Ken Kennedy
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Ken Kennedy has a total of 27 co-authors. Here are the top co-authors by number of shared publications:
A Chien
University of California at San Diego
1 shared publicationA. Dasgupta
Rice University
1 shared publicationA. Olugbile
University of California at San Diego
1 shared publicationA. YarKhan
University of Tennessee
1 shared publication- Anirban Mandal
Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
1 shared publication B. Liu
University of Houston
1 shared publication
Latest Publications
- 16 November 2007
- Published by Springer Nature
- p. 19-29
Why Performance Models Matter for Grid Computing
- 16 November 2007
- Published by Springer Nature
- p. 19-29
Why Performance Models Matter for Grid Computing
- 1 June 2005
- Published by Springer Nature in International Journal of Parallel Programming
- Vol. 33 (2-3) , 209-229
New Grid Scheduling and Rescheduling Methods in the GrADS Project
- 1 June 2005
- Published by Springer Nature in International Journal of Parallel Programming
- Vol. 33 (2-3) , 209-229
New Grid Scheduling and Rescheduling Methods in the GrADS Project
- 1 January 2005
- Published by Springer Nature in The Journal of Supercomputing
- Vol. 31 (1) , 5-46
Scalarization Using Loop Alignment and Loop Skewing
- 1 January 2005
- Published by Springer Nature in The Journal of Supercomputing
- Vol. 31 (1) , 5-46
Scalarization Using Loop Alignment and Loop Skewing
- Open Access
- 26 January 1996
- Published by Wiley in Scientific Programming
- Vol. 6 (1) , 153-158
Experiences in Data‐Parallel Programming
To efficiently parallelize a scientific application with a data‐parallel compiler requires certain structural properties in the source program, and conversely, the absence of others. A recent parallelization effort of ours reinforced this observation and motivated this correspondence. Specifically, we have transformed a Fortran 77 version of GROMOS, a popular dusty‐deck program for molecular dynamics, into Fortran D, a data‐parallel dialect of Fortran. During this transformation we have encountered a number of difficulties that probably are neither limited to this particular application nor do they seem likely to be addressed by improved compiler technology in the near future. Our experience with GROMOS suggests a number of points to keep in mind when developing software that may at some time in its life cycle be parallelized with a data‐parallel compiler. This note presents some guidelines for engineering data‐parallel applications that are compatible with Fortran D or High Performance Fortran compilers.
- 26 January 1996
- Published by Wiley in Scientific Programming
- Vol. 6 (1) , 153-158
Open AccessExperiences in Data‐Parallel Programming
To efficiently parallelize a scientific application with a data‐parallel compiler requires certain structural properties in the source program, and conversely, the absence of others. A recent parallelization effort of ours reinforced this observation and motivated this correspondence. Specifically, we have transformed a Fortran 77 version of GROMOS, a popular dusty‐deck program for molecular dynamics, into Fortran D, a data‐parallel dialect of Fortran. During this transformation we have encountered a number of difficulties that probably are neither limited to this particular application nor do they seem likely to be addressed by improved compiler technology in the near future. Our experience with GROMOS suggests a number of points to keep in mind when developing software that may at some time in its life cycle be parallelized with a data‐parallel compiler. This note presents some guidelines for engineering data‐parallel applications that are compatible with Fortran D or High Performance Fortran compilers.