This is the implementation of the PriML language. PriML is a source language for writing fine-grained parallel programs with asynchronous computation and I/O. It is an extension of Standard ML (and, currently, compiles to Standard ML and uses MLton as a backend).
The following instructions have been tested on Linux x64.
An existing installation of MLton is required to build the compiler. See [https://github.com/MLton/mlton] for other requirements for building MLton from source.
To build, run
make
in the top-level directory.
Note: Compiling with MLton requires a lot of memory and time.
This process will produce the primlc executable. It can be invoked as follows:
./primlc <input> <output>
The output will be a stand-alone Linux executable that can be invoked on N
processors as follows:
./output <command line options> @MLton number-processors <N> --
The input to primlc can be either a single source file with the .prm extension,
or can be a MLton Basis (.mlb) file (see http://www.mlton.org/MLBasis).
PriML programs can freely interface with SML code using the ML Basis system.
A .mlb file input to primlc can provide any number of SML files on which the
PriML program depends, but must end with exactly one .prm file. PriML currently
does not support compilation of multiple .prm files.
Several example programs are provided in the priml-examples directory.
Run make to compile all of them. Individual make targets are also provided for
each example.
A bank simulator (after Babaoğlu, Ö., Marzullo, K. & Schneider, F.B. A formalization of priority inversion. Real-Time Syst 5, 285–303 (1993)) that is used as an example of the utility of partially ordered priorities.
A skeleton of an email client that can display emails, sort emails and send emails (at differing priorities).
Takes integers n on the command line and computes fib(n) in parallel while
responsively accepting more integers.
Computes the 45th Fibonacci numbers while echoing console input.
Opens a simple web server (serving the page in the www directory) on
localhost:8000.
For background information on the theory behind PriML and more code and syntax examples:
My PhD thesis, Responsive Parallel Computation
Stefan K. Muller, Umut A. Acar and Robert Harper. Competitive Parallelism: Getting Your Priorities Right. ICFP 2018.
For algorithmic and implementation details on the runtime scheduler, and a description of the "fairness" properties of PriML:
Stefan K. Muller, Sam Westrick and Umut A. Acar. Fairness in Responsive Parallelism. ICFP 2019.