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updated Evaluation (Experimetnal Setup)

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1 parent 920a9b7 commit a3be4bc95b62d02ec133b93aaf9d43300b7acfd5 @shinpei0208 committed Mar 28, 2013
Showing with 26 additions and 2 deletions.
  1. +1 −1 draft/conclusion.tex
  2. BIN draft/draft.pdf
  3. +25 −1 draft/evaluation.tex
@@ -31,7 +31,7 @@ \section{Conclusion}
coordinations of computations and I/O devices.
Since real-world applications require camera sensors to obtain input
images while GPUs are compute devices off the host computer, the data
-I/O latency could become a bottleneck upon data buses.
+I/O latency could become a non-trivial bottleneck on the data bus.
In this scenario, we need enhanced system support such as zero-copy
approaches \cite{Kato13} to minimize the data latency raised between
camera sensors and GPUs.
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@@ -1,4 +1,28 @@
-10 pictures and success rate is 95 to 99\%.
+We now demonstrate performance improvements brought by our GPU
+implementations using the existing vehicle detection program
+We further provide the details of performance comparisons among our GPU
+implementations and prior CPU implementations to discuss fundamental
+factors that allowed the GPU to outperform the CPU.
+\subsection{Experimental Setup}
+We prepare three variants of the vehicle detection program implemented
+using (i) a single core of the multicore CPU, (ii) multiple cores of the
+multicore CPU, (iii) and massively parallel compute cores of the GPU.
+The CPU implementations use the Intel Core i7 2700K series while we
+provide several varied GPUs for the GPU implementations: namely NVIDIA
+GeForce GTX 560 Ti, GTX 580, GTX 680, Titan, and K20X.
+The same set of 10 images as previous work \cite{Niknejad12} is used as
+input data and their average computation time is considered as a major
+performance metrics.
+Note that this computation time includes all relevant pieces of image
+processing such as image loading and output rendering in addition to the
+primary object detection part.
+\subsection{Experimental Results}

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