13,458 research outputs found
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
Automating Fault Tolerance in High-Performance Computational Biological Jobs Using Multi-Agent Approaches
Background: Large-scale biological jobs on high-performance computing systems
require manual intervention if one or more computing cores on which they
execute fail. This places not only a cost on the maintenance of the job, but
also a cost on the time taken for reinstating the job and the risk of losing
data and execution accomplished by the job before it failed. Approaches which
can proactively detect computing core failures and take action to relocate the
computing core's job onto reliable cores can make a significant step towards
automating fault tolerance.
Method: This paper describes an experimental investigation into the use of
multi-agent approaches for fault tolerance. Two approaches are studied, the
first at the job level and the second at the core level. The approaches are
investigated for single core failure scenarios that can occur in the execution
of parallel reduction algorithms on computer clusters. A third approach is
proposed that incorporates multi-agent technology both at the job and core
level. Experiments are pursued in the context of genome searching, a popular
computational biology application.
Result: The key conclusion is that the approaches proposed are feasible for
automating fault tolerance in high-performance computing systems with minimal
human intervention. In a typical experiment in which the fault tolerance is
studied, centralised and decentralised checkpointing approaches on an average
add 90% to the actual time for executing the job. On the other hand, in the
same experiment the multi-agent approaches add only 10% to the overall
execution time.Comment: Computers in Biology and Medicin
Computational Physics on Graphics Processing Units
The use of graphics processing units for scientific computations is an
emerging strategy that can significantly speed up various different algorithms.
In this review, we discuss advances made in the field of computational physics,
focusing on classical molecular dynamics, and on quantum simulations for
electronic structure calculations using the density functional theory, wave
function techniques, and quantum field theory.Comment: Proceedings of the 11th International Conference, PARA 2012,
Helsinki, Finland, June 10-13, 201
Developing performance-portable molecular dynamics kernels in Open CL
This paper investigates the development of a molecular dynamics code that is highly portable between architectures. Using OpenCL, we develop an implementation of Sandia’s miniMD benchmark that achieves good levels of performance across a wide range of hardware: CPUs, discrete GPUs and integrated GPUs.
We demonstrate that the performance bottlenecks of miniMD’s short-range force calculation kernel are the same across these architectures, and detail a number of platform- agnostic optimisations that improve its performance by at least 2x on all hardware considered. Our complete code is shown to be 1.7x faster than the original miniMD, and at most 2x slower than implementations individually hand-tuned for a specific architecture
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