12,625 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
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Parallel data compression
Data compression schemes remove data redundancy in communicated and stored data and increase the effective capacities of communication and storage devices. Parallel algorithms and implementations for textual data compression are surveyed. Related concepts from parallel computation and information theory are briefly discussed. Static and dynamic methods for codeword construction and transmission on various models of parallel computation are described. Included are parallel methods which boost system speed by coding data concurrently, and approaches which employ multiple compression techniques to improve compression ratios. Theoretical and empirical comparisons are reported and areas for future research are suggested
Interstellar: Using Halide's Scheduling Language to Analyze DNN Accelerators
We show that DNN accelerator micro-architectures and their program mappings
represent specific choices of loop order and hardware parallelism for computing
the seven nested loops of DNNs, which enables us to create a formal taxonomy of
all existing dense DNN accelerators. Surprisingly, the loop transformations
needed to create these hardware variants can be precisely and concisely
represented by Halide's scheduling language. By modifying the Halide compiler
to generate hardware, we create a system that can fairly compare these prior
accelerators. As long as proper loop blocking schemes are used, and the
hardware can support mapping replicated loops, many different hardware
dataflows yield similar energy efficiency with good performance. This is
because the loop blocking can ensure that most data references stay on-chip
with good locality and the processing units have high resource utilization. How
resources are allocated, especially in the memory system, has a large impact on
energy and performance. By optimizing hardware resource allocation while
keeping throughput constant, we achieve up to 4.2X energy improvement for
Convolutional Neural Networks (CNNs), 1.6X and 1.8X improvement for Long
Short-Term Memories (LSTMs) and multi-layer perceptrons (MLPs), respectively.Comment: Published as a conference paper at ASPLOS 202
Systolic and Hyper-Systolic Algorithms for the Gravitational N-Body Problem, with an Application to Brownian Motion
A systolic algorithm rhythmically computes and passes data through a network
of processors. We investigate the performance of systolic algorithms for
implementing the gravitational N-body problem on distributed-memory computers.
Systolic algorithms minimize memory requirements by distributing the particles
between processors. We show that the performance of systolic routines can be
greatly enhanced by the use of non-blocking communication, which allows
particle coordinates to be communicated at the same time that force
calculations are being carried out. Hyper-systolic algorithms reduce the
communication complexity at the expense of increased memory demands. As an
example of an application requiring large N, we use the systolic algorithm to
carry out direct-summation simulations using 10^6 particles of the Brownian
motion of the supermassive black hole at the center of the Milky Way galaxy. We
predict a 3D random velocity of 0.4 km/s for the black hole.Comment: 33 pages, 10 postscript figure
From 4D medical images (CT, MRI, and Ultrasound) to 4D structured mesh models of the left ventricular endocardium for patient-specific simulations
With cardiovascular disease (CVD) remaining the primary cause of death worldwide, early detection of CVDs becomes essential. The intracardiac flow is an important component of ventricular function, motion kinetics, wash-out of ventricular chambers, and ventricular energetics. Coupling between Computational Fluid Dynamics (CFD) simulations and medical images can play a fundamental role in terms of patient-specific diagnostic tools. From a technical perspective, CFD simulations with moving boundaries could easily lead to negative volumes errors and the sudden failure of the simulation. The generation of high-quality 4D meshes (3D in space + time) with 1-to-l vertex becomes essential to perform a CFD simulation with moving boundaries. In this context, we developed a semiautomatic morphing tool able to create 4D high-quality structured meshes starting from a segmented 4D dataset. To prove the versatility and efficiency, the method was tested on three different 4D datasets (Ultrasound, MRI, and CT) by evaluating the quality and accuracy of the resulting 4D meshes. Furthermore, an estimation of some physiological quantities is accomplished for the 4D CT reconstruction. Future research will aim at extending the region of interest, further automation of the meshing algorithm, and generating structured hexahedral mesh models both for the blood and myocardial volume
Design of testbed and emulation tools
The research summarized was concerned with the design of testbed and emulation tools suitable to assist in projecting, with reasonable accuracy, the expected performance of highly concurrent computing systems on large, complete applications. Such testbed and emulation tools are intended for the eventual use of those exploring new concurrent system architectures and organizations, either as users or as designers of such systems. While a range of alternatives was considered, a software based set of hierarchical tools was chosen to provide maximum flexibility, to ease in moving to new computers as technology improves and to take advantage of the inherent reliability and availability of commercially available computing systems
A pilgrimage to gravity on GPUs
In this short review we present the developments over the last 5 decades that
have led to the use of Graphics Processing Units (GPUs) for astrophysical
simulations. Since the introduction of NVIDIA's Compute Unified Device
Architecture (CUDA) in 2007 the GPU has become a valuable tool for N-body
simulations and is so popular these days that almost all papers about high
precision N-body simulations use methods that are accelerated by GPUs. With the
GPU hardware becoming more advanced and being used for more advanced algorithms
like gravitational tree-codes we see a bright future for GPU like hardware in
computational astrophysics.Comment: To appear in: European Physical Journal "Special Topics" : "Computer
Simulations on Graphics Processing Units" . 18 pages, 8 figure
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