59,704 research outputs found
FFT for the APE Parallel Computer
We present a parallel FFT algorithm for SIMD systems following the `Transpose
Algorithm' approach. The method is based on the assignment of the data field
onto a 1-dimensional ring of systolic cells. The systolic array can be
universally mapped onto any parallel system. In particular for systems with
next-neighbour connectivity our method has the potential to improve the
efficiency of matrix transposition by use of hyper-systolic communication. We
have realized a scalable parallel FFT on the APE100/Quadrics massively parallel
computer, where our implementation is part of a 2-dimensional hydrodynamics
code for turbulence studies. A possible generalization to 4-dimensional FFT is
presented, having in mind QCD applications.Comment: 17 pages, 13 figures, figures include
A Parallel Adaptive P3M code with Hierarchical Particle Reordering
We discuss the design and implementation of HYDRA_OMP a parallel
implementation of the Smoothed Particle Hydrodynamics-Adaptive P3M (SPH-AP3M)
code HYDRA. The code is designed primarily for conducting cosmological
hydrodynamic simulations and is written in Fortran77+OpenMP. A number of
optimizations for RISC processors and SMP-NUMA architectures have been
implemented, the most important optimization being hierarchical reordering of
particles within chaining cells, which greatly improves data locality thereby
removing the cache misses typically associated with linked lists. Parallel
scaling is good, with a minimum parallel scaling of 73% achieved on 32 nodes
for a variety of modern SMP architectures. We give performance data in terms of
the number of particle updates per second, which is a more useful performance
metric than raw MFlops. A basic version of the code will be made available to
the community in the near future.Comment: 34 pages, 12 figures, accepted for publication in Computer Physics
Communication
An Evaluation of the X10 Programming Language
As predicted by Moore\u27s law, the number of transistors on a chip has been doubled approximately every two years. As miraculous as it sounds, for many years, the extra transistors have massively benefited the whole computer industry, by using the extra transistors to increase CPU clock speed, thus boosting performance. However, due to heat wall and power constraints, the clock speed cannot be increased limitlessly. Hardware vendors now have to take another path other than increasing clock speed, which is to utilize the transistors to increase the number of processor cores on each chip. This hardware structural change presents inevitable challenges to software structure, where single thread targeted software will not benefit from newer chips or may even suffer from lower clock speed. The two fundamental challenges are: 1. How to deal with the stagnation of single core clock speed and cache memory. 2. How to utilize the additional power generated from more cores on a chip. Most software programming languages nowadays have distributed computing support, such as C and Java [1]. Meanwhile, some new programming languages were invented from scratch just to take advantage of the more distributed hardware structures. The X10 Programming Language is one of them. The goal of this project is to evaluate X10 in terms of performance, programmability and tool support
A portable platform for accelerated PIC codes and its application to GPUs using OpenACC
We present a portable platform, called PIC_ENGINE, for accelerating
Particle-In-Cell (PIC) codes on heterogeneous many-core architectures such as
Graphic Processing Units (GPUs). The aim of this development is efficient
simulations on future exascale systems by allowing different parallelization
strategies depending on the application problem and the specific architecture.
To this end, this platform contains the basic steps of the PIC algorithm and
has been designed as a test bed for different algorithmic options and data
structures. Among the architectures that this engine can explore, particular
attention is given here to systems equipped with GPUs. The study demonstrates
that our portable PIC implementation based on the OpenACC programming model can
achieve performance closely matching theoretical predictions. Using the Cray
XC30 system, Piz Daint, at the Swiss National Supercomputing Centre (CSCS), we
show that PIC_ENGINE running on an NVIDIA Kepler K20X GPU can outperform the
one on an Intel Sandybridge 8-core CPU by a factor of 3.4
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Automatic data/program partitioning using the single assignment principle
Loosely-coupled MIMD architectures do not suffer from memory contention; hence large numbers of processors may be utilized. The main problem, however, is how to partition data and programs in order to exploit the available parallelism. In this paper we show that efficient schemes for automatic data/program partitioning and synchronization may be employed if single assignment is used. Using simulations of program loops common to scientific computations (the Livermore Loops), we demonstrate that only a small fraction of data accesses are remote and thus the degradation in network performance due to multiprocessing is minimal
Array operators using multiple dispatch: a design methodology for array implementations in dynamic languages
Arrays are such a rich and fundamental data type that they tend to be built
into a language, either in the compiler or in a large low-level library.
Defining this functionality at the user level instead provides greater
flexibility for application domains not envisioned by the language designer.
Only a few languages, such as C++ and Haskell, provide the necessary power to
define -dimensional arrays, but these systems rely on compile-time
abstraction, sacrificing some flexibility. In contrast, dynamic languages make
it straightforward for the user to define any behavior they might want, but at
the possible expense of performance.
As part of the Julia language project, we have developed an approach that
yields a novel trade-off between flexibility and compile-time analysis. The
core abstraction we use is multiple dispatch. We have come to believe that
while multiple dispatch has not been especially popular in most kinds of
programming, technical computing is its killer application. By expressing key
functions such as array indexing using multi-method signatures, a surprising
range of behaviors can be obtained, in a way that is both relatively easy to
write and amenable to compiler analysis. The compact factoring of concerns
provided by these methods makes it easier for user-defined types to behave
consistently with types in the standard library.Comment: 6 pages, 2 figures, workshop paper for the ARRAY '14 workshop, June
11, 2014, Edinburgh, United Kingdo
Efficient Iterative Processing in the SciDB Parallel Array Engine
Many scientific data-intensive applications perform iterative computations on
array data. There exist multiple engines specialized for array processing.
These engines efficiently support various types of operations, but none
includes native support for iterative processing. In this paper, we develop a
model for iterative array computations and a series of optimizations. We
evaluate the benefits of an optimized, native support for iterative array
processing on the SciDB engine and real workloads from the astronomy domain
Teaching Parallel Programming Using Java
This paper presents an overview of the "Applied Parallel Computing" course
taught to final year Software Engineering undergraduate students in Spring 2014
at NUST, Pakistan. The main objective of the course was to introduce practical
parallel programming tools and techniques for shared and distributed memory
concurrent systems. A unique aspect of the course was that Java was used as the
principle programming language. The course was divided into three sections. The
first section covered parallel programming techniques for shared memory systems
that include multicore and Symmetric Multi-Processor (SMP) systems. In this
section, Java threads was taught as a viable programming API for such systems.
The second section was dedicated to parallel programming tools meant for
distributed memory systems including clusters and network of computers. We used
MPJ Express-a Java MPI library-for conducting programming assignments and lab
work for this section. The third and the final section covered advanced topics
including the MapReduce programming model using Hadoop and the General Purpose
Computing on Graphics Processing Units (GPGPU).Comment: 8 Pages, 6 figures, MPJ Express, MPI Java, Teaching Parallel
Programmin
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