Ben Clifford

Swift: Fast, Reliable, Loosely Coupled Parallel Computation

We present Swift, a system that combines a novel scripting language called SwiftScript with a powerful runtime system based on CoG Karajan, Falkon, and Globus to allow for the concise specification, and reliable and efficient execution, of large loosely coupled computations. Swift adopts and adapts ideas first explored in the GriPhyN virtual data system, improving on that system in many regards. We describe the SwiftScript language and its use of XDTM to describe the logical structure of complex file system structures. We also present the Swift runtime system and its use of CoG Karajan, Falkon, and Globus services to dispatch and manage the execution of many tasks in parallel and grid environments. We describe application experiences and performance experiments that quantify the cost of Swift operations.

Swift: A language for distributed parallel scripting

Scientists, engineers, and statisticians must execute domain-specific application programs many times on large collections of file-based data. This activity requires complex orchestration and data management as data is passed to, from, and among application invocations. Distributed and parallel computing resources can accelerate such processing, but their use further increases programming complexity. The Swift parallel scripting language reduces these complexities by making file system structures accessible via language constructs and by allowing ordinary application programs to be composed into powerful parallel scripts that can efficiently utilize parallel and distributed resources. We present Swifts implicitly parallel and deterministic programming model, which applies external applications to file collections using a functional style that abstracts and simplifies distributed parallel execution.

Toward loosely coupled programming on petascale systems

We have extended the Falkon lightweight task execution framework to make loosely coupled programming on petascale systems a practical and useful programming model. This work studies and measures the performance factors involved in applying this approach to enable the use of petascale systems by a broader user community, and with greater ease. Our work enables the execution of highly parallel computations composed of loosely coupled serial jobs with no modifications to the respective applications. This approach allows a new---and potentially far larger---class of applications to leverage petascale systems, such as the IBM Blue Gene/P supercomputer. We present the challenges of I/O performance encountered in making this model practical, and show results using both microbenchmarks and real applications from two domains: economic energy modeling and molecular dynamics. Our benchmarks show that we can scale up to 160K processor-cores with high efficiency, and can achieve sustained execution rates of thousands of tasks per second.

Parallel Scripting for Applications at the Petascale and Beyond

Scripting accelerates and simplifies the composition of existing codes to form more powerful applications. Parallel scripting extends this technique to allow for the rapid development of highly parallel applications that can run efficiently on platforms ranging from multicore workstations to petascale supercomputers.

Extreme-scale scripting : opportunities for large task-parallel applications on petascale computers.

Parallel scripting is a loosely-coupled programming model in which applications are composed of highly parallel scripts of program invocations that process and exchange data via files. We characterize here the applications that can benefit from parallel scripting on petascale-class machines, describe the mechanisms that make this feasible on such systems, and present results achieved with parallel scripts on currently available petascale computers.

Provenance management in Swift

The Swift parallel scripting language allows for the specification, execution and analysis of large-scale computations in parallel and distributed environments. It incorporates a data model for recording and querying provenance information. In this article we describe these capabilities and evaluate the interoperability with other systems through the use of the Open Provenance Model. We describe Swifts provenance data model and compare it to the Open Provenance Model. We also describe and evaluate activities performed within the Third Provenance Challenge, which consisted of implementing a specific scientific workflow, capturing and recording provenance information of its execution, performing provenance queries, and exchanging provenance information with other systems. Finally, we propose improvements to both the Open Provenance Model and Swifts provenance system.

Provenance management in Swift with implementation details.

The Swift parallel scripting language allows for the specification, execution and analysis of large-scale computations in parallel and distributed environments. It incorporates a data model for recording and querying provenance information. In this article we describe these capabilities and evaluate interoperability with other systems through the use of the Open Provenance Model. We describe Swifts provenance data model and compare it to the Open Provenance Model. We also describe and evaluate activities performed within the Third Provenance Challenge, which consisted of implementing a specific scientific workflow, capturing and recording provenance information of its execution, performing provenance queries, and exchanging provenance information with other systems. Finally, we propose improvements to both the Open Provenance Model and Swifts provenance system.

Experiences of On-Demand Execution for Large Scale Parameter Sweep Applications on OSG by Swift

Large scale parameter sweep application (PSA) is one of the main grid applications, which may have different characteristics and demands. In this paper, we describe how to use swift to enable the on-demand execution of large scale PSA on open science grid (OSG). The basic on-demand concept means providing appropriate grid resources for the application, which is decided by the characteristics and demands of the application. So we can get high reliability, efficiency, and scalability for large scale independent PSA jobs on OSG. The main on-demand policies include: trust based site selection and pre-selection; scheduling policy on-demand configuration; clustering for small jobs; adaptive execution and automatic data staging; divide and conquer for the scalability. Some usage examples of swift for executing large scale PSA are presented, such as dock, blast. The experimental results for the performance of different policies are presented, with a benchmarking workload size of 10,000 jobs.