High Productivity Computing Systems (HPCS) Library Study Effort; Final technical repts. Aug 2006-Aug 2007

摘要

The research team explores a rich feature set, large algorithmic variety, and detailed implementation considerations for one of the most fundamental computational kernels of computational science: LU factorization of a dense matrix by Gaussian elimination with partial pivoting. For the target implementation platforms and systems, they analyze and compare established shared and distributed memory environments as well as relatively new Partitioned Global Address Space programming languages, which include those coming from the High Productivity Computing Systems (HPCS) project. To give quantitative measures of each hardware platform metrics, combined with implementation characteristics, they compare scalability, raw and relative performance as well as the source code features, functionality, and absolute size breakdown as measured by Source Lines of Code (SLOC).