GPCG is an algorithm within the Toolkit for Advanced Optimization (TAO) for solving bound constrained, convex quadratic problems. Originally developed by More' and Toraldo, this algorithm was designed for large-scale problems but had been implemented only for a single processor. The TAO implementation is available for a wide range of high-performance architecture, and has been tested on up to 64 processors to solve problems with over 2.5 million variables.
@article{arxiv.cs/0101018,
title = {GPCG: A Case Study in the Performance and Scalability of Optimization Algorithms},
author = {Steven J. Benson and Lois Curfman McInnes and Jorge J. Moré},
journal= {arXiv preprint arXiv:cs/0101018},
year = {2007}
}