1,606 research outputs found
Parallel matrix inversion techniques
In this paper, we present techniques for inverting sparse, symmetric and positive definite matrices on parallel and distributed computers. We propose two algorithms, one for SIMD implementation and the other for MIMD implementation. These algorithms are modified versions of Gaussian elimination and they take into account the sparseness of the matrix. Our algorithms perform better than the general parallel Gaussian elimination algorithm. In order to demonstrate the usefulness of our technique, we implemented the snake problem using our sparse matrix algorithm. Our studies reveal that the proposed sparse matrix inversion algorithm significantly reduces the time taken for obtaining the solution of the snake problem. In this paper, we present the results of our experimental work
Tackling Exascale Software Challenges in Molecular Dynamics Simulations with GROMACS
GROMACS is a widely used package for biomolecular simulation, and over the
last two decades it has evolved from small-scale efficiency to advanced
heterogeneous acceleration and multi-level parallelism targeting some of the
largest supercomputers in the world. Here, we describe some of the ways we have
been able to realize this through the use of parallelization on all levels,
combined with a constant focus on absolute performance. Release 4.6 of GROMACS
uses SIMD acceleration on a wide range of architectures, GPU offloading
acceleration, and both OpenMP and MPI parallelism within and between nodes,
respectively. The recent work on acceleration made it necessary to revisit the
fundamental algorithms of molecular simulation, including the concept of
neighborsearching, and we discuss the present and future challenges we see for
exascale simulation - in particular a very fine-grained task parallelism. We
also discuss the software management, code peer review and continuous
integration testing required for a project of this complexity.Comment: EASC 2014 conference proceedin
On Designing Multicore-aware Simulators for Biological Systems
The stochastic simulation of biological systems is an increasingly popular
technique in bioinformatics. It often is an enlightening technique, which may
however result in being computational expensive. We discuss the main
opportunities to speed it up on multi-core platforms, which pose new challenges
for parallelisation techniques. These opportunities are developed in two
general families of solutions involving both the single simulation and a bulk
of independent simulations (either replicas of derived from parameter sweep).
Proposed solutions are tested on the parallelisation of the CWC simulator
(Calculus of Wrapped Compartments) that is carried out according to proposed
solutions by way of the FastFlow programming framework making possible fast
development and efficient execution on multi-cores.Comment: 19 pages + cover pag
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