Difference between revisions of "Rodinia:Accelerating Compute-Intensive Applications with Accelerators"
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* [[Downloads | Download]]
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<em> 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
<em> 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 NEC labs. </em>
Revision as of 15:02, 13 May 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 amongresearchers 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 current benchmark
suites or benchmark development.
The Rodinia Benchmark Suite
Rodinia is designed for heterogeneous computing infrastructures, and,
using OpenMP and CUDA, targets both GPUs and multicore CPUs.
The suite currently consists of 6 applications and 7 kernels. They have
been parallelized with OpenMP for multicore CPUs and with CUDA for
GPUs. We have been preparing for the OpenCL release.
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||Unstructured Grid||Fluid Dynamics||CUDA, OMP|
|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|
|Needleman-Wunsch||Dynamic Programming||Bioinformatics||CUDA, OMP, OCL||✔|
|Kmeans||Dense Linear Algebra||Data Mining||CUDA, OMP, OCL|
|Breadth-First Search||Graph Traversal||Graph Algorithms||CUDA, OMP, OCL|
|SRAD||Structured Grid||Image Processing||CUDA, OMP||✔|
|Streamcluster||Dense Linear Algebra||Data Mining||CUDA, OMP|
|Particle Filter||Structured Grid||Medical Imaging||CUDA, OMP|
Other applications (some with CUDA only):
|SQL Database||Map Reduce||Relational Database||CUDA|
|Nearest Neighbor||Dense Linear Algebra||Data Mining||CUDA, OMP, OCL|
|Gaussian Elimination||Dense Linear Algebra||Linear Algebra||CUDA|
|Cell||Structured Grid||Cellular Automation||CUDA|
|PathFinder||Dynamic Programming||Grid Traversal||CUDA|
|Hybrid Sort||Sorting||Sorting Algorithms||CUDA|
|Myocyte*||Structured Grid||Biological Simulation||CUDA, OMP|
New Rodinia applications coming soon:
|LavaMD*||Structured Grid||Molecular Dynamics||CUDA, OMP|
The applications(*) are relatively hard for compilers to analyze and
generate efficient GPU codes.
Please read the license file.
Also, if your use of Rodinia results in a publication, please cite:
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.(pdf)
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.