libmcl-sys 0.1.1

This system crate provides Rust language bindings to the Minos Compute Library (MCL)
Documentation

libmcl-sys

This system crate provides Rust language bindings (via the use of Bindgen) for Minos Computing Library (MCL).

Build requirements

  • Rust
  • Cargo
  • Clang
  • OpenCL
  • MCL

How to build (Tested on Linux)

  1. Set the MCL_PATH environmental variable with the path to MCL installation directory. For example in bash use:

    export MCL_PATH=/path/to/mcl/install/

    Cargo will try to find the lib/ and include/ directories based on that.

  2. Set the OCL_PATH_INC and OCL_PATH_LIB environmental variables with the path to OpenCL include and lib (or lib64) directories respectively.

    Note: this may not be needed if OpenCL is in the system directories

  3. Build using Cargo

    cargo build --release

    This should produce an .rlib file in target/release/ directory.

How to test

libmcl-sys comes with a set of unit tests that can be executed by running:

 cargo test --release

Reminder: The MCL scheduler should be running when executing the tests.

STATUS

MCL (and libmcl-sys) is a research prototype and still under development, thus not all intended features are yet implemented.

CONTACTS

Please, contact Roberto Gioiosa at PNNL (roberto.gioiosa@pnnl.gov) if you have any MCL questions. For Rust related questions please contact Ryan Friese at PNNL (ryan.friese@pnnl.gov)

MCL-Rust Team

Roberto Gioiosa
Ryan Friese
Polykarpos Thomadakis

LICENCSE

This project is licensed under the BSD License - see the LICENSE file for details.

REFERENCES

IF you wish to cite MCL, please, use the following reference:

  • Roberto Gioiosa, Burcu O. Mutlu, Seyong Lee, Jeffrey S. Vetter, Giulio Picierro, and Marco Cesati. 2020. The Minos Computing Library: efficient parallel programming for extremely heterogeneous systems. In Proceedings of the 13th Annual Workshop on General Purpose Processing using Graphics Processing Unit (GPGPU '20). Association for Computing Machinery, New York, NY, USA, 1–10. DOI:https://doi.org/10.1145/3366428.3380770

Other work that leverage or describe additional MCL features:

  • A. V. Kamatar, R. D. Friese and R. Gioiosa, "Locality-Aware Scheduling for Scalable Heterogeneous Environments," 2020 IEEE/ACM International Workshop on Runtime and Operating Systems for Supercomputers (ROSS), 2020, pp. 50-58, doi:10.1109/ROSS51935.2020.00011.
  • Rizwan Ashraf and Roberto Gioiosa, "Exploring the Use of Novel Spatial Accelerators in Scientific Applications" 2020 ACM/SPEC International Conference on Performance Engineering (ICPE), 2022.