Design and Evaluation of a Collective IO Model for Loosely Coupled Petascale Programming

Loosely coupled programming is a powerful paradigm for rapidly creating higher-level applications from scientific programs on petascale systems, typically using scripting languages. This paradigm is a form of many-task computing (MTC) which focuses on the passing of data between programs as ordinary files rather than messages. While it has the significant benefits of decoupling producer and consumer and allowing existing application programs to be executed in parallel with no recoding, its typical implementation using shared file systems places a high performance burden on the overall system and on the user who will analyze and consume the downstream data. Previous efforts have achieved great speedups with loosely coupled programs, but have done so with careful manual tuning of all shared file system access. In this work, we evaluate a prototype collective IO model for file-based MTC. The model enables efficient and easy distribution of input data files to computing nodes and gathering of output results from them. It eliminates the need for such manual tuning and makes the programming of large-scale clusters using a loosely coupled model easier. Our approach, inspired by in-memory approaches to collective operations for parallel programming, builds on fast local file systems to provide high-speed local file caches for parallel scripts, uses a broadcast approach to handle distribution of common input data, and uses efficient scatter/gather and caching techniques for input and output. We describe the design of the prototype model, its implementation on the Blue Gene/P supercomputer, and present preliminary measurements of its performance on synthetic benchmarks and on a large-scale molecular dynamics application.Comment: IEEE Many-Task Computing on Grids and Supercomputers (MTAGS08) 200

Agentless robust load sharing strategy for utilising hetero-geneous resources over wide area network

Resource monitoring and performance prediction services have always been regarded as important keys to improving the performance of load sharing strategy. However, the traditional methodologies usually require specific performance information, which can only be collected by installing proprietary agents on all participating resources. This requirement of implementing a single unified monitoring service may not be feasible because of the differences in the underlying systems and organisation policies. To address this problem, we define a new load sharing strategy which bases the load decision on a simple performance estimation that can be measured easily at the coordinator node. Our proposed strategy relies on a stage-based dynamic task allocation to handle the imprecision of our performance estimation and to correct load distribution on-the-fly. The simulation results showed that the performance of our strategy is comparable or better than traditional strategies, especially when the performance information from the monitoring service is not accurate