# Difference between revisions of "Rodinia:Accelerating Compute-Intensive Applications with Accelerators"

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===Application Download=== | ===Application Download=== | ||

− | Please read the [http://www.cs.virginia.edu/~sc5nf/license.htm license] | + | Please read the [http://www.cs.virginia.edu/~sc5nf/license.htm Rodinia license]. |

Also, if your use of Rodinia results in a publication, please cite: | Also, if your use of Rodinia results in a publication, please cite: |

## Revision as of 17:52, 10 November 2011

A vision of heterogeneous computer systems that incorporate diverse

accelerators and automatically select the best computational unit for

a particular task is widely shared among researchers and many industry

analysts; however, there are no agreed-upon benchmarks to support the

research needed in the development of such a platform. There are many

suites for parallel computing on general-purpose CPU architectures,

but accelerators fall into a gap that is not covered by previous benchmark

development. Rodinia is released to address this concern.

### The Rodinia Benchmark Suite

Rodinia is designed for heterogeneous computing infrastructures with

OpenMP, OpenCL and CUDA implementations.

Current Rodinia applications:

Applications | Dwarves | Domains | Parallel Model | Incre. Ver. |
---|---|---|---|---|

Leukocyte | Structured Grid | Medical Imaging | CUDA, OMP | ✔ |

Heart Wall | Structured Grid | Medical Imaging | CUDA, OMP | |

MUMmerGPU | Graph Traversal | Bioinformatics | CUDA, OMP | |

CFD Solver^{1} | Unstructured Grid | Fluid Dynamics | CUDA, OMP, OCL | |

LU Decomposition | Dense Linear Algebra | Linear Algebra | CUDA, OMP | ✔ |

HotSpot | Structured Grid | Physics Simulation | CUDA, OMP, OCL | |

Back Propagation | Unstructured Grid | Pattern Recognition | CUDA, OMP, OCL | |

Needleman-Wunsch | Dynamic Programming | Bioinformatics | CUDA, OMP, OCL | ✔ |

Kmeans | Dense Linear Algebra | Data Mining | CUDA, OMP, OCL | |

Breadth-First Search^{1} | Graph Traversal | Graph Algorithms | CUDA, OMP, OCL | |

SRAD | Structured Grid | Image Processing | CUDA, OMP, OCL | ✔ |

Streamcluster^{1} | Dense Linear Algebra | Data Mining | CUDA, OMP, OCL | |

Particle Filter | Structured Grid | Medical Imaging | CUDA, OMP | |

PathFinder | Dynamic Programming | Grid Traversal | CUDA, OCL | |

Gaussian Elimination | Dense Linear Algebra | Linear Algebra | CUDA, OCL | |

k-Nearest Neighbors | Dense Linear Algebra | Data Mining | CUDA, OMP, OCL | |

LavaMD^{2} | Structured Grid | Molecular Dynamics | CUDA, OMP, OCL |

Other applications under evaluation:

Applications | Dwarves | Domains | Parallel Model | Comment |
---|---|---|---|---|

SQLite Select | Map Reduce | Relational Database | CUDA | This benchmark needs an OCL version and also may be too simple |

3D Stencil | Structured Grid | Cellular Automation | CUDA | Will be superseded by a more sophisticated 3D benchmark |

Hybrid Sort | Sorting | Sorting Algorithms | CUDA | This benchmark is under consideration to include in the main suite |

Myocyte | Structured Grid | Biological Simulation | CUDA, OMP | This benchmark needs an OpenCL version |

*
^{1}Ana Lucia Varbanescu and Jianbin Fang, Delft University of Technology contributed the OpenCL version.
*

*
^{2}Collaboration with Lawrence Livermore National Laboratory.
*

### Application Download

Please read the Rodinia license.

Also, if your use of Rodinia results in a publication, please cite:

[1] S. Che, M. Boyer, J. Meng, D. Tarjan, J. W. Sheaffer, S.-H. Lee, and K. Skadron.

Rodinia: A Benchmark Suite for Heterogeneous Computing. In Proceedings of the IEEE

International Symposium on Workload Characterization (IISWC), pp. 44-54, Oct. 2009.

[2] S. Che, J. W. Sheaffer, M. Boyer, L. G. Szafaryn, L. Wang, and K. Skadron.

A Characterization of the Rodinia Benchmark Suite with Comparison to Contemporary

CMP Workloads. In Proceedings of the IEEE International Symposium on Workload

Characterization, Dec. 2010.

* This work is supported by NSF grant nos. IIS-0612049, CNS-0916908 and CNS-0615277, a grant from the SRC under task no. 1607, and grants from AMD, NEC labs, and NVIDIA Research.
*