2,630 research outputs found
Harnessing the Power of Many: Extensible Toolkit for Scalable Ensemble Applications
Many scientific problems require multiple distinct computational tasks to be
executed in order to achieve a desired solution. We introduce the Ensemble
Toolkit (EnTK) to address the challenges of scale, diversity and reliability
they pose. We describe the design and implementation of EnTK, characterize its
performance and integrate it with two distinct exemplar use cases: seismic
inversion and adaptive analog ensembles. We perform nine experiments,
characterizing EnTK overheads, strong and weak scalability, and the performance
of two use case implementations, at scale and on production infrastructures. We
show how EnTK meets the following general requirements: (i) implementing
dedicated abstractions to support the description and execution of ensemble
applications; (ii) support for execution on heterogeneous computing
infrastructures; (iii) efficient scalability up to O(10^4) tasks; and (iv)
fault tolerance. We discuss novel computational capabilities that EnTK enables
and the scientific advantages arising thereof. We propose EnTK as an important
addition to the suite of tools in support of production scientific computing
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
Best bang for your buck: GPU nodes for GROMACS biomolecular simulations
The molecular dynamics simulation package GROMACS runs efficiently on a wide
variety of hardware from commodity workstations to high performance computing
clusters. Hardware features are well exploited with a combination of SIMD,
multi-threading, and MPI-based SPMD/MPMD parallelism, while GPUs can be used as
accelerators to compute interactions offloaded from the CPU. Here we evaluate
which hardware produces trajectories with GROMACS 4.6 or 5.0 in the most
economical way. We have assembled and benchmarked compute nodes with various
CPU/GPU combinations to identify optimal compositions in terms of raw
trajectory production rate, performance-to-price ratio, energy efficiency, and
several other criteria. Though hardware prices are naturally subject to trends
and fluctuations, general tendencies are clearly visible. Adding any type of
GPU significantly boosts a node's simulation performance. For inexpensive
consumer-class GPUs this improvement equally reflects in the
performance-to-price ratio. Although memory issues in consumer-class GPUs could
pass unnoticed since these cards do not support ECC memory, unreliable GPUs can
be sorted out with memory checking tools. Apart from the obvious determinants
for cost-efficiency like hardware expenses and raw performance, the energy
consumption of a node is a major cost factor. Over the typical hardware
lifetime until replacement of a few years, the costs for electrical power and
cooling can become larger than the costs of the hardware itself. Taking that
into account, nodes with a well-balanced ratio of CPU and consumer-class GPU
resources produce the maximum amount of GROMACS trajectory over their lifetime
Using Pilot Systems to Execute Many Task Workloads on Supercomputers
High performance computing systems have historically been designed to support
applications comprised of mostly monolithic, single-job workloads. Pilot
systems decouple workload specification, resource selection, and task execution
via job placeholders and late-binding. Pilot systems help to satisfy the
resource requirements of workloads comprised of multiple tasks. RADICAL-Pilot
(RP) is a modular and extensible Python-based pilot system. In this paper we
describe RP's design, architecture and implementation, and characterize its
performance. RP is capable of spawning more than 100 tasks/second and supports
the steady-state execution of up to 16K concurrent tasks. RP can be used
stand-alone, as well as integrated with other application-level tools as a
runtime system
Synapse: Synthetic Application Profiler and Emulator
We introduce Synapse motivated by the needs to estimate and emulate workload
execution characteristics on high-performance and distributed heterogeneous
resources. Synapse has a platform independent application profiler, and the
ability to emulate profiled workloads on a variety of heterogeneous resources.
Synapse is used as a proxy application (or "representative application") for
real workloads, with the added advantage that it can be tuned at arbitrary
levels of granularity in ways that are simply not possible using real
applications. Experiments show that automated profiling using Synapse
represents application characteristics with high fidelity. Emulation using
Synapse can reproduce the application behavior in the original runtime
environment, as well as reproducing properties when used in a different
run-time environments
High-throughput Binding Affinity Calculations at Extreme Scales
Resistance to chemotherapy and molecularly targeted therapies is a major
factor in limiting the effectiveness of cancer treatment. In many cases,
resistance can be linked to genetic changes in target proteins, either
pre-existing or evolutionarily selected during treatment. Key to overcoming
this challenge is an understanding of the molecular determinants of drug
binding. Using multi-stage pipelines of molecular simulations we can gain
insights into the binding free energy and the residence time of a ligand, which
can inform both stratified and personal treatment regimes and drug development.
To support the scalable, adaptive and automated calculation of the binding free
energy on high-performance computing resources, we introduce the High-
throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block
approach in order to attain both workflow flexibility and performance. We
demonstrate close to perfect weak scaling to hundreds of concurrent multi-stage
binding affinity calculation pipelines. This permits a rapid time-to-solution
that is essentially invariant of the calculation protocol, size of candidate
ligands and number of ensemble simulations. As such, HTBAC advances the state
of the art of binding affinity calculations and protocols
GPU fast multipole method with lambda-dynamics features
A significant and computationally most demanding part of molecular dynamics simulations is the calculation of long-range electrostatic interactions. Such interactions can be evaluated directly by the naĂŻve pairwise summation algorithm, which is a ubiquitous showcase example for the compute power of graphics processing units (GPUS). However, the pairwise summation has O(N^2) computational complexity for N interacting particles; thus, an approximation method with a better scaling is required. Today, the prevalent method for such approximation in the field is particle mesh Ewald (PME). PME takes advantage of fast Fourier transforms (FFTS) to approximate the solution efficiently. However, as the underlying FFTS require all-to-all communication between ranks, PME runs into a communication bottleneck. Such communication overhead is negligible only for a moderate parallelization. With increased parallelization, as needed for high-performance applications, the usage of PME becomes unprofitable. Another PME drawback is its inability to perform constant pH simulations efficiently. In such simulations, the protonation states of a protein are allowed to change dynamically during the simulation. The description of this process requires a separate evaluation of the energies for each protonation state. This can not be calculated efficiently with PME as the algorithm requires a repeated FFT for each state, which leads to a linear overhead with respect to the number of states. For a fast approximation of pairwise Coulombic interactions, which does not suffer from PME drawbacks, the Fast Multipole Method (FMM) has been implemented and fully parallelized with CUDA. To assure the optimal FMM performance for diverse MD systems multiple parallelization strategies have been developed. The algorithm has been efficiently incorporated into GROMACS and subsequently tested to determine the optimal FMM parameter set for MD simulations. Finally, the FMM has been incorporated into GROMACS to allow for out-of-the-box electrostatic calculations. The performance of the single-GPU FMM implementation, tested in GROMACS 2019, achieves about a third of highly optimized CUDA PME performance when simulating systems with uniform particle distributions. However, the FMM is expected to outperform PME at high parallelization because the FMM global communication overhead is minimal compared to that of PME. Further, the FMM has been enhanced to provide the energies of an arbitrary number of titratable sites as needed in the constant-pH method. The extension is not fully optimized yet, but the first results show the strength of the FMM for constant pH simulations. For a relatively large system with half a million particles and more than a hundred titratable sites, a straightforward approach to compute alternative energies requires the repetition of a simulation for each state of the sites. The FMM calculates all energy terms only a factor 1.5 slower than a single simulation step. Further improvements of the GPU implementation are expected to yield even more speedup compared to the actual implementation.2021-11-1
BOSS-LDG: A Novel Computational Framework that Brings Together Blue Waters, Open Science Grid, Shifter and the LIGO Data Grid to Accelerate Gravitational Wave Discovery
We present a novel computational framework that connects Blue Waters, the
NSF-supported, leadership-class supercomputer operated by NCSA, to the Laser
Interferometer Gravitational-Wave Observatory (LIGO) Data Grid via Open Science
Grid technology. To enable this computational infrastructure, we configured,
for the first time, a LIGO Data Grid Tier-1 Center that can submit
heterogeneous LIGO workflows using Open Science Grid facilities. In order to
enable a seamless connection between the LIGO Data Grid and Blue Waters via
Open Science Grid, we utilize Shifter to containerize LIGO's workflow software.
This work represents the first time Open Science Grid, Shifter, and Blue Waters
are unified to tackle a scientific problem and, in particular, it is the first
time a framework of this nature is used in the context of large scale
gravitational wave data analysis. This new framework has been used in the last
several weeks of LIGO's second discovery campaign to run the most
computationally demanding gravitational wave search workflows on Blue Waters,
and accelerate discovery in the emergent field of gravitational wave
astrophysics. We discuss the implications of this novel framework for a wider
ecosystem of Higher Performance Computing users.Comment: 10 pages, 10 figures. Accepted as a Full Research Paper to the 13th
IEEE International Conference on eScienc
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