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Ranking

Problem size: 256 | 512 | 1024
Rank Algorithm Description qrmse* Error Hist. PSNR TimePerformance
[HU] [dB] [s]
1
GPU
Elmer
Submitter: Sam Hawker
Institution: Nikon Metrology

RabbitCT dataset version: 2
Date of submission: 2016-07-21

Elmer is a multi-GPU implementation using CUDA 5.5
0.1688.291.2100.00%
2
GPU
Elmer (high accuracy)
Submitter: Sam Hawker
Institution: Nikon Metrology

RabbitCT dataset version: 2
Date of submission: 2016-07-21

High accuracy version of Elmer avoiding the fixed-point bilinear interpolation hardware
0.0598.522.159.40%
3
GPU
AccuRabbit
Submitter: Oleg Konings,Tobias Funk ,Scott Hsieh,Paul Kahn
Institution: Triple Ring Technologies

RabbitCT dataset version: 2
Date of submission: 2017-01-23

We have developed a fast accurate 2 GPU implementation of the back projection algorithm for output volumes of sizes 256^3, 512^3, 1024^3 and 2048^3
0.03101.963.931.40%
4
GPU
GAPBckPrj
Submitter: Giovanni Di Domenico
Institution: University of Ferrara - Italy

RabbitCT dataset version: 2
Date of submission: 2016-07-29

GAPBckPrj is a CUDA 8.0 back-projection implementation.
0.1688.305.921.06%
5
GPU
Thumper
Submitter: Timo Zinßer
Institution: Siemens AG

RabbitCT dataset version: 2
Date of submission: 2012-12-20

Thumper is a CUDA-based back-projection implementation.
0.1688.316.020.41%
6
GPU
CERA on Tesla C2070
Submitter: Matthias Elter
Institution: Siemens AG

RabbitCT dataset version: 1
Date of submission: 2012-06-15

A CUDA 4.1 based back-projection implementation running on a NVIDIA Tesla C2070 GPU.
2.8463.1736.13.41%
7
CPU
fastrabbitEX
Submitter: Jan Treibig
Institution: RRZE (FAU Erlangen)

RabbitCT dataset version: 1
Date of submission: 2011-07-21

OpenMP parallel implementation with SSE assembly kernel. A variant with higher precision is also available, see details.
1.6867.7343.82.82%
8
GPU
CLeopatra
Submitter: Sebastian Schuberth
Institution: Zuse Institute Berlin, Department Visualization and Data Analysis

RabbitCT dataset version: 1
Date of submission: 2011-02-18

This is an OpenCL-based implementation in order to evaluate its performance against CUDA, and its scalability across multiple GPUs / CPUs.
2.8463.1786.61.42%
9
CPU
fastrabbit
Submitter: Jan Treibig
Institution: RRZE (FAU Erlangen)

RabbitCT dataset version: 1
Date of submission: 2011-07-21

OpenMP parallel implementation with SSE assembly kernel.
0.03102.44129.80.95%
10
CPU
TomatoSalad (formerly LolaTBBSSE)
Submitter: Hannes G. Hofmann
Institution: Pattern Recognition Lab, FAU Erlangen

RabbitCT dataset version: 1
Date of submission: 2009-07-07

LolaBunny multi-threaded and vectorized using Intel's Threading Building Blocks (TBB) and SSE.
0.03101.71959.00.13%
11
CPU
LolaTBB
Submitter: Hannes G. Hofmann
Institution: Pattern Recognition Lab, FAU Erlangen

RabbitCT dataset version: 1
Date of submission: 2009-07-07

LolaBunny multi-threaded using Intel's Threading Building Blocks (TBB).
0.02106.612,687.80.05%
12
CPU
LolaOMP
Submitter: Hannes G. Hofmann
Institution: Pattern Recognition Lab, FAU Erlangen

RabbitCT dataset version: 1
Date of submission: 2009-07-07

LolaBunny multi-threaded using OpenMP.
0.002,941.80.04%
13
CPU
LolaSSE
Submitter: Hannes G. Hofmann
Institution: Pattern Recognition Lab, FAU Erlangen

RabbitCT dataset version: 1
Date of submission: 2009-07-07

LolaBunny vectorized using SSE.
0.03101.716,634.50.02%
14
CPU
LolaBunny
Submitter: RabbitCT Team
Institution: Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nuremberg, Germany

RabbitCT dataset version: 1
Date of submission: 2009-06-26

This is the official RabbitCT reference implementation.
0.0025,362.20.00%
Notes:
*Mean squared error. qrmse = sum(error2) / Nvoxels.
The error histogram shows the distribution of the absolute errors. Bins 0–9 count errors between (bin, (bin+1)] HU, bin 10 counts all errors > 10 HU. Figures show relative amount: y = count / Nvoxels * 100.
Peak SNR. PSNR = 10.0 * log10(4095.0*4095.0 / qrmse2).
Runtime for backprojection of all views, measured in seconds.
Performance relative to fastest implementation for this problem size. perf = tavg,fastest / tavg
versionBetween RabbitCT dataset version 1 and 2 we have changed the reference volume due to an error in the first version. Learn more
whereNvoxels = 10243 (on this page) and Nviews = 496 for the RabbitCT data set.

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