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

Toward understanding I/O behavior in HPC workflows

Scientific discovery increasingly depends on complex workflows consisting of multiple phases and sometimes millions of parallelizable tasks or pipelines. These workflows access storage resources for a variety of purposes, including preprocessing, simulation output, and postprocessing steps. Unfortunately, most workflow models focus on the scheduling and allocation of com- putational resources for tasks while the impact on storage systems remains a secondary objective and an open research question. I/O performance is not usually accounted for in workflow telemetry reported to users. In this paper, we present an approach to augment the I/O efficiency of the individual tasks of workflows by combining workflow description frameworks with system I/O telemetry data. A conceptual architecture and a prototype implementation for HPC data center deployments are introduced. We also identify and discuss challenges that will need to be addressed by workflow management and monitoring systems for HPC in the future. We demonstrate how real-world applications and workflows could benefit from the approach, and we show how the approach helps communicate performance-tuning guidance to users

New technologies to bridge the gap between High Performance Computing (HPC) and Big Data

The unification of HPC and Big Data has received increasing attention in the last years. It is a common belief that exascale computing and Big Data are closely associated since HPC requires processing large-scale data from scientific instruments and simulations. But, at the same time, it was observed that tools and cultures of HPC and Big Data communities differ significantly. One of the most important issues in the path to the convergence is caused by the differences in their software stacks. This thesis will address the research challenge of bridging the gap between Big Data and HPC worlds. With this goal in mind, a set of tools and technologies will be developed and integrated into a new unified Big Data-HPC framework that will allow the execution of scientific multi-language applications on both environments using containers