The HCORE Library

The HCORE library provides BLAS operations for matrices in low rank format, i.e., A=UV. HCORE is part of the HiCMA library (https://github.com/ecrc/hicma).

Features of HCORE 0.1.1

• Matrix Compression
• Matrix-Matrix Multiplication (HCORE_GEMM)
• Symmetric Rank-K update (HCORE_SYRK)
• Double Precision
• Complex Number Support
• Testing Suite

Current Research

• Hardware Accelerators
• Support for Multiple Precisions
• Autotuning: Tile Size, Fixed Accuracy and Fixed Ranks
• Support for OpenMP
• Support for HODLR, H, HSS and H2

External Dependencies

HCORE depends on the following libraries:

• BLAS
• LAPACK

Installation

Please see INSTALL.md for information about installing and testing.

References

1. K. Akbudak, H. Ltaief, A. Mikhalev, and D. E. Keyes, Tile Low Rank Cholesky Factorization for Climate/Weather Modeling Applications on Manycore Architectures, International Supercomputing Conference (ISC17), June 18-22, 2017, Frankfurt, Germany.

2. K. Akbudak, H. Ltaief, A. Mikhalev, A. Charara, and D. E. Keyes, Exploiting Data Sparsity for Large-Scale Matrix Computations, Euro-Par 2018, August 27-31, 2018, Turin, Italy.

3. Q. Cao, Y. Pei, T. Herault, K. Akbudak, A. Mikhalev, G. Bosilca, H. Ltaief, D. E. Keyes, and J. Dongarra, Performance Analysis of Tile Low-Rank Cholesky Factorization Using PaRSEC Instrumentation Tools, IEEE/ACM International Workshop on Programming and Performance Visualization Tools (ProTools), Denver, CO, USA, 2019, pp. 25-32.

4. Q. Cao, Y. Pei, K. Akbudak, A. Mikhalev, G. Bosilca, H. Ltaief, D. E. Keyes, and J. Dongarra, Extreme-Scale Task-Based Cholesky Factorization Toward Climate and Weather Prediction Applications, The Platform for Advanced Scientific Computing (PASC 2020).

5. N. Al-Harthi, R. Alomairy, K. Akbudak, R. Chen, H. Ltaief, H. Bagci, and D. E. Keyes, Solving Acoustic Boundary Integral Equations Using High Performance Tile Low-Rank LU Factorization, International Supercomputing Conference (ISC 2020).

6. Q. Cao, Y. Pei, K. Akbudak, G. Bosilca, H. Ltaief, D. E. Keyes, and J. Dongarra, Leveraging PaRSEC Runtime Support to Tackle Challenging 3D Data-Sparse Matrix Problems, IEEE International Parallel & Distributed Processing Symposium (IPDPS 2021).