Due to the enormous importance of electrostatics in molecular biology, calculating the electrostatic potential and corresponding energies has become a standard computational approach for the study of biomolecules and nano-objects immersed in water and salt phase or other media. However, the electrostatics of large macromolecules and macromolecular complexes, including nano-objects, may not be o...

This paper presents a new parallelization model, called coarse-grained thread pipelining, for exploiting speculative coarse-grained parallelism from general-purpose application programs in shared-memory multiprocessor systems. This parallelization model, which is based on the ne-grained thread pipelining model proposed for the superthreaded architecture 11, 12], allows concurrent execution of l...

Automatic parallelization and reduction of memory requirement for functional programs have been two active research topics. In this paper, we address both issues simultaneously thanks to the polytope model, a framework originally crafted for the parallelization of imperative programs.

We present two new algorithms which perform automatic parallelization via source-to-source transformations. The objective is to exploit goal-level, unrestricted independent and-parallelism. The proposed algorithms use as targets new parallel execution primitives which are simpler and more flexible than the well-known &/2 parallel operator. This makes it possible to generate better parallel expr...

It is well known that a direct parallelization of sequential optimization methods (e.g., coordinate descent and stochastic gradient methods) is often not effective. The reason is that at each iteration, the number of operations may be too small. In this paper, we point out that because of the skewed distribution of non-zero values in real-world data sets, this common understanding may not be tr...

The Fortran D language extends Fortran by permitting the user to specify the distribution of array variables across the processors of a parallel computer system. This information can then be used by the compiler to derive a multidimensional parallelization. For programs in which multidimensional arrays have been linearized for optimal performance on vector processors, this strategy will not pro...

Genetic and simulated annealing algorithms have been used to solve many combinatorial problems. Their results are proven to be efficient in solving such problems. The running time of these techniques is generally less than the run time needed to find the optimal solution. However, in very large-scale problems such as sensor deployment, their running time is extremely slow. In this paper, we int...

The development of an enhanced parallel algorithm for batch pattern training of a multilayer perceptron with the back propagation training algorithm and the research of its efficiency on general-purpose parallel computers are presented in this paper. An algorithmic description of the parallel version of the batch pattern training method is described. Several technical solutions which lead to en...

Portfolio parallelization is an approach that runs several solver instances in parallel and terminates when one of them succeeds in solving the problem. Despite it’s simplicity portfolio parallelization has been shown to perform well for modern mixed-integer programming (MIP) and boolean satisfiability problem (SAT) solvers. Domain propagation has also been shown to be a simple technique in mod...