My interests are in high performance scientific computing, with
particular emphasis on parallel computing. My major projects involve
developing the message passing inteface (MPI) and hierarchical
numerical methods for the numerical solution of partial differential
One of the biggest challenges in parallel computing for scientific
computing is efficiently and correctly expressing parallel programs.
My work in this area has three main directions:
Each of these is described in more detail below.
- Developing standards for parallel computing that can be
efficiently and widely implemented. This work has focused on the
Message Passing Interface (MPI) standard and a freely-available
implementation of the MPI standard, MPICH.
- Developing tools to understand and improve the performance and
correctness of parallel programs.
- Innovative methods for parallelism that will match radical
changes in computer architecture.
is a Petascale computing system, funded by that National Science
Foundation and installed at NCSA.
The Message Passing Interface (MPI) is a standard for parallel
computing developed by the high performance computing community. The
standard is available at the MPI
Forum web site. I was involved in the development of the
MPI-1, MPI-2, and MPI-3 standards, and I currently edit and maintain
the sources for the official document.
For a standard to succeed, there must be an effective implementation
of the standard. For this reason, our group developed a portable,
efficient implementation of the MPI standard called MPICH.
Along with my collaborators at Argonne, I
continue to use this project to perform research into implementation
issues for MPI, such as how to make best use of emerging
Achieving high performance with I/O in parallel applications requires careful
attention to the choice of semantics. MPI-IO defines parallel file semantics
that permit a highly efficient implementation. The key here is
permits - both new implementation ideas and careful engineering is required
to achieve good performance.
Performance Modeling, Visualization, and Tuning
The quantitative study of performance is critical in developing new
algorithms, understanding new architectural directions for processors
and interconnects, and getting the most out of applications.
Some introductory papers on performance modeling
New Execution Models
Execution Paradigm is an NSF-funded project that looks at new ways
to organize I/O on high-end computing (HEC) systems.
FPMPI is a tool for collecting
data on the performance
of MPI programs. FPMPI collects detailed summary data about MPI
usage, along with some information about the other resources used by a
parallel program. FPMPI tries to collect just the right amount of
data: rather than collect information either on each separate call
(too much data) or combining all calls to a particular routine (too
little data), FPMPI collects enough data to understand the overall
communication pattern, including the different contributions by short
and long message communication. FPMPI complements the abilities of
Jumpshot, a portable
visualization tool that alas is no longer being developed or maintained.
PETSc is a suite of data
structures and routines for the scalable (parallel) solution of
scientific applications modeled by partial differential equations.