FlexFloat Benchmarks

FlexFloat can be used to perform precision tuning of floating-point variables. This repository contains some examples of program adopting the FlexFloat C++ interface.

Build a benchmark

• Enter the folder of a specific benchmark, for instance: cd kmeans
• Execute the make command providing the required paths for FlexFloat: make clean all FLEXFLOAT_INC_DIR=<flexfloat/include path> FLEXFLOAT_LIB_DIR=<flexfloat/build path>

Perform precision tuning with an external tool

Steps to perform precision tuning (using fpPrecisionTuning):

• [Prerequisites] Install Python 2.7 and its MPI support. On Ubuntu 14.04 systems (or later versions) this can be accomplished with: sudo apt-get install python sudo apt-get install python-mpi4py
• Download the fpPrecisionTuning toolchain from its official git repo: git clone https://github.com/minhhn2910/fpPrecisionTuning.git
• Enter the folder of a specific benchmark, for instance: cd flexfloat-benchmarks/kmeans
• Execute the precision tuning tool: mpirun -np 8 <fpPrecisionTuning path>/PrecisionAnalysis/greedy_search_mpi.py 1 kmeans_flex

Additional notes:

• The accuracy required for the results can be tuned changing the error_rate parameter in the "greedy_search_mpi.py" script (located in fpPrecisionTuning folder).
• The available floating-point types can be modified updating the set_coefficient_bits function in the "compile.py" script (located in each benchmark folder). The current configuration is taking into account the additional floating-point types defined by the PULP smallFloat extension.
• The output provides the minimum bit-width of the mantissa for each input variable (a total order for program variables is defined in "compile.py").
• Use make clean all to remove all temporary files from a benchmark folder.