Process-in-Process: Techniques for Practical Address-Space Sharing

A Hori and M Si and B Gerofi and M Takagi and J Dayal and P Balaji and Y Ishikawa, HPDC '18: PROCEEDINGS OF THE 27TH INTERNATIONAL SYMPOSIUM ON HIGH-PERFORMANCE PARALLEL AND DISTRIBUTED COMPUTING, 131-143 (2018).

DOI: 10.1145/3208040.3208045

The two most common parallel execution models for many-core CPUs today are multiprocess (e.g., MPI) and multithread (e.g., OpenMP). The multiprocess model allows each process to own a private address space, although processes can explicitly allocate shared-memory regions. The multithreaded model shares all address space by default, although threads can explicitly move data to thread-private storage. In this paper, we present a third model called process-in-process (PiP), where multiple processes are mapped into a single virtual address space. Thus, each process still owns its process-private storage (like the multiprocess model) but can directly access the private storage of other processes in the same virtual address space (like the multithread model). The idea of address-space sharing between multiple processes itself is not new. What makes PiP unique, however, is that its design is completely in user space, making it a portable and practical approach for large supercomputing systems where porting existing OS-based techniques might be hard. The PiP library is compact and is designed for integrating with other runtime systems such as MPI and OpenMP as a portable low-level support for boosting communication performance in HPC applications. We showcase the uniqueness of the PiP environment through both a variety of parallel runtime optimizations and direct use in a data analysis application. We evaluate PiP on several platforms including two high-ranking supercomputers, and we measure and analyze the performance of PiP by using a variety of micro-and macro-kernels, a proxy application as well as a data analysis application.

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