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Ranking

Problem size: 256 | 512 | 1024
Rank Algorithm Description qrmse* Error Hist. PSNR TimePerformance
[HU] [dB] [s]
1
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
0.0599.170.1100.00%
2
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
0.1688.360.1100.00%
3
GPU
AccuRabbit
Submitter: Oleg Konings,Tobias Funk ,Scott Hsieh,Paul Kahn
Institution: Triple Ring Technologies

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

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
0.03102.460.194.86%
4
GPU
CERA on GTX 680
Submitter: Matthias Elter
Institution: Siemens AG

RabbitCT dataset version: 2
Date of submission: 2013-02-15

The CUDA 5.0 based CERA back-projection implementation (extended with CUDA streams) running on a NVIDIA GTX 680.
0.1787.480.623.68%
5
GPU
RapidRabbit
Submitter: Eric Papenhausen, Ziyi Zheng
Institution: Stony Brook University

RabbitCT dataset version: 1
Date of submission: 2012-10-18

A CUDA 3.0 based back projection implementation using a variety of optimization techniques.
2.8463.180.915.62%
6
GPU
CERA on GTX 670
Submitter: Matthias Elter
Institution: Siemens AG

RabbitCT dataset version: 1
Date of submission: 2012-06-11

A CUDA 4.2 based back-projection implementation running on a NVIDIA GTX 670 GPU.
2.8463.181.212.00%
7
GPU
CERA on GTX 570
Submitter: Matthias Elter
Institution: Siemens AG

RabbitCT dataset version: 1
Date of submission: 2012-06-11

A CUDA 4.1 based back-projection implementation running on a NVIDIA GTX 570 GPU.
2.8463.181.49.87%
8
GPU
ConradCL
Submitter: Andreas Maier
Institution: Pattern Recognition Lab, Friedrich-Alexander University Erlangen-Nuremberg

RabbitCT dataset version: 2
Date of submission: 2013-09-06

OpenCL Backprojector implemented in CONRAD.
0.1688.422.55.51%
9
GPU
CLeopatra
Submitter: Sebastian Schuberth
Institution: Zuse Institute Berlin, Department Visualization and Data Analysis

RabbitCT dataset version: 1
Date of submission: 2011-01-19

This is an OpenCL-based implementation in order to evaluate its performance against CUDA, and its scalability across multiple GPUs / CPUs.
2.8563.142.75.16%
10
GPU
SpeedyGonzales
Submitter: Christopher Rohkohl
Institution: Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nuremberg, Germany

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

SpeedyGonzales is a CUDA 2.1 implementation. It uses the nice capabilities of the cuda texture-based interpolation.
2.8463.184.82.95%
11
GPU
RoadRunner
Submitter: Christian Siegl
Institution: Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nuremberg, Germany

RabbitCT dataset version: 1
Date of submission: 2011-05-23

OpenCL implementation using the CUDA 3.1 framework (OpenCL 1.1). Uses texture-based interpolation and coalesced memory access.
2.8463.185.22.72%
12
CPU
TomatoSalad (formerly LolaTBBSSE)
Submitter: Hannes G. Hofmann
Institution: Pattern Recognition Lab, FAU Erlangen

RabbitCT dataset version: 1
Date of submission: 2009-06-16

LolaBunny multi-threaded and vectorized using Intel's Threading Building Blocks (TBB) and SSE.
0.03101.7860.70.23%
13
CPU
LolaSSE
Submitter: Hannes G. Hofmann
Institution: Pattern Recognition Lab, FAU Erlangen

RabbitCT dataset version: 1
Date of submission: 2009-06-16

LolaBunny vectorized using SSE.
0.03101.78105.40.13%
14
CPU
LolaTBB
Submitter: Hannes G. Hofmann
Institution: Pattern Recognition Lab, FAU Erlangen

RabbitCT dataset version: 1
Date of submission: 2009-06-16

LolaBunny multi-threaded using Intel's Threading Building Blocks (TBB).
0.02106.30162.70.09%
15
CPU
LolaOMP
Submitter: Hannes G. Hofmann
Institution: Pattern Recognition Lab, FAU Erlangen

RabbitCT dataset version: 1
Date of submission: 2009-06-16

LolaBunny multi-threaded using OpenMP.
0.00170.00.08%
16
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: 2009-06-16

This is the official RabbitCT reference implementation.
0.00335.10.04%
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 = 2563 (on this page) and Nviews = 496 for the RabbitCT data set.

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