Exploring large macromolecular functional motions on clusters of multicore processors

López‐Blanco, José Ramón; Reyes, Ruyman; Aliaga, José I.; Badía, Rosa M.; Chacón, Pablo; Quintana–Ort́ı, Enrique S.

doi:10.1016/j.jcp.2013.03.032

Cited by 16 publications

(21 citation statements)

References 29 publications

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“…Other calculations benefit from fast algorithms for the reverse transformation, which converts from internal to Cartesian coordinates. Examples include protein structure refinement (improving the quality of experimentally estimated protein structures using modeling) and understanding large changes in protein structure [3] [4]. In these types of applications, internal coordinates offer advantages because the relevant conformational changes involve primarily dihedral angles (Fig.…”

Section: Introductionmentioning

confidence: 99%

GPU implementation of reverse coordinate conversion for proteins

Bayati

Bardhan

Leeser

2015

2015 IEEE High Performance Extreme Computing Conference (HPEC)

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We describe the first parallel algorithm to obtain Cartesian coordinates of a molecular structure from internal coordinates. This algorithm is important for problems such as protein engineering and fitting protein structures to experimental data. Proteins contain thousands to millions of atoms. Their positions can be represented using one of two methods: Cartesian or internal coordinates (bond lengths, angles, etc.). In molecular dynamics and modeling of proteins in different conformational states, it is often necessary to transform one coordinate system to another. In addition, since protein structures change over time, any computation must be done over successive time frames, increasing the computational load. To lessen this computational load we have applied different parallel techniques to the coordinate conversion problem. Converting Cartesian coordinates to internal ones is easily parallelized, and we have previously reported a GPU implementation. On the other hand, the reverse computation has inherent linear dependency because bond lengths and angles are relative to neighboring atoms. Existing implementations walk over a protein structure in a sequential fashion. This paper presents the first parallel implementation of internal to Cartesian coordinates, in which substructures of the protein backbone are converted into their own local Cartesian coordinate spaces, and then combined using a reduction technique to find global Cartesian coordinates. We observe one order of magnitude speedup using parallel processing on a GPU.

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Section: Introductionmentioning

confidence: 99%

GPU implementation of reverse coordinate conversion for proteins

Bayati

Bardhan

Leeser

2015

2015 IEEE High Performance Extreme Computing Conference (HPEC)

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“…While there exist other related work [1], [11], [10], our paper makes the following original contributions:…”

Section: Introductionmentioning

confidence: 99%

“…-As an alternative algorithm, we revisit an implementation of the two-stage reduction to tridiagonal form [6], where the first stage is also an OOC-GPU code while the subsequent stage operates on a much reduced compact matrix that fits in-core. -We perform a comparison of these two approaches using several datasets representative of large-scale macromolecular complexes [10].…”

Section: Introductionmentioning

confidence: 99%

Out-of-Core Solution of Eigenproblems for Macromolecular Simulations

Aliaga

Davidović

Quintana–Ort́ı

2014

Parallel Processing and Applied Mathematics

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Abstract. We consider the solution of large-scale eigenvalue problems that appear in the motion simulation of complex macromolecules on desktop platforms. To tackle the dimension of the matrices that are involved in these problems, we formulate out-of-core (OOC) variants of the two selected eigensolvers, that basically decouple the performance of the solver from the storage capacity. Furthermore, we contend with the high computational complexity of the solvers by off-loading the arithmeticallyintensive parts of the algorithms to a hardware graphics accelerator.

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“…• We compare the two OOC-GPU approaches with the OOC Krylov subspace-based algorithm using several datasets representative of much larger macromolecular complexes [15].…”

Section: Introductionmentioning

confidence: 99%

“…We revisited an implementation of the two-stage reduction to tridiagonal form [15], where the first stage is also an OOC-GPU code while the subsequent stage operates on a much reduced compact matrix that fits in-core in the GPU memory. We compared these two OOC-GPU approaches using relatively large macromolecules [11].…”

Section: Introductionmentioning

confidence: 99%

Out‐of‐core macromolecular simulations on multithreaded architectures

Aliaga

Badía

Dolz

et al. 2014

Concurrency and Computation

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SUMMARYWe address the solution of large-scale eigenvalue problems that appear in the motion simulation of complex macromolecules on multithreaded platforms, consisting of (one or more) multicore processors and possibly a graphics processor (GPU). In particular, we compare specialized implementations of three high-performance eigensolvers that rely on disk storage and out-of-core (OOC) techniques to tackle the large memory requirements of these biological problems, which in general do not fit into the main memory of current desktop machines. Two of the OOC eigensolvers are composed of compute-bounded operations and we enhance their performance by leveraging hybrid CPU-GPU routines that off-load the arithmetically-intensive parts of the algorithms to a GPU accelerator. The third OOC eigesolver is a memory-bounded algorithm, which strongly constrains its performance when the data is on disk. However, this eigensolver exhibits a much lower arithmetic cost compared with its compute-bounded alternatives for this particular application. Experimental results on a desktop platform with two Intel Xeon multicore processors and an NVIDIA "Fermi" GPU, representative of current server technology, illustrate the potential of these methods to address the simulation of biological activity.

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Exploring large macromolecular functional motions on clusters of multicore processors

Cited by 16 publications

References 29 publications

GPU implementation of reverse coordinate conversion for proteins

GPU implementation of reverse coordinate conversion for proteins

Out-of-Core Solution of Eigenproblems for Macromolecular Simulations

Out‐of‐core macromolecular simulations on multithreaded architectures

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