We address the problem of data parallel processing for computational quantum chemistry (CQC). CQC is a computationally demanding tool to study the electronic structure of molecules. An important algorithmic component of these computations is the evaluation of Electron Repulsion Integrals (ERIs). A key problem with ERI evaluation is controlflow variation between different ERI evaluations, which can only be resolved at runtime. This causes the computation to be unsuitable for data parallel execution. However, it is observed that although there is variation between ERI evaluations, the variation is limited; in fact there are a limited number of ERI classes present within any given workload. Conceptually, it is possible to classify the ERIs into sizable sets, and execute these sets in a data parallel fashion. Practically, creating these sets is computationally expensive. We describe an architecture to perform this thread sorting, where high throughput is achieved with small associative and multiport memories. The performance of the prototype is evaluated with FPGA synthesis. We go on to envision other uses for thread sorting, in general-purpose manycore architectures.

Recent advances in grid infrastructure and middleware development have enabled various types of applications in science and engineering to be deployed on the grid. The characteristics of these applications and the diverse infrastructure and middleware solutions developed, utilized or adapted by PRAGMA member institutes are summarized. The applications include those for climate modeling, computational chemistry, bioinformatics and computational genomics, remote control of instruments, and distributed databases. Many of the applications are deployed to the PRAGMA grid testbed in routine basis experiments. Strategies for deploying applications without modifications, and those taking advantage of new programming models on the grid are explored and valuable lessons learned are reported. Comprehensive end to end solutions from PRAGMA member institutes that provide important grid middleware components and generalized models of integrating applications and instruments on the grid are also described.

Many important and fundamental questions in biology and biochemistry can be better understood through investigations performed at the protein-ligand or drugreceptor level. A variety of techniques have been used over the years, and it is an area of active research. In this paper we illustrate an approach that leverages a number of different computational chemistry approaches, and combines these with non-linear optimization algorithms and grid based high performance computing platforms. The result is a very flexible, high performance method of evaluating protein-ligand interaction algorithms. We illustrate the approach by evaluating a hybrid molecular modeling and quantum theoretical based algorithm.

The grid-based infrastructure enables large-scale scientific applications to be run on distributed resources and coupled in innovative ways. However, in practice, grid resources are not very easy to use for the end-users who have to learn how to generate security credentials, stage inputs and outputs, access grid-based schedulers, and install complex client software. There is an imminent need to provide transparent access to these resources so that the end-users are shielded from the complicated details, and free to concentrate on their domain science. Scientific applications wrapped as Web services alleviate some of these problems by hiding the complexities of the back-end security and computational infrastructure, only exposing a simple SOAP API that can be accessed programmatically by application-specific user interfaces. However, writing the application services that access grid resources can be quite complicated, especially if it has to be replicated for every application. In this paper, we present Opal which is a toolkit for wrapping scientific applications as Web services in a matter of hours, providing features such as scheduling, standards-based grid security and data management in an easy-to-use and configurable manner

Evolving technologies, as exemplified by computational grids and Web services, have made it possible to solve new scientific problems that would not have been feasible previously. In order to make such advances available to the community in general and to be able to solve new problems, not necessarily from the same discipline, it is imperative to build tools that provide a common user interface in order that application programmers and users do not have to be concerned with particulars of Web services and their underlying code, computational platforms, or with data file formats. We will describe our efforts in creating a computational chemistry environment that encompasses a general scientific workflow environment, a domain specific example for quantum chemistry, our ongoing design of a workflow user interface, and our efforts at database integration.

High performance computing, storage, visualization, and database infrastructures are increasing geometrically in complexity as scientists move towards grid-based computing. While this is natural, it has the effect of pushing computational capabilities beyond the reach of scientists because of the time needed to harness the infrastructure. Hiding the complexity of networked resources becomes essential if scientists are to utilize them effectively. In this work, we describe our efforts to integrate various computational chemistry components into a scientific computing environment. We briefly describe improvements we have made to individual components of the chemistry environment as well as future directions, followed by a more in-depth discussion of our strategy for integration into a grid workflow environment based on web services, which enables access to remote resources while shielding users from the complexities of the grid infrastructures. A preliminary schema for storing data obtained from computational chemistry calculations is also described.