Überarb. Diss., 1984. / Based on the author's thesis, Copenhagen, 1984. Bibliogr.: p. [137]-139. Index.

Saarbrücken, Univ., Diss., 1996. / Computerdatei im Fernzugriff.

University, Diss., 1989--Paderborn.

Modern Graphics Processing Units (GPUs) are providing breakthrough performance for numerical computing at the cost of increased programming complexity. Current programming models for GPUs require that the programmer manually manage the data transfer between CPU and GPU. This thesis proposes a simpler programming model and introduces a new compilation framework to enable Python applications containing numerical computations to be executed on GPUs and multi-core CPUs. The new programming model minimally extends Python to include type and parallel-loop annotations. Our compiler framework then automatically identifies the data to be transferred between the main memory and the GPU for a particular class of affine array accesses. The compiler also automatically performs loop transformations to improve performance on GPUs. For kernels with regular loop structure and simple memory access patterns, the GPU code generated by the compiler achieves significant performance improvement over multi-core CPU codes.

No description available.

University, Diss., 2005--Linz.

Stuttgart, Univ., Fakultät Informatik, Studienarb., 1998.

Chemnitz, Techn. Univ., Studienarb., 1998.

This paper presents an approach to compiler correctness in which the compiler generates a proof that the transformed program correctly implements the input program. A simple proof checker can then verify that the program was compiled correctly. We call a compiler that produces such proofs a credible compiler, because it produces verifiable evidence that it is operating correctly. / Singapore-MIT Alliance (SMA)

Previously, compiler transformations have primarily focused on minimizing program execution time. This thesis explores some examples of applying compiler technology outside of its original scope. Specifically, we apply compiler analysis to the field of software maintenance and evolution by examining the use of global data throughout the lifetimes of many open source projects. Also, we investigate the effects of compiler optimizations on the power consumption of small battery powered devices. Finally, in an area closer to traditional compiler research we examine automatic program parallelization in the form of thread-level speculation.