3,616 research outputs found
A Comparison of some recent Task-based Parallel Programming Models
The need for parallel programming models that are simple to use and at the same time efficient for current ant future parallel platforms has led to recent attention to task-based models such as Cilk++, Intel TBB and the task concept in OpenMP version 3.0. The choice of model and implementation can have a major impact on the final performance and in order to understand some of the trade-offs we have made a quantitative study comparing four implementations of OpenMP (gcc, Intel icc, Sun studio and the research compiler Mercurium/nanos mcc), Cilk++ and Wool, a high-performance task-based library developed at SICS.
Abstract. We use microbenchmarks to characterize costs for task-creation and stealing and the Barcelona OpenMP Tasks Suite for characterizing application performance. By far Wool and Cilk++ have the lowest overhead in both spawning and stealing tasks. This is reflected in application performance when many tasks with small granularity are spawned where Cilk++ and, in particular, has the highest performance. For coarse granularity applications, the OpenMP implementations have quite similar performance as the more light-weight Cilk++ and Wool except for one application where mcc is superior thanks to a superior task scheduler.
Abstract. The OpenMP implemenations are generally not yet ready for use when the task granularity becomes very small. There is no inherent reason for this, so we expect future implementations of OpenMP to focus on this issue
Efficient Implementations of Molecular Dynamics Simulations for Lennard-Jones Systems
Efficient implementations of the classical molecular dynamics (MD) method for
Lennard-Jones particle systems are considered. Not only general algorithms but
also techniques that are efficient for some specific CPU architectures are also
explained. A simple spatial-decomposition-based strategy is adopted for
parallelization. By utilizing the developed code, benchmark simulations are
performed on a HITACHI SR16000/J2 system consisting of IBM POWER6 processors
which are 4.7 GHz at the National Institute for Fusion Science (NIFS) and an
SGI Altix ICE 8400EX system consisting of Intel Xeon processors which are 2.93
GHz at the Institute for Solid State Physics (ISSP), the University of Tokyo.
The parallelization efficiency of the largest run, consisting of 4.1 billion
particles with 8192 MPI processes, is about 73% relative to that of the
smallest run with 128 MPI processes at NIFS, and it is about 66% relative to
that of the smallest run with 4 MPI processes at ISSP. The factors causing the
parallel overhead are investigated. It is found that fluctuations of the
execution time of each process degrade the parallel efficiency. These
fluctuations may be due to the interference of the operating system, which is
known as OS Jitter.Comment: 33 pages, 19 figures, add references and figures are revise
Multi-Architecture Monte-Carlo (MC) Simulation of Soft Coarse-Grained Polymeric Materials: SOft coarse grained Monte-carlo Acceleration (SOMA)
Multi-component polymer systems are important for the development of new
materials because of their ability to phase-separate or self-assemble into
nano-structures. The Single-Chain-in-Mean-Field (SCMF) algorithm in conjunction
with a soft, coarse-grained polymer model is an established technique to
investigate these soft-matter systems. Here we present an im- plementation of
this method: SOft coarse grained Monte-carlo Accelera- tion (SOMA). It is
suitable to simulate large system sizes with up to billions of particles, yet
versatile enough to study properties of different kinds of molecular
architectures and interactions. We achieve efficiency of the simulations
commissioning accelerators like GPUs on both workstations as well as
supercomputers. The implementa- tion remains flexible and maintainable because
of the implementation of the scientific programming language enhanced by
OpenACC pragmas for the accelerators. We present implementation details and
features of the program package, investigate the scalability of our
implementation SOMA, and discuss two applications, which cover system sizes
that are difficult to reach with other, common particle-based simulation
methods
Stepping Stones to Inductive Synthesis of Low-Level Looping Programs
Inductive program synthesis, from input/output examples, can provide an
opportunity to automatically create programs from scratch without presupposing
the algorithmic form of the solution. For induction of general programs with
loops (as opposed to loop-free programs, or synthesis for domain-specific
languages), the state of the art is at the level of introductory programming
assignments. Most problems that require algorithmic subtlety, such as fast
sorting, have remained out of reach without the benefit of significant
problem-specific background knowledge. A key challenge is to identify cues that
are available to guide search towards correct looping programs. We present
MAKESPEARE, a simple delayed-acceptance hillclimbing method that synthesizes
low-level looping programs from input/output examples. During search, delayed
acceptance bypasses small gains to identify significantly-improved stepping
stone programs that tend to generalize and enable further progress. The method
performs well on a set of established benchmarks, and succeeds on the
previously unsolved "Collatz Numbers" program synthesis problem. Additional
benchmarks include the problem of rapidly sorting integer arrays, in which we
observe the emergence of comb sort (a Shell sort variant that is empirically
fast). MAKESPEARE has also synthesized a record-setting program on one of the
puzzles from the TIS-100 assembly language programming game.Comment: AAAI 201
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