Performance comparison of erasure coding libraries
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The storage-benchmarks repository is used for the performance comparison of different erasure coding libraries, currently Kodo, OpenFEC and the Intel Storage Acceleration library (ISA).

If you have any questions or suggestions about the benchmarks, please contact us at our developer mailing list (hosted at Google Groups):


1. Kodo

To obtain a valid Kodo license you must fill out the license request form.

Kodo is available under a research and educational friendly license, see the details in the LICENSE.rst file.

2. ISA

See "About_bsd.txt" in the "isa-l_open_src_2.13" folder.

3. OpenFEC

See "Licence_CeCILL_V2-en.txt" in the "openfec-1.4.2" folder.

The libraries are benchmarked in standalone applications that implement a common benchmarking interface to create a framework for fair comparison. These standalone applications are licensed under the same terms as the original libraries (Kodo license, BSD or CeCILL).


  1. A recent C++11 compiler
  2. yasm (for compiling the Assembly sources in ISA)


Clone the repository:

git clone

How to build it

The benchmarks can be built with waf:

cd storage-benchmarks
python waf configure
python waf build

How to run the benchmarks

The benchmark applications are built in the ./build/linux/benchmark folder, and they can be started with the following commands:


By default, these applications will execute some basic benchmarks with the same default parameters for all libraries.

The benchmarked scenario is the same in all cases:

  1. A random data block is generated which consists of a given number of original symbols (specified by the symbols parameter).
  2. Encoding: This data block is used to generate some encoded symbols (the encoding throughput is measured during this step)
  3. Several original symbols are erased from the data block (this is specified by the loss_rate parameter)
  4. Decoding: The erased original symbols are reconstructed using the encoded symbols (the decoding throughput is measured during this step)

The benchmark results contain the following metrics:

  • goodput (encoding): the total number of encoded bytes divided by processing time (measured in MegaBytes/second)
  • extra_symbols (decoding): some codecs might need more encoded symbols than the number of erased symbols to reconstruct the original symbols, the extra_symbols show this difference (NB: this is measured in number of packets, not in MB/s as shown in the output)
  • goodput (decoding): the total number of reconstructed bytes divided by processing time (measured in MegaBytes/second)

Additional parameters can be given to these binaries to customize the benchmark runs:

--runs=N: the number of repetitions for a given benchmark

--symbols=N: the number of symbols in a block/generation

--symbol_size=N: the size of each symbol in bytes (this should be a multiple of 64)

--loss_rate=0.x: the ratio of erased original symbols

--type=encoder/decoder: enables only the encoder or decoder benchmark type

--python_file=filename: saves the results as a Python dictionary to the given file

--csv_file=filename: saves the results as a CSV table to the given file

--json_file=filename: saves the results as a JSON document to the given file

Extra option for kodo_storage:

--density=0.x: the code density used for the sparse RLNC benchmark

For example, kodo_storage can be invoked with these parameters:

build/linux/benchmark/kodo_storage/kodo_storage --symbols=100 --symbol_size=1000000 --loss_rate=0.2 --csv_file=myfile.csv

We also have a helper script to run the benchmark applications in sequence. You can start it without parameters to use the default settings:


Or you can specify some parameters that will be used for every benchmark application:

sh --runs=10 --symbols=16 --symbol_size=32000
sh --runs=10 --symbols=64 --symbol_size=32000
sh --runs=10 --symbols=16 --symbol_size=1000000
sh --runs=10 --symbols=64 --symbol_size=1000000