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spectrum-impl

Build Status

Implementation and experiments for the Spectrum paper.

Disclaimer: research code, not for production use.

Project Structure

Our Spectrum implementation is written primarily in Rust. This project is a Cargo workspace containing 3 crates; run tests for all three with cargo test. (We use some pretty new features, so you may need a recent nightly of Rust; see the "Experiments" section).

For details, see the (slightly outdated) design document.

spectrum_primitives

This crate contains interfaces and constructions for primitives used in Spectrum. Highlights:

  • We start with low-level algebraic primitives (Group, Field). We include both toy constructions (IntsMod<p>, which is generic over the modulus; use IntMod<7> or so for debugging) and the values we ultimately use (a prime-order subgroup of the Jubjub elliptic curve and its associated scalar field).
  • We then build a pseudorandom generator (Prg), including a [seed-homomorphic] variant.
  • We build distributed point functions (Dpf) and an extension: verifiable distributed point functions (Vdpf), which have additional methods for creating and auditing proofs.
  • Miscellaneous utilities include a Sampleable trait for taking samples of these objects (à la rand::distributions::Distribution) and a trait for secret-shareable (Shareable) values.

For each of these, we provide an interface and an associated test suite to verify correctness properties (using property-based testing). For instance, we check associativity for a Group G (clones omitted):

#[test]
fn test_associative(a: G, b: G, c: G) {
    prop_assert_eq!((a + b) + c, a + (b + c));
}

The test runner (proptest) then checks this for random G instances; if an error is found, a minimal case is reported (e.g., the smallest G that triggers the error). We do not check security.

This contains (very rudimentary) benchmarks too.

spectrum_protocol

This crate contains the interface and constructions for the Spectrum protocol (Protocol, unimaginatively). As above, we provide correctness tests. The constructions are a thin wrapper around the primitives in the above crate; they're generic over the choice of Vdpf.

This crate also introduces protocol buffer definitions for the protocol

spectrum

This contains the practical implementation: client/server binaries, data structures, multithreading, service discovery/coordination, etc. It's built on tonic, a gRPC implementation.

experiments

This is a Python module which runs experiments on Spectrum and other systems. The top-level directory is system-agnostic (though opinionated about workflow). To add more systems, add a new subdirectory (express contains a nice, mostly minimal example). See "Experiments" section for usage.

data

This contains the parameters for generating the set of experiments we run (make_experiments.py). Contains some helper scripts for collecting miscellanous data (dates, Rust version, instance costs, LoC count) and running several experiments sequentially (run_experiments.sh) and comparing performance across commits (benchmark_diff.sh).

Good news: assuming local dependencies are installed and configured correctly, this (run_experiments.sh) is sufficient to collect all the data in the paper. Bad news: this is the least likely parts to work on anybody else's machine (hardcoded paths, references to OS-local files); sorry! They should be sufficient to piece together something that works for you.

Installation

Development was done on NixOS, and a shell.nix file contains a complete record of dependencies (though not exact versions) for easy installation (nix shell shell.nix).

Local Development

For local development, the main dependencies are:

  • Rust (likely a recent nightly needed)
  • Build dependencies: m4, proto
  • Libraries including (but not limited to): libgmp6, libffi, openssl, glibc

Optional dependencies include:

  • etcd, for local testing involving service discovery
  • gnuplot, for visualizing local benchmarks

Experiments

We require:

  • Packer, for building machine images
  • Terraform, for managing cloud deployments
  • Python (3.8), for running the experiment scripts
  • Python dependencies (in experiments/requirements.txt; venv advised)

For the final numbers (including metadata, code count, etc.) we require:

  • cURL: for pulling data from a couple of places
  • AWS CLI (v2): for instance type data (cores, RAM, etc.)
  • Tokei: for lines-of-code count

Usage: Spectrum binaries

Throughout, use --help to see parameters.

We provide client, viewer, worker, leader, publisher, and setup binaries, roughly as described in the design document. These are relatively complicated to use together, so we recommend using the experiment scripts for evaluation and below methods for local testing.

For local development, the primary entry point is cargo run --bin run_inmem, which will run all of the parties in the protocol in-memory.

We can run some quick local tests of the whole system (including local TCP connections) with cargo run --bin run_processes. This requires some setup:

  • It will take care of running etcd for you, but you need an etcd binary on your $PATH.
  • Set $SPECTRUM_BIN_DIR to point to a directory containing compiled Spectrum binaries.

Make sure to use --release, and maybe set ulimit -n 8192 if you're attempting to run larger scale tests. We also provide an entry point for more structured local testing. By default, this runs some hard-coded parameters, but we accept arbitrary inputs in CSV format:

$ cat input.csv
2,1,50,8,,1250000
2,1,50,8,63,1250000
$ cargo run \
      --release \
      --bin local_experiments \
      -- \
      --output data.csv \
      --input input.csv
Running: InputRecord { groups: 2, group_size: 1, clients: 50, channels: 8, security_bits: None, msg_size: 1250000 }...done. elapsed time 293.903964ms
Running: InputRecord { groups: 2, group_size: 1, clients: 50, channels: 8, security_bits: Some(63), msg_size: 1250000 }...done. elapsed time 600.449376ms
$ cat data.csv
groups,group_size,clients,channels,security_bits,msg_size,elapsed_millis
2,1,50,8,,1250000,293
2,1,50,8,60,1250000,600

This mode works interactively, too (--input -), though for one-off executions run_inmem is probably be better.

Experiments

We've tried to make our measurements (including measurements of other systems) as reproducible as possible, but none of this has been tested except on the authors' machines; small tweaks may be needed.

To that end, our cloud-based experiments can be run with the provided scripts.

Running

First, you need the usual AWS credentials and all dependencies installed.

Then, with the following in a file called experiments.json:

[
{"workers_per_machine": 1, "channels": 3, "clients": 200, "message_size": 10240},
{"workers_per_machine": 1, "channels": 3, "clients": 200, "message_size": 10240}
]

Run python experiments spectrum --build release.

Parameters are explained in the script help for each system. If all goes well, you should see something like (transcription in this document below if images are unsuitable):

Experiment script output

The script batches the inputs that have the same "environment" (need the same cloud resources) for better performance.

For debugging, we provide simple SSH scripts for each system that allows SSH easy in to Terraform-deployed VMs. For instance, run

python -m experiments.spectrum.ssh --worker 1

to SSH into worker machine 1. Use --help to see available options.

By default, the experiment script leaves resources running! This allows for running batches more efficiently. Use the --cleanup flag to tear down on completion, or run:

echo [] | python -m experiments --cleanup spectrum -

Under the hood

We provide Terraform templates for deployment, and Packer configurations for machine images. The Python script does the following:

  • Builds a machine image using Packer, if needed (e.g., if sources have changed). Use the --force-rebuild flag to invalidate this cache (in a file on disk).
  • Use Terraform, passing in the machine image and environment parameters (number of machines, machine type), to set up infrastructure.
  • SSH into these machines to run the experiments, parsing the output to get performance numbers.

Transcription of experiment script output

$ python -m experiments spectrum experiments.json
• [infrastructure] Environment(instance_type='c5.4xlarge', client_machines=1, worker_machines=2)
ℹ [infrastructure] no changes to apply
✔ [infrastructure] connected (SSH)
✔ [infrastructure] etcd healthy

• Experiment(clients=50, channels=3, message_size=10240, instance_type='c5.4xlarge', clients_per_machine=50, workers_per_machine=1, worker_machines_per_group=1, protocol=Symmetric(security=16))
✔ [experiment] 5375 queries in 1232ms => 4362 qps

• [infrastructure] Environment(instance_type='c5.4xlarge', client_machines=2, worker_machines=2)
✔ [infrastructure] found changes to apply:
  • aws_instance.client[1] will be created
✔ [infrastructure] created
✔ [infrastructure] connected (SSH)
✔ [infrastructure] etcd healthy

• Experiment(clients=100, channels=3, message_size=10240, instance_type='c5.4xlarge', clients_per_machine=50, workers_per_machine=1, worker_machines_per_group=1, protocol=Symmetric(security=16))
ℹ Got Ctrl+C; retrying (do it again to quit everything).
ℹ Got ^C multiple times; exiting.