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 filebased
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 highspeed
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.