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 +====== Rodinia: Accelerating Compute-Intensive Applications with Accelerators ======
 +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, version 3.1 ([[|Version history]])
 +Rodinia is designed for heterogeneous computing infrastructures with OpenMP, OpenCL and CUDA implementations.
 +^ Applications    ^ Dwarves       ^ Domains         ^Parallel Model         ^Incre. Ver. ^
 +|[[Leukocyte|Leukocyte]]|Structured Grid|Medical Imaging|CUDA, OMP, OCL|_/|
 +|[[Heart_Wall|Heart Wall]]|Structured Grid|Medical Imaging|CUDA, OMP, OCL||
 +|[[MUMmerGPU|MUMmerGPU]]|Graph Traversal|Bioinformatics|CUDA, OMP||
 +|[[CFD_Solver|CFD Solver]]|Unstructured Grid|Fluid Dynamics|CUDA, OMP, OCL||
 +|[[LU_Decomposition|LU Decomposition]]|Dense Linear Algebra|Linear Algebra|CUDA, OMP, OCL|_/|
 +|[[HotSpot|HotSpot]]|Structured Grid|Physics Simulation|CUDA, OMP, OCL||
 +|[[Back_Propagation|Back Propogation]]|Unstructured Grid|Pattern Recognition|CUDA, OMP, OCL||
 +|[[Needleman-Wunsch|Needleman-Wunsch]]|Dynamic Programming|Bioinformatics|CUDA, OMP, OCL|_/|
 +|[[Kmeans|Kmeans]]|Dense Linear Algebra|Data Mining|CUDA, OMP, OCL||
 +|[[Graph_traversal|Breadth-First Search]]|Graph Traversal|Graph Algorithms|CUDA, OMP, OCL||
 +|[[SRAD|SRAD]]|Structured Grid|Image Processing|CUDA, OMP, OCL|_/|
 +|[[Streamcluster|Streamcluster]]|Dense Linear Algebra|Data Mining|CUDA, OMP, OCL||
 +|[[Particle_Filter|Particle Filter]]|Structured Grid|Medical Imaging|CUDA, OMP, OCL||
 +|[[PathFinder|PathFinder]]|Dynamic Programming|Grid Traversal|CUDA, OMP, OCL||
 +|[[Gaussian_Elimination|Gaussian Elimination]]|Dense Linear Algebra|Linear Algebra|CUDA, OCL||
 +|[[k-Nearest_Neighbors|k-Nearest Neighbors]]|Dense Linear Algebra|Data Mining|CUDA, OMP, OCL||
 +|[[LavaMD2|LavaMD2]]|N-Body|Molecular Dynamics|CUDA, OMP, OCL||
 +|[[Myocyte|Myocyte]]|Structured Grid|Biological Simulation|CUDA, OMP, OCL||
 +|[[B+_Tree|B+ Tree]]|Graph Traversal|Search|CUDA, OMP, OCL||
 +|[[GPUDWT|GPUDWT]]|Spectral Method|Image/Video Compression|CUDA, OCL||
 +|[[Hybrid_Sort|Hybrid Sort]]|Sorting|Sorting Algorithms|CUDA, OCL||
 +|[[Hotspot3D|Hotspot3D]]|Structured Grid|Physics Simulation|CUDA, OCL, OMP|Hotspot for 3D IC|
 +|[[Huffman|Huffman]]|Finite State Machine|Lossless data compression|CUDA, OCL||
 +Other applications under evaluation:
 +^Applications ^Dwarves ^Domains ^Parallel Model ^Comment ^
 +|[[SQLite Select|SQLite Select]]|Map Reduce|Relational Database|CUDA|This benchmark needs an OCL version and also may be too simple|
 +|[[3D Stencil|3D Stencil]]|Structured Grid|Cellular Automation|CUDA|Will be superseded by a more sophisticated 3D benchmark|
 +//1. Ana Lucia Varbanescu and Jianbin Fang, Delft University of Technology contributed the OpenCL version.//
 +//2. In collaboration with [[|EssayLamba]] and Lawrence Livermore National Laboratory.//
 +===== License =====
 +Please read the [[|Rodinia license]].
 +Several applications/libraries come with [[|their own licenses]].
 +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.//
 +Retrieved from
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