ACM Transactions on Programming Languages and Systems (TOPLAS)
Parallel discrete event simulation
Communications of the ACM - Special issue on simulation
Optimistic Make (Software Design)
IEEE Transactions on Computers
Optimistic active messages: a mechanism for scheduling communication with computation
PPOPP '95 Proceedings of the fifth ACM SIGPLAN symposium on Principles and practice of parallel programming
Global Virtual Time and distributed synchronization
PADS '95 Proceedings of the ninth workshop on Parallel and distributed simulation
Dummynet: a simple approach to the evaluation of network protocols
ACM SIGCOMM Computer Communication Review
ACM Transactions on Computer Systems (TOCS)
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
A wait-free algorithm for optimistic programming: HOPE realized
ICDCS '96 Proceedings of the 16th International Conference on Distributed Computing Systems (ICDCS '96)
Mesh generation and optimistic computation on the grid
Performance analysis and grid computing
An integrated experimental environment for distributed systems and networks
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Proceedings of the 19th Workshop on Principles of Advanced and Distributed Simulation
Cluster scheduling for explicitly-speculative tasks
Cluster scheduling for explicitly-speculative tasks
Hi-index | 0.00 |
This paper explores the use of optimistic computation to improve application performance in wide-area distributed environments. We do so by defining a parametric model of optimistic computation and then running sets of parameterized experiments to show where, and to what degree, optimistic computation can produce speed-ups. The model is instantiated as an optimistic workload generator implemented as a parallel MPI code. Sets of experiments are then run using this code on an EmuLab system where the network topology, bandwidth and latency can be experimentally controlled. Hence, the results we obtain are from a real parallel code running over a real network protocol through emulated network conditions. We show that under favorable conditions, many fold speed-ups are possible, and even under moderate conditions, speed-ups can still be realized. While generally optimism provides the best speed-ups when network latency dominates the processing cycle, we have seen cases (with a 90% probability of success) when latency is only 1/6 of the processing cycle yet produces break-even relative performance and 85% of "local" performance. The ultimate goal is to apply this understanding to real-world grid applications that can use optimism to tolerate higher latencies.