|Number of watchers on Github||54|
|Number of open issues||8|
|Average time to close an issue||3 months|
|Open pull requests||0+|
|Closed pull requests||1+|
|Last commit||over 3 years ago|
|Repo Created||over 9 years ago|
|Repo Last Updated||over 1 year ago|
|Organization / Author||vtsynergy|
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The OpenDwarfs project provides a benchmark suite consisting of different computation/communication idioms, i.e., dwarfs, for state-of-art multicore CPUs, GPUs, Intel MICs and Altera FPGAs.
The first instantiation of the OpenDwarfs has been realized in OpenCL, as briefly described in
OpenCL and the 13 Dwarfs: A Work in Progress by Feng, Lin, Scogland, and Zhang in the 3rd ACM/SPEC International Conference on Performance Engineering, April 2012.
The current version, which contains an in-depth performance evaluation on a subset of OpenDwarfs, is described in
On the Characterization of OpenCL Dwarfs on Fixed and Reconfigurable Platforms by Krommydas, Feng, Owaida, Antonopoulos, and Bellas in the 25th IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP), June 2014.
A more thorough description of the latest version, including further in-depth performance evaluation for a larger number of OpenDwarfs, is described in
OpenDwarfs: Characterization of Dwarf-based Benchmarks on Fixed and Reconfigurable Architectures by Krommydas, Feng, Antonopoulos, and Bellas in Journal of Signal Processing Systems (JSPS), Springer, October 2015.
The computation/communication idioms are based on the 13 Berkeley Dwarfs: (http://view.eecs.berkeley.edu/wiki/Dwarf_Mine).
Beta: bfs cfd crc fft kmeans lud nw spmv srad swat bwa_hmm nqueens
Packages and libraries needed to build and run the applications.
opencl >= 1.0 (some apps require 1.1, but we do not yet guarantee support for 1.2 in all applications.) autoconf >= 2.63 autoheader automake libtool gcc maker
To build all of the included applications:
$ ./autogen.sh $ mkdir build $ cd build $ ../configure $ make
To build only the applications you select, call configure with the --with-apps option:
$ ../configure --with-apps=srad,gem,cfd
To see a full list of options and applications:
$ ../configure --help
See the application-specific README file in each application's directory.
All the dwarf applications support a common list of options for optionally specifying the OpenCL platform ID (-p)
and OpenCL device ID (-d), or alternatively, the device type (-t). Optionally you can provide -o option to use
optimized kernels. It picks up the optimized kernel for the given device type. For an example, if the device in use
is GPU and -o option is provided, it will use
General format: ./
<platform> :integer ID of platform to use <device> :integer ID of device in <platform> to use <type> : device type to use (0:CPU, 1:GPU, 2:MIC, 3:FPGA) -o :Optional flag to use the optimzed flag for the device in use
Example1: ./astar -p 0 -d 0 -- (selects device with device ID 0 on platform with platform ID 0) Example2: ./astar -t 0 -- (selects CPU device type on default platform with platform ID 0, if available) Example2: ./nw -p 0 -d 0 -o -- (Run the optimized dwarf of device ID 0 on platfrom with platform ID 0)
Notes: If no parameters are supplied, default platform ID is 0 and default device type is CPU.
If -t parameter is given, default platform ID 0 is searched for supplied device type
Notes: SWAT DOES NOT compile for OpenCl and FFT kernel DID NOT fit on Stratix V in this release.
This project has been supported in part by Air Force Research Lab, Altera, AMD, Department of Defense, Harris, Los Alamos National Laboratory, and Xilinx via the NSF Center for High-Performance Reconfigurable Computing (CHREC) under NSF grant IIP-0804155 and indirectly by AFOSR grant FA9550-12-1-0442 and NSF grants CNS-0916719 and MRI-0960081.
Integration for Altera FPGA support for crc and csr, as well as extensions for these benchmarks, have been contributed by Tyler Kenney at IBM.
Part of the OpenDwarfs benchmark suite (as acknowledged in the respective benchmarks' READMEs) was ported to OpenCL from the corresponding CUDA implementations in earlier implementations of the Rodinia benchmark suite (http://www.cs.virginia.edu/~skadron/wiki/rodinia/index.php/Main_Page).