+# mg, cg = np.linalg.lstsq(A, ysgm)[0]
+
+# print 100000000/ (100000000 * mc + cc)
+# print 100000000/ (100000000 * mg + cg)

data/latency/plot.py

@@ -35,7 +35,7 @@ cam2_cpu_mean, cam2_cpu_std = read_data('ipecamera2.cpu.txt')
 cam2_gpu_mean, cam2_gpu_std = read_data('ipecamera2.gpu.txt')
 
 
-plt.rc('font', **dict(family='serif'))
+#plt.rc('font', **dict(family='serif'))
 
 fig = plt.figure(1, (4,3))
 ax = Subplot(fig, 111)
@@ -45,10 +45,10 @@ plt.xlabel('Packet size in (B)')
 plt.ylabel('Latency (us)')
 
 ax.plot(xs, dgma_gpu_mean, 'x-', label='GPU memory (embedded)', color='#3b5b92')
-ax.plot(xs, dgma_cpu_mean, 'o-', markersize=4, label='Main memory (embedded)', color='#77DD77')
+#ax.plot(xs, dgma_cpu_mean, 'o-', markersize=4, label='Main memory (embedded)', color='#77DD77')
 
 ax.plot(xs, cam2_gpu_mean, '.-', label='GPU memory (workstation)', color='#3b5b92')
-ax.plot(xs, cam2_cpu_mean, '*-', markersize=4, label='Main memory (workstation)', color='#77DD77')
+#ax.plot(xs, cam2_cpu_mean, '*-', markersize=4, label='Main memory (workstation)', color='#77DD77')
 
 plt.xticks([128, 1024, 2048, 2048+1024, 4096])
 # plt.yticks([2,4,6,8])

data/latency/plot_cpu.py

@@ -0,0 +1,68 @@
+# -*- coding: utf-8 -*-
+
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid.axislines import Subplot
+
+
+def get_mean_std(data, size):
+    s = data[np.where(data[:,0] == size)][:,1]
+
+    # remove 2x std outliers
+    std = np.std(s)
+    outliers = s[np.abs(s - np.mean(s)) > 4 * np.std(s)]
+    s = s[np.abs(s - np.mean(s)) < 4 * np.std(s)]
+    return np.mean(s), np.std(s)
+
+
+def read_data(fname):
+    data = np.loadtxt(fname)
+    ymean = []
+    ystd = []
+
+    for i in range(128, 4096 + 128, 128):
+        m, s = get_mean_std(data, i)
+        ymean.append(m)
+        ystd.append(s)
+
+    return ymean, ystd
+
+
+xs = np.arange(128, 4096 + 128, 128)
+dgma_cpu_mean, dgma_cpu_std = read_data('ipedirectgma.cpu.txt')
+#dgma_gpu_mean, dgma_gpu_std = read_data('ipedirectgma.gpu.txt')
+cam2_cpu_mean, cam2_cpu_std = read_data('ipecamera2.cpu.txt')
+#cam2_gpu_mean, cam2_gpu_std = read_data('ipecamera2.gpu.txt')
+
+
+#plt.rc('font', **dict(family='serif'))
+
+fig = plt.figure(1, (4,3))
+ax = Subplot(fig, 111)
+fig.add_subplot(ax)
+
+plt.xlabel('Packet size in (B)')
+plt.ylabel('Latency (us)')
+
+#ax.plot(xs, dgma_gpu_mean, 'x-', label='GPU memory (embedded)', color='#3b5b92')
+ax.plot(xs, dgma_cpu_mean, 'o-', markersize=4, label='Main memory (embedded)', color='#77DD77')
+
+#ax.plot(xs, cam2_gpu_mean, '.-', label='GPU memory (workstation)', color='#3b5b92')
+ax.plot(xs, cam2_cpu_mean, '*-', markersize=4, label='Main memory (workstation)', color='#77DD77')
+
+plt.xticks([128, 1024, 2048, 2048+1024, 4096])
+# plt.yticks([2,4,6,8])
+plt.xlim(0, 4200)
+ax.axis["right"].set_visible(False)
+ax.axis["top"].set_visible(False)
+plt.legend(loc='upper left', frameon=False)
+plt.savefig('latency-cpu.pdf', dpi=300, bbox_inches='tight')
+
+
+# A = np.vstack([xs, np.ones(len(xs))]).T
+# mc, cc = np.linalg.lstsq(A, yscm)[0]
+
+# mg, cg = np.linalg.lstsq(A, ysgm)[0]
+
+# print 100000000/ (100000000 * mc + cc)
+# print 100000000/ (100000000 * mg + cg)

data/throughput.py

@@ -22,7 +22,7 @@ plt.ylim(0, 7000)
 plt.xticks([1e4,1e6,1e8,1e10])
 plt.yticks([0,2000,4000,6000])
 
-plt.xlabel('Data size in B')
-plt.ylabel('Throughput in MB/s')
+plt.xlabel('Data size (B)')
+plt.ylabel('Throughput (MB/s)')
 plt.legend(loc='lower right',frameon=False)
 plt.savefig('throughput.pdf', dpi=300, bbox_inches='tight')

paper.tex

@@ -279,22 +279,11 @@ Python.
 %% --------------------------------------------------------------------------
 \section{Results}
 
-We carried out performance measurements on two different setups, which are
-described in table~\ref{table:setups}. In both setups, a Xilinx VC709
-evaluation board was used. In Setup 1, the FPGA board and the GPU were plugged
-into a PCIe 3.0 slot, but they were connected to different PCIe Root Complexes
-(RC). In Setup 2, a low-end Supermicro X7SPA-HF-D525 system was connected to a
-Netstor NA255A xeternal PCIe enclosure, where both the FPGA board and the GPU
-were connected to the same RC, as opposed to Setup 1. As stated in the
-NVIDIA's GPUDirect documentation, the devices must share the same RC to
-achieve the best performance~\cite{cuda_doc}.  In case of FPGA-to-CPU data
-transfers, the software implementation is the one described
-in~\cite{rota2015dma}.
 
-\begin{table}[]
+\begin{table}[b]
 \centering
 \small
-\caption{Description of the measurement setup}
+\caption{Setups used for throughput and latency measurements}
 \label{table:setups}
 \tabcolsep=0.11cm
 \begin{tabular}{@{}llll@{}}
@@ -304,12 +293,24 @@ in~\cite{rota2015dma}.
 CPU           & Intel Xeon E5-1630             & Intel Atom D525   \\
 Chipset       & Intel C612                     & Intel ICH9R Express   \\
 GPU           & AMD FirePro W9100              & AMD FirePro W9100   \\
-PCIe slot: System memory    & x8 Gen3 (same RC) & x4 Gen1 (different RC)    \\
+PCIe slot: System memory    & x8 Gen3 & x4 Gen1    \\
 PCIe slot: FPGA \& GPU    & x8 Gen3 (different RC) & x8 Gen3 (same RC)    \\
   \bottomrule
 \end{tabular}
 \end{table}
 
+We carried out performance measurements on two different setups, which are
+described in table~\ref{table:setups}. In both setups, a Xilinx VC709
+evaluation board was used. In Setup 1, the FPGA board and the GPU were plugged
+into a PCIe 3.0 slot, but they were connected to different PCIe Root Complexes
+(RC). In Setup 2, a low-end Supermicro X7SPA-HF-D525 system was connected to a
+Netstor NA255A xeternal PCIe enclosure, where both the FPGA board and the GPU
+were connected to the same RC, as opposed to Setup 1. As stated in the
+NVIDIA's GPUDirect documentation, the devices must share the same RC to
+achieve the best performance~\cite{cuda_doc}.  In case of FPGA-to-CPU data
+transfers, the software implementation is the one described
+in~\cite{rota2015dma}.
+
 %% --------------------------------------------------------------------------
 \subsection{Throughput}
 
@@ -322,7 +323,7 @@ PCIe slot: FPGA \& GPU    & x8 Gen3 (different RC) & x8 Gen3 (same RC)    \\
 \label{fig:throughput}
 \end{figure}
 
-In order to evaluate the performance of the DMA engine, measurements of pure
+In order to evaluate the maximum performance of the DMA engine, measurements of pure
 data throughput were carried out using Setup 1. The results are shown in
 \figref{fig:throughput} for transfers to the system's main memory as well as
 to the global memory. For FPGA-to-GPU data transfers bigger than 95 MB, the
@@ -342,36 +343,57 @@ handshaking sequence between DMA engine and the hosts.
   \centering
   \begin{subfigure}[b]{.49\textwidth}
     \centering
-    \includegraphics[width=\textwidth]{figures/latency}
-    \label{fig:latency_vs_size}
+    \includegraphics[width=\textwidth]{figures/latency-cpu}
+  
+    \label{fig:latency-cpu}
+  \vspace{-0.4\baselineskip}
+    \caption{}
   \end{subfigure}
   \begin{subfigure}[b]{.49\textwidth}
-    \includegraphics[width=\textwidth]{figures/latency-hist}
-    \label{fig:latency_hist}
-  \end{subfigure}
-  \caption{Measured round-trip latency for FPGA to main memory data transfers.}
-  \label{fig:cpu_latency}
+    \includegraphics[width=\textwidth]{figures/latency-gpu}
+
+    \label{fig:latency-gpu}
+  \vspace{-0.4\baselineskip}
+    \caption{}
+    \end{subfigure}
+  \caption{Measured round-trip latency for data transfers to system main memory (a) and GPU memory (b).}
+  \label{fig:latency}
 \end{figure}
 
 
 We conducted the following test in order to measure the latency introduced by the DMA engine : 
-1) the host starts a DMA transfer by issuing the \emph{start\_dma} command
-2) the DMA engine transmits data into the system main memory
-3) the DMA engine notifies the host that new data is present by writing into a specific address in the system main memory
-4) the host acknowledges that data has been received by issuing the the \emph{stop\_dma} command
+1) the host starts a DMA transfer by issuing the \emph{start\_dma} command.
+2) the DMA engine transmits data into the system main memory.
+3) when all the data has been transferred, the DMA engine notifies the host that new data is present by writing into a specific address in the system main memory.
+4) the host acknowledges that data has been received by issuing the the \emph{stop\_dma} command.
 
+The correct ordering of the packets is assured by the PCIe protocol. 
 A counter on the FPGA measures the time interval between the \emph{start\_dma}
 and \emph{stop\_dma} commands with a resolution of 4 ns, therefore measuring
-the round-trip latency of the system.
-
-The results of 1000 measurements of the round-trip latency using system memory
-are shown in \figref{fig:latency-hist}. The latency for Setup 1 and Setup 2
-are, respectively, XXX \textmu s and XXX \textmu s. The non-Gaussian
-distribution 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.
-
-
+the round-trip latency of the system. The round-trip latencies for data
+transfers to system main memory and GPU memory are shown in
+\figref{fig:latency}.
+
+When system main memory is used,
+latencies as low as 1.1 \textmu s are achieved with Setup 1 for a packet size
+of 1024 B. The higher latency and the dependance on size measured with Setup 2
+are caused by the slower PCIe x4 Gen1 link connecting the FPGA board to the system main memory.
+
+The same test was performed when transferring data inside GPU memory, but also
+in this case the notification is written to systen main memory. This approach
+was used because the latency introduced by OpenCL scheduling (\~ 100 \textmu
+s) does not allow for a direct measurement based only on DirectGMA
+communication. When connecting the devices to the same RC, as in Setup 2, a
+latency of 2 \textmu is achieved (limited by the latency to system main
+memory, as seen in \figref{fig:latency}.a. On the contrary, if the FPGA board
+and the GPU are connected to different RC as in Setup 1, the latency increases
+significantly. It must be noted that the low latencies measured with Setup 1
+for packet sizes below 1 kB seem to be due to a caching mechanism  inside the
+PCIe switch, and it is not clear whether data has been successfully written
+into GPU memory when the notification is delivered to the CPU. This effect
+must be taken into account for future implementations as it could potentially
+lead to data corruption.
+ 
 \section{Conclusion and outlook}
 
 We developed a hardware and software solution that enables DMA transfers
@@ -387,14 +409,13 @@ system a cost-effective alternative to larger workstations.
 We measured a round-trip latency of 1 \textmu s when transfering data between
 the DMA engine with system main memory. We also assessed the applicability of
 DirectGMA in low latency applications: preliminary results shows that
-latencies as low as 2 \textmu s can by achieved when writing data in the GPU
+latencies as low as 2 \textmu s can by achieved during data transfers to GPU
 memory.  However, at the time of writing this paper, the latency introduced by
-OpenCL scheduling is in the range of hundreds of \textmu s. Therefore,
-optimization of the GPU- DMA interfacing OpenCL code is ongoing with the help
-of technical support by AMD, in order to lift the current limitation and
-enable the use of our implementation in low latency applications. Moreover,
-measurements show that for low latency applications dedicated hardware must be
-used in order to achieve the best performance.
+OpenCL scheduling is in the range of hundreds of \textmu s. Optimization of
+the GPU-DMA interfacing OpenCL code is ongoing with the help of technical
+support by AMD, in order to lift the current limitation and enable the use of
+our implementation in low latency applications. Moreover, measurements show
+that dedicated hardware must be employed in low latency applications.
 
 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
@@ -415,13 +436,9 @@ 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.
+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