Computer
Unifying and Optimizing Parallel Linear Algebra Algorithms
IEEE Transactions on Parallel and Distributed Systems
An overview of message passing environments
Parallel Computing - Special issue: message passing interfaces
Non-uniform and dynamic domain decompositions for hypercomputing
Parallel Computing
In search of clusters (2nd ed.)
In search of clusters (2nd ed.)
Adaptive load migration systems for PVM
Proceedings of the 1994 ACM/IEEE conference on Supercomputing
Adaptive Parallelism and Piranha
Computer
A Case for NOW (Networks of Workstations)
IEEE Micro
HyperParallel Technologies: HyperC: Portable Parallel Programming in C
Proceedings of the 8th International Symposium on Parallel Processing
Dynamic load balancing of distributed SPMD computations with explicit message-passing
HCW '97 Proceedings of the 6th Heterogeneous Computing Workshop (HCW '97)
MPI: A Message-Passing Interface Standard
MPI: A Message-Passing Interface Standard
Predicting the cost and benefit of adapting data parallel applications in clusters
Journal of Parallel and Distributed Computing
Developing a Data-Parallel Application with DaParT
PPAM '01 Proceedings of the th International Conference on Parallel Processing and Applied Mathematics-Revised Papers
A Parallel Algorithm for the Simulation of the Dynamic Behaviour of Liquid-Liquid Agitated Columns
VECPAR '00 Selected Papers and Invited Talks from the 4th International Conference on Vector and Parallel Processing
Supporting Self-Adaptivity for SPMD Message-Passing Applications
LCR '98 Selected Papers from the 4th International Workshop on Languages, Compilers, and Run-Time Systems for Scalable Computers
Developing SPMD applications with load balancing
Parallel Computing
Hi-index | 0.00 |
DAME (Data Migration Environment) uses transparent supports to overcome inefficiencies in data-parallel programming. These supports hide irregular network topology, dynamically adapt the data distribution to platform conditions, and mask the consequent nonuniform distribution to the programmer. The authors compare DAME's performance with that of some popular frameworks. They begin by discussing DAME's three main design goals: efficiency, transparency, and scalability. Next, they describe the five supports that DAME gives the programmer: virtual topology, data distribution, data management, interprocess communication, and workload reconfiguration. Then, they present the results they obtained from experiments using 10 workstations that provide a hardware-heterogeneous, data-homogeneous, nonuniform platform. The results show that DAME provides a virtual Single-Program, Multiple-Data machine that overcomes most of the differences that distinguish a parallel virtual machine from an ideal SPMD machine.