scholarly journals MP-NeRF: A Massively Parallel Method for Accelerating Protein Structure Reconstruction from Internal Coordinates

2021 ◽  
Author(s):  
Eric Alcaide ◽  
Stella Biderman ◽  
Amalio Telenti ◽  
Michael Cyrus Maher
Keyword(s):  
Natural Extension ◽  
Ease Of Use ◽  
Parallel Applications ◽  
Massively Parallel ◽  
Parallel Composition ◽  
Matrix Operations ◽  
Previous State ◽  
Dynamics Simulations ◽  
Protein Structure Reconstruction ◽  
Python Package

The conversion of proteins between internal and cartesian coordinates is a limiting step in many pipelines, such as molecular dynamics simulations and machine learning models. This conversion is typically carried out by sequential or parallel applications of the Natural extension of Reference Frame (NeRF) algorithm. This work proposes a massively parallel NeRF implementation which, depending on the polymer length, achieves speedups between 400-1000x over the previous state-of-the-art NeRF implementation. It accomplishes this by dividing the conversion into three main phases: a parallel composition of the monomer backbone, the assembly of backbone subunits, and the parallel elongation of sidechains; and by batching computations into a minimal number of efficient matrix operations. Special emphasis is placed on reusability and ease of use within diverse pipelines. We open source the code (available at https://github.com/EleutherAI/mp_nerf) and provide a corresponding python package.

scholarly journals A mechanism for balancing accuracy and scope in cross-machine black-box GPU performance modeling

2020 ◽  
Vol 34 (6) ◽  
pp. 589-614
Author(s):  
James D Stevens ◽  
Andreas Klöckner
Keyword(s):  
Performance Optimization ◽  
Heterogeneous Computing ◽  
Performance Modeling ◽  
Matrix Multiplication ◽  
Black Box ◽  
Ease Of Use ◽  
Performance Tuning ◽  
Parallel Applications ◽  
Accuracy Evaluation ◽  
Trade Offs

The ability to model, analyze, and predict execution time of computations is an important building block that supports numerous efforts, such as load balancing, benchmarking, job scheduling, developer-guided performance optimization, and the automation of performance tuning for high performance, parallel applications. In today’s increasingly heterogeneous computing environment, this task must be accomplished efficiently across multiple architectures, including massively parallel coprocessors like GPUs, which are increasingly prevalent in the world’s fastest supercomputers. To address this challenge, we present an approach for constructing customizable, cross-machine performance models for GPU kernels, including a mechanism to automatically and symbolically gather performance-relevant kernel operation counts, a tool for formulating mathematical models using these counts, and a customizable parameterized collection of benchmark kernels used to calibrate models to GPUs in a black-box fashion. With this approach, we empower the user to manage trade-offs between model accuracy, evaluation speed, and generalizability. A user can define their own model and customize the calibration process, making it as simple or complex as desired, and as application-targeted or general as desired. As application examples of our approach, we demonstrate both linear and nonlinear models; these examples are designed to predict execution times for multiple variants of a particular computation: two matrix-matrix multiplication variants, four discontinuous Galerkin differentiation operation variants, and two 2D five-point finite difference stencil variants. For each variant, we present accuracy results on GPUs from multiple vendors and hardware generations. We view this highly user-customizable approach as a response to a central question arising in GPU performance modeling: how can we model GPU performance in a cost-explanatory fashion while maintaining accuracy, evaluation speed, portability, and ease of use, an attribute we believe precludes approaches requiring manual collection of kernel or hardware statistics.

scholarly journals A Visual Analytics System for Optimizing Communications in Massively Parallel Applications

2017 ◽  
Author(s):  
Takanori Fujiwara ◽  
Preeti Malakar ◽  
Khairi Reda ◽  
Venkatram Vishwanath ◽  
Michael E. Papka ◽  
...  
Keyword(s):  
Visual Analytics ◽  
Parallel Applications ◽  
Massively Parallel

IMD: A Software Package for Molecular Dynamics Studies on Parallel Computers

1997 ◽  
Vol 08 (05) ◽  
pp. 1131-1140 ◽  
Author(s):  
J. Stadler ◽  
R. Mikulla ◽  
H.-R. Trebin
Keyword(s):  
Molecular Dynamics ◽  
Software Package ◽  
Large Scale ◽  
Md Simulation ◽  
Parallel Computers ◽  
Massively Parallel ◽  
Massively Parallel Computers ◽  
Communication Scheme ◽  
And Performance ◽  
Dynamics Simulations

We report on implementation and performance of the program IMD, designed for short range molecular dynamics simulations on massively parallel computers. After a short explanation of the cell-based algorithm, its extension to parallel computers as well as two variants of the communication scheme are discussed. We provide performance numbers for simulations of different sizes and compare them with values found in the literature. Finally we describe two applications, namely a very large scale simulation with more than 1.23×109 atoms, to our knowledge the largest published MD simulation up to this day and a simulation of a crack propagating in a two-dimensional quasicrystal.

scholarly journals PyTrilinos: Recent Advances in the Python Interface to Trilinos

2012 ◽  
Vol 20 (3) ◽  
pp. 311-325 ◽  
Author(s):  
William F. Spotz
Keyword(s):  
Linear Algebra ◽  
Utility Functions ◽  
Ease Of Use ◽  
Linear Solution ◽  
Dense Linear Algebra ◽  
Multilevel Preconditioners ◽  
Front End ◽  
Nonlinear Solvers ◽  
Sparse Linear Algebra ◽  
Python Package

PyTrilinos is a set of Python interfaces to compiled Trilinos packages. This collection supports serial and parallel dense linear algebra, serial and parallel sparse linear algebra, direct and iterative linear solution techniques, algebraic and multilevel preconditioners, nonlinear solvers and continuation algorithms, eigensolvers and partitioning algorithms. Also included are a variety of related utility functions and classes, including distributed I/O, coloring algorithms and matrix generation. PyTrilinos vector objects are compatible with the popular NumPy Python package. As a Python front end to compiled libraries, PyTrilinos takes advantage of the flexibility and ease of use of Python, and the efficiency of the underlying C++, C and Fortran numerical kernels. This paper covers recent, previously unpublished advances in the PyTrilinos package.

scholarly journals PyLipID: A Python package for analysis of protein-lipid interactions from MD simulations

2021 ◽  
Author(s):  
Wanling Song ◽  
Robin A. Corey ◽  
Bertie Ansell ◽  
Keith Cassidy ◽  
Michael Horrell ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Membrane Proteins ◽  
Molecular Dynamics Simulations ◽  
Binding Sites ◽  
Objective Assessment ◽  
Source Distribution ◽  
Lipid Interactions ◽  
Dynamics Simulations ◽  
Python Package

Lipids play important modulatory and structural roles for membrane proteins. Molecular dynamics simulations are frequently used to provide insights into the nature of these protein-lipid interactions. Systematic comparative analysis requires tools that provide algorithms for objective assessment of such interactions. We introduce PyLipID, a python package for the identification and characterization of specific lipid interactions and binding sites on membrane proteins from molecular dynamics simulations. PyLipID uses a community analysis approach for binding site detection, calculating lipid residence times for both the individual protein residues and the detected binding sites. To assist structural analysis, PyLipID produces representative bound lipid poses from simulation data, using a density-based scoring function. To estimate residue contacts robustly, PyLipID uses a dual-cutoff scheme to differentiate between lipid conformational rearrangements whilst bound from full dissociation events. In addition to the characterization of protein-lipid interactions, PyLipID is applicable to analysis of the interactions of membrane proteins with other ligands. By combining automated analysis, efficient algorithms, and open-source distribution, PyLipID facilitates the systematic analysis of lipid interactions from large simulation datasets of multiple species of membrane proteins.

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