Silvia A. Crivelli

A Physical Approach to Protein Structure Prediction

We describe our global optimization method called Stochastic Perturbation with Soft Constraints (SPSC), which uses information from known proteins to predict secondary structure, but not in the tertiary structure predictions or in generating the terms of the physics-based energy function. Our approach is also characterized by the use of an all atom energy function that includes a novel hydrophobic solvation function derived from experiments that shows promising ability for energy discrimination against misfolded structures. We present the results obtained using our SPSC method and energy function for blind prediction in the 4th Critical Assessment of Techniques for Protein Structure Prediction competition, and show that our approach is more effective on targets for which less information from known proteins is available. In fact our SPSC method produced the best prediction for one of the most difficult targets of the competition, a new fold protein of 240 amino acids.

An efficient parallel termination detection algorithm

In this paper, we present a new, easy to implement algorithm for detecting the termination of a parallel asynchronous computation on distributed-memory MIMD computers. We demonstrate that it operates concurrently with the main computation, adding minimal overhead, and we prove that it correctly detects termination when it occurs. Experimental results confirm that the termination detection routine imposes an overhead smaller than the experimental uncertainty.

The PMESC Programming Library for Distributed-Memory MIMD Computers

Efficient programming of task-parallel problems, where the number and execution times of the computational tasks can vary unpredictably, demands an asynchronous and adaptive approach. In this sort of approach, however, such fundamental programming issues as load sharing, data sharing, and termination detection can present difficult programming problems. This paper presents the PMESC library for managing task-parallel problems on distributed-memory MIMD computers within the context of the SPMD (single program, multiple data) programming model. PMESC offers support for all of the application-independent programming issues involved in SPMD task-parallel computation in a portable and efficient way while still allowing users to customize their codes. Because different problems may require different strategies to achieve good performance, PMESC is based on a straightforward model in which different building blocks can be easily put together and changed to accommodate the particular needs of the different applications. The library provides an interface that allows users to program a virtual machine and thereby ignore the details associated with message passing and machine architecture. These features make PMESC accessible to a wide variety of users.

Predicting Protein Tertiary Structure using a Global Optimization Algorithm with Smoothing

We present a global optimization algorithm and demonstrate its effectiveness in solving the protein structure prediction problem for a 70 amino-acid helical protein, the A-chain of uteroglobin. This is a larger protein than solved previously by our global optimization method or most other optimization-based protein structure prediction methods. Our approach combines techniques that “smooth” the potential energy surface being minimized with methods that do a global search in selected subspaces of the problem in addition to locally minimizing in the full parameter space. Neural network predictions of secondary structure are used in the formation of initial structures.

Task Parallelism: What a Tool Can Provide and What Should Be Left to the User

This paper discusses some programming issues involved in the implementation of task parallelism on distributed-memory MIMD computers. In particular, we separate those issues that are application-independent and so can be part of a library from those that should be controlled by the user to maximize the performance.

An introduction to the PMESC parallel programming paradigm and library for task parallel computation

Task-parallel problems are difficult to implement efficiently in parallel because they are asynchronous and unpredictable. The difficulties are compounded on distributedmemory computers where interprocessor communication can impose a substantial overhead. A few languages and libraries have been proposed that are specifically designed to support this kind of computation. However, one big challenge still remains: to make those tools understood and used by scientists. engineers, and others who want to exploit the power of parallel computers without spending much effort in mastering those tools. The PMESC programming paradigm and library presented here are designed to make programming on distributed-memory computers easy to understand and to make efficient parallel code easy to produce. The paradigm provides a methodology for structuring task-parallel problems that allows the separation of different phases in the computation. The library provides support for those phases that are application-independent allowing the users to concentrate on the applicationspecific oues.

The cost of eigenvalue computation on distributed-memory MIMD multiprocessors

Abstract In [20], Simon proves that bisection is not the optimal method for computing an eigenvalue on a single vector processor. In this paper, we show that his analysis does not extend in a straightforward way to the computation of an eigenvalue on a distributed-memory MIMD multiprocessor. In particular, we show how the optimal number of sections (and processors) to use for multisection depends on variables such as the matrix size and the ratio of communication and computation costs. We also show that parallel multisection outperforms the variant of parallel bisection called polysection proposed by Swarztrauber in [22] for this problem on a distributed-memory MIMD multiprocessor. We present the results of experiments on the 64-processor Intel iPSC/2 hypercube and the 512-processor Intel Touchstone Delta mesh multiprocessor.