Processor-In-Memory (PIM) Based Architectures for PetaFlops Potential Massively Parallel Processing

The report summarizes the work performed at the University of Notre Dame under a NASA grant from July 15, 1995 through July 14, 1996. Researchers involved in the work included the PI, Dr. Peter M. Kogge, and three graduate students under his direction in the Computer Science and Engineering Department: Stephen Dartt, Costin Iancu, and Lakshmi Narayanaswany. The organization of this report is as follows. Section 2 is a summary of the problem addressed by this work. Section 3 is a summary of the project's objectives and approach. Section 4 summarizes PIM technology briefly. Section 5 overviews the main results of the work. Section 6 then discusses the importance of the results and future directions. Also attached to this report are copies of several technical reports and publications whose contents directly reflect results developed during this study

Application Performance of Physical System Simulations

Various parallel computer benchmarking projects have been around since early 1990s but the adopted so far approaches for performance analysis require a significant revision in view of the recent developments of both the application domain and the computer technologies. This paper presents a novel performance evaluation methodology based on assessing the processing rate of two orthogonal use cases – dense and sparse physical systems – as well as the energy efficiency for both. Evaluation results with two popular codes — HPL and HPCG — validate our approach and demonstrate its use for analysis and interpretation in order to identify and confirm current technological challenges as well as to track and roadmap the future application performance of physical system simulations

Polygonal path simplification with angle constraints

We present efficient geometric algorithms for simplifying polygonal paths in R2 and R3 that have angle constraints, improving by nearly a linear factor over the graph-theoretic solutions based on known techniques. The algorithms we present match the time bounds for their unconstrained counterparts. As a key step in our solutions, we formulate and solve an off-line ball exclusion search problem, which may be of interest in its own right