Compilation Process

The easiest way of using MP-SPDZ is using compile.py as described below. If you would like to run compilation directly from Python, see Direct Compilation in Python.

After putting your code in Program/Source/<progname>.[mpc|py], run the compiler from the root directory as follows

./compile.py [options] <progname> [args]

The arguments <progname> [args] are accessible as list under program.args within progname.[mpc|py], with <progname> as program.args[0]. The resulting program for the virtual machine will be called <progname>[-<arg0>[-<arg1>...].

The following options influence the computation domain:

-F <integer length>

--field=<integer length>

Compile for computation modulo a prime and the default integer length. This means that secret integers are assumed to have at most said length unless explicitly told otherwise. The compiled output will communicate the minimum length of the prime number to the virtual machine, which will fail if this is not met. This is the default with an integer length set to 64. When not specifying the prime, the minimum prime length will be around 40 bits longer than the integer length. Furthermore, the computation will be optimistic in the sense that overflows in the secrets might have security implications.

-P <prime>

--prime=<prime>

Use bit decomposition by Nishide and Ohta with a concrete prime modulus for non-linear computation. This can be used together with -F, in which case integer length has to be at most the prime length minus two. The security implications of overflows in the secrets do not go beyond incorrect results. You can use prime order domains without specifying this option. Using this option involves algorithms for non-linear computation which are generally more expensive but allow for integer lengths that are close to the bit length of the prime. See Non-linear Computation for more details

-R <ring size>

--ring=<ring size>

Compile for computation modulo 2^(ring size). This will set the assumed length of secret integers to one less because many operations require this. The exact ring size will be communicated to the virtual machine, which will use it automatically if supported.

-B <integer length>

--binary=<integer length>

Compile for binary computation using integer length as default.

-G

--garbled-circuit

Compile for garbled circuits (does not replace -B).

For arithmetic computation (-F, -P, and -R) you can set the bit length during execution using program.set_bit_length(length). For binary computation you can do so with sint = sbitint.get_type(length). Use sfix.set_precision() to change the range for fixed-point numbers.

The following options switch from a single computation domain to mixed computation when using in conjunction with arithmetic computation:

-X

--mixed

Enables mixed computation using daBits.

-Y

--edabit

Enables mixed computation using edaBits.

The implementation of both daBits and edaBits are explained in this paper.

-Z <number of parties>

--split=<number of parties>

Enables mixed computation using local conversion. This has been used by Mohassel and Rindal and Araki et al. It only works with additive secret sharing modulo a power of two.

The following options change less fundamental aspects of the computation:

-D

--dead-code-elimination

Eliminates unused code. This currently means computation that isn’t used for input or output or written to the so-called memory (e.g., Array; see types).

-b <budget>

--budget=<budget>

Set the budget for loop unrolling with for_range_opt() and similar. This means that loops are unrolled up to budget instructions. Default is 1000 instructions.

-C

--CISC

Speed up the compilation of repetitive code at the expense of a potentially higher number of communication rounds. For example, the compiler by default will try to compute a division and a logarithm in parallel if possible. Using this option complex operations such as these will be separated and only multiple divisions or logarithms will be computed in parallel. This speeds up the compilation because of reduced complexity.

-l

--flow-optimization

Optimize simple loops (for <iterator> in range(<n>)) by using for_range_opt() and defer if statements to the run time.

Direct Compilation in Python

You may prefer to not have an entirely static .mpc file to compile, and may want to compile based on dynamic inputs. For example, you may want to be able to compile with different sizes of input data without making a code change to the .mpc file. To handle this, the compiler an also be directly imported, and a function can be compiled with the following interface:

# hello_world.mpc
from Compiler.library import print_ln
from Compiler.compilerLib import Compiler

compiler = Compiler()

@compiler.register_function('helloworld')
def hello_world():
    print_ln('hello world')

if __name__ == "__main__":
    compiler.compile_func()

You could then run this with the same args as used with compile.py:

python hello_world.mpc <compile args>

This is particularly useful if want to add new command line arguments specifically for your .mpc file. See test_args.mpc for more details on this use case.

Note that when using this approach, all objects provided in the high level interface (e.g. sint, print_ln) need to be imported, because the .mpc file is interpreted directly by Python (instead of being read by compile.py.)

Compilation vs run time

The most important thing to keep in mind is that the Python code is executed at compile-time. This means that Python data structures such as list and dict only exist at compile-time and that all Python loops are unrolled. For run-time loops and lists, you can use for_range() (or the more optimizing for_range_opt()) and Array. For convenient multithreading you can use for_range_opt_multithread(), which automatically distributes the computation on the requested number of threads.

This reference uses the term ‘compile-time’ to indicate Python types (which are inherently known when compiling). If the term ‘public’ is used, this means both compile-time values as well as public run-time types such as regint.