This page presents **a Channel Coding Software Decoders "Hall of Fame"**. It allows to see at a glance what has been achieved, what can be expected from today software decoders, and easily compare their respective characteristics. For now, three wide code families are considered: **the Turbo codes (LTE, LTE-Advanced, CCSDS, etc.), the Low-Density Parity-Check (LDPC) codes (Wi-Fi, WiMAX, CCSDS, WRAN, DVB-S2, etc.), and the recently introduced Polar codes (candidates for 5G)**.

All the presented results, collected from the state-of-the-art research papers published in the field, consider a **BPSK (Bit Phase-Shift Keying) modulation/demodulation** and an **AWGN (Additive White Gaussian Noise) channel**.

**This Hall of Fame strives to present results as fairly as possible**: for example, early termination criteria are not taken into consideration while computing throughput, in order to compare raw performances using a consistent method. It remains possible, however, for typos/glitches/mistakes to have inadvertantly made it to the scoreboard. In that eventuality, do not hesitate to contact us. If you would like to have your decoder listed as well in the Hall of Fame: **please send us the corresponding research paper references, and we will be delighted to add them**.

In blue, the results simulated or reproducible with AFF3CT: our Open-source communication chain dedicated to the Forward Error Correction (FEC) simulations.

*Last update: 04-21-2017.*

Belief Propagation (BP)

Work | Year | Platform | Implem. | Pre. | Inter | Code | $i$ | $\mathrm{Lat.}$ | $\mathrm{Thr.}$ | $\mathrm{NThr.}$ | $\mathrm{TNDC}$ | ${E}_{d}$ |
---|---|---|---|---|---|---|---|---|---|---|---|---|

[1] | 2008 | CELL | F-MS | 8 | 96 | (1248, 624) | 25 | 3653 | 32.8 | 16.4 | 0.052 | 6098 |

[2] | 2008 | 8800 GT | F-SPA+ | 32 | 1 | (4096, 2048) | 6 | 467000 | 0.01 | 0.001 | 0.000006 | 105000000 |

[3] | 2009 | 8800 GTX | F-SPA | 32 | - | (1908, 1696) | 50 | - | 0.08 | 0.08 | 0.0005 | 2200000 |

[4] | 2011 | CELL | F-OMS | 8 | 1 | (960, 480) | 15 | 74 | 13.0 | 3.9 | 0.0095 | 51282 |

[5] | 2011 | Tesla C1060 | F-LSPA | 32 | 1 | (8000, 4000) | 50 | 8638 | 0.92 | 0.92 | 0.0029 | 217391 |

[6] | 2011 | Tesla C2050 | F-MS | 8 | 16 | (64800, 21600) | 30 | 13275 | 78.1 | 46.86 | 0.091 | 5271 |

[7] | 2011 | 8800 GTX | F-SPA | 8 | - | (8000, 4000) | 50 | - | 10.1 | 10.1 | 0.058 | 17426 |

[7] | 2011 | CELL | F-SPA | 32 | 24 | (1024, 512) | 50 | 1719 | 14.3 | 14.3 | 0.181 | 13986 |

[7] | 2011 | 2xE5530 | F-SPA | 32 | 1 | (8000, 4000) | 50 | 13115 | 0.61 | 0.61 | 0.0079 | 262295 |

[8] | 2011 | GTX 285 | F-SPA+ | 32 | 1 | (2304, 1152) | 15 | 1097 | 2.1 | 0.63 | 0.0018 | 323810 |

[9] | 2011 | GTX 470 | F-LSPA+ | 32 | 300 | (1944, 972) | 50 | 57743 | 10.1 | 10.1 | 0.018 | 21287 |

[10] | 2011 | GTX 470 | F-LSPA | 32 | 224 | (2304, 1152) | 10 | 10533 | 49.0 | 9.8 | 0.018 | 21939 |

[11] | 2012 | HD 5870 | F-MS | 8 | 500 | (8000, 4000) | 10 | 22222 | 180.0 | 36.0 | 0.075 | 5222 |

[11] | 2012 | Tesla C2050 | F-MS | 8 | 500 | (8000, 4000) | 10 | 20000 | 200.0 | 40.0 | 0.078 | 6175 |

[12] | 2012 | GTX 480 | F-SPA+ | 32 | 1 | (2048, 1723) | 50 | 426 | 4.8 | 4.8 | 0.0071 | 52083 |

[13] | 2012 | Tesla C2050 | F-MS | 8 | 128 | (16200, 8100) | 50 | 26083 | 79.5 | 79.5 | 0.154 | 3107 |

[13] | 2012 | i7-950 | F-MS | 8 | 128 | (16200, 8100) | 50 | 113934 | 18.2 | 18.2 | 0.093 | 7143 |

[14] | 2013 | i7-3960X | F-NMS+ | 8 | 12 | (9216, 4608) | 10 | 1202 | 92.0 | 18.4 | 0.058 | 7065 |

[15] | 2013 | i7-2600K | L-OMS | 8 | 1 | (524280, 262140) | 5 | 17420 | 30.1 | 3.0 | 0.055 | 31667 |

[16] | 2013 | Cortex-A9 | F-MS | 8 | 128 | (16200, 8100) | 20 | 592457 | 3.5 | 1.4 | 0.014 | 2857 |

[17] | 2013 | GTX TITAN | F-NMS+ | 32 | 50 | (2304, 1152) | 10 | 1266 | 304.2 | 60.8 | 0.027 | 4112 |

[17] | 2013 | GTX TITAN | F-NMS+ | 32 | 6 | (2304, 1152) | 10 | 207 | 66.8 | 13.4 | 0.006 | 18657 |

[18] | 2013 | GTX 580 | CL-MS | 8 | 1024 | (2304, 1152) | 5 | 3322 | 710.2 | 142.0 | 0.180 | 1718 |

[19] | 2014 | GTX 660 Ti | F-SPA | 8 | 12544 | (8000, 4000) | 50 | 954100 | 105.2 | 105.2 | 0.085 | 1426 |

[20] | 2014 | GTX 660 | HL-OMS | 8 | 16384 | (1944, 972) | 10 | 34362 | 926.9 | 185.4 | 0.049 | 755 |

[21] | 2016 | i7-4960HQ | HL-NMS+ | 8 | 128 | (2304, 1152) | 50 | 1359 | 217 | 217 | 0.500 | 217 |

[22] | 2016 | GTX 470 | PL-MS | 32 | 256 | (1944, 972) | 10 | 9739 | 51.1 | 10.2 | 0.019 | 21078 |

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*IEEE Communications Letters*, vol. 18, no. 2, pp. 344-347, 2014. - B. Le Gal, C. Jego, and J. Crenne, “A high throughput efficient approach for decoding LDPC codes onto GPU devices,”
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