Parallel GPGPU Evaluation of Small Angle X-Ray Scattering Profiles in a Markov Chain Monte Carlo Framework

Antonov, Lubomir D.; Andreetta, Christian; Hamelryck, Thomas

doi:10.1007/978-3-642-38256-7_15

Cited by 4 publications

(6 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This slight decrease was due to hyper-threading, which dropped the efficiency of thread execution when multiple threads were assigned to a single physical core. A similar behavior can be found in other memory-intensive applications [15]- [17].…”

Section: Fig 10supporting

confidence: 80%

See 1 more Smart Citation

Accelerating the Held-Karp Algorithm for the Symmetric Traveling Salesman Problem

Kimura

Higa

Okita

et al. 2019

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

In this paper, we propose an acceleration method for the Held-Karp algorithm that solves the symmetric traveling salesman problem by dynamic programming. The proposed method achieves acceleration with two techniques. First, we locate data-independent subproblems so that the subproblems can be solved in parallel. Second, we reduce the number of subproblems by a meet in the middle (MITM) technique, which computes the optimal path from both clockwise and counterclockwise directions. We show theoretical analysis on the impact of MITM in terms of the time and space complexities. In experiments, we compared the proposed method with a previous method running on a single-core CPU. Experimental results show that the proposed method on an 8-core CPU was 9.5-10.5 times faster than the previous method on a single-core CPU. Moreover, the proposed method on a graphics processing unit (GPU) was 30-40 times faster than that on an 8-core CPU. As a side effect, the proposed method reduced the memory usage by 48%. key words: symmetric traveling salesman problem, Held-Karp algorithm, parallelization, meet in the middle, GPU

show abstract

Section: Fig 10supporting

confidence: 80%

“…Therefore, similar to T bstr (n) and T offset (n), T ′ 2 (n) is asymptotically negligible in comparison to T 1 in Eq. (17). In summary, MITM is expected to double the performance according to Theorem 3.…”

Section: Meet In the Middle (Mitm) Techniquementioning

confidence: 85%

Accelerating the Held-Karp Algorithm for the Symmetric Traveling Salesman Problem

Kimura

Higa

Okita

et al. 2019

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

show abstract

“…In 2013, Antonov et al . showed how to use GPU acceleration to evaluate SAXS profiles in a Markov Chain Monte Carlo framework . From a protein structure created in silico , they reconstructed the SAXS profile using the Debye formula and GPU Acceleration.…”

Section: Methodsmentioning

confidence: 99%

BCL::SAXS: GPU accelerated Debye method for computation of small angle X-ray scattering profiles

et al. 2015

View full text Add to dashboard Cite

Small angle X-ray scattering (SAXS) is an experimental technique used for structural characterization of macromolecules in solution. Here, we introduce BCL::SAXS – an algorithm designed to replicate SAXS profiles from rigid protein models at different levels of detail. We first show our derivation of BCL::SAXS and compare our results with the experimental scattering profile of Hen Egg White Lysozyme. Using this protein we show how to generate SAXS profiles representing: 1) complete models, 2) models with approximated side chain coordinates, and 3) models with approximated side chain and loop region coordinates. We evaluated the ability of SAXS profiles to identify a correct protein topology from a non-redundant benchmark set of proteins. We find that complete SAXS profiles can be used to identify the correct protein by receiver operating characteristic (ROC) analysis with an area under the curve (AUC) > 99%. We show how our approximation of loop coordinates between secondary structure elements improves protein recognition by SAXS for protein models without loop regions and side chains. Agreement with SAXS data is a necessary but not sufficient condition for structure determination. We conclude that experimental SAXS data can be used as a filter to exclude protein models with large structural differences from the native.

show abstract

“…This idea is similar to that of the bead model utilized in the SAXS simulation of a protein solution. 20,21 However, the application to the solid lamellar stacking structure is made for the first time.…”

Section: Avoidance Of Lamellar Collisionmentioning

confidence: 99%

“…The process itself is comparatively simple where the initially built structure model is modified step by step by generating random numbers to reproduce the observed SAXS pattern. The actual application of the Monte Carlo (MC) method was reported for the simulation of the observed 1D SAXS profile of a protein solution. − In some cases, many tiny beads coagulate together and their positions are varied randomly to reproduce the observed 1D SAXS profile. , As for the synthetic polymers, Hagita et al tried to simulate the stress-induced structural change of a rubber compound containing silica particles. − In their case, the target was the behavior of silica particles embedded in the rubber matrix. Bordín et al simulated a Nafion membrane channel structure using the MC method .…”

Section: Introductionmentioning

confidence: 99%

Metropolis Monte Carlo Simulation of Two-Dimensional Small-Angle X-ray Scattering Patterns of Oriented Polymer Materials

et al. 2019

View full text Add to dashboard Cite

The Metropolis Monte Carlo method has been applied to the simulation of twodimensional (2D) small-angle X-ray scattering (SAXS) patterns observed for oriented crystalline polymer samples from which concrete aggregation structure models of stacked lamellae were derived. Two types of lamellar models were built here in this study: one was the aggregation model of rectangular lamellae of a fixed shape, and another was the coagulation model of small particles. In the first model, several thousands of rectangular lamellae of a fixed shape were arranged randomly in a large 2D cell, and their relative positions and orientations were modified stepwise so that the observed 2D SAXS pattern was reproduced best in a quantitative manner. In the second model, several tens of thousands of small and round particles were input randomly into the cell, and their positions were changed stepwise so that the observed SAXS pattern was reproduced well enough. In both cases, the stacked lamellar structure was derived concretely. The structure parameters were extracted from these models to describe the long period, lamellar thickness, lamellar orientations, and so on, including the calculation of the correlation functions for the lamellar stacking structure. The advantage of these two models has been discussed from various points of view.

show abstract

Parallel GPGPU Evaluation of Small Angle X-Ray Scattering Profiles in a Markov Chain Monte Carlo Framework

Cited by 4 publications

References 20 publications

Accelerating the Held-Karp Algorithm for the Symmetric Traveling Salesman Problem

Accelerating the Held-Karp Algorithm for the Symmetric Traveling Salesman Problem

BCL::SAXS: GPU accelerated Debye method for computation of small angle X-ray scattering profiles

Metropolis Monte Carlo Simulation of Two-Dimensional Small-Angle X-ray Scattering Patterns of Oriented Polymer Materials

Contact Info

Product

Resources

About