  Secure multi-party computation (MPC) is a broad cryptographic concept that
can be adopted for privacy-preserving computation. With MPC, a number of
parties can collaboratively compute a function, without revealing the actual
input or output of the plaintext to others. The applications of MPC range from
privacy-preserving voting, arithmetic calculation, and large-scale data
analysis. From the system perspective, each party in MPC can run on one compute
node. The compute nodes of multiple parties could be either homogeneous or
heterogeneous; however, the distributed workloads from the MPC protocols tend
to be always homogeneous (symmetric). In this work, we study a representative
MPC framework and a set of MPC applications from the system performance
perspective. We show the detailed online computation workflow of a
state-of-the-art MPC protocol and analyze the root cause of its stall time and
performance bottleneck on homogeneous and heterogeneous compute nodes.
