37,858 research outputs found
Ianus: an Adpative FPGA Computer
Dedicated machines designed for specific computational algorithms can
outperform conventional computers by several orders of magnitude. In this note
we describe {\it Ianus}, a new generation FPGA based machine and its basic
features: hardware integration and wide reprogrammability. Our goal is to build
a machine that can fully exploit the performance potential of new generation
FPGA devices. We also plan a software platform which simplifies its
programming, in order to extend its intended range of application to a wide
class of interesting and computationally demanding problems. The decision to
develop a dedicated processor is a complex one, involving careful assessment of
its performance lead, during its expected lifetime, over traditional computers,
taking into account their performance increase, as predicted by Moore's law. We
discuss this point in detail
Janus II: a new generation application-driven computer for spin-system simulations
This paper describes the architecture, the development and the implementation
of Janus II, a new generation application-driven number cruncher optimized for
Monte Carlo simulations of spin systems (mainly spin glasses). This domain of
computational physics is a recognized grand challenge of high-performance
computing: the resources necessary to study in detail theoretical models that
can make contact with experimental data are by far beyond those available using
commodity computer systems. On the other hand, several specific features of the
associated algorithms suggest that unconventional computer architectures, which
can be implemented with available electronics technologies, may lead to order
of magnitude increases in performance, reducing to acceptable values on human
scales the time needed to carry out simulation campaigns that would take
centuries on commercially available machines. Janus II is one such machine,
recently developed and commissioned, that builds upon and improves on the
successful JANUS machine, which has been used for physics since 2008 and is
still in operation today. This paper describes in detail the motivations behind
the project, the computational requirements, the architecture and the
implementation of this new machine and compares its expected performances with
those of currently available commercial systems.Comment: 28 pages, 6 figure
SUE: A Special Purpose Computer for Spin Glass Models
The use of last generation Programmable Electronic Components makes possible
the construction of very powerful and competitive special purpose computers. We
have designed, constructed and tested a three-dimensional Spin Glass model
dedicated machine, which consists of 12 identical boards. Each single board can
simulate 8 different systems, updating all the systems at every clock cycle.
The update speed of the whole machine is 217ps/spin with 48 MHz clock
frequency. A device devoted to fast random number generation has been developed
and included in every board. The on-board reprogrammability permits us to
change easily the lattice size, or even the update algorithm or the action. We
present here a detailed description of the machine and the first runs using the
Heat Bath algorithm.Comment: Submitted to Computer Physics Communications, 19 pages, 5 figures,
references adde
Simulating chemistry using quantum computers
The difficulty of simulating quantum systems, well-known to quantum chemists,
prompted the idea of quantum computation. One can avoid the steep scaling
associated with the exact simulation of increasingly large quantum systems on
conventional computers, by mapping the quantum system to another, more
controllable one. In this review, we discuss to what extent the ideas in
quantum computation, now a well-established field, have been applied to
chemical problems. We describe algorithms that achieve significant advantages
for the electronic-structure problem, the simulation of chemical dynamics,
protein folding, and other tasks. Although theory is still ahead of experiment,
we outline recent advances that have led to the first chemical calculations on
small quantum information processors.Comment: 27 pages. Submitted to Ann. Rev. Phys. Che
Simulating spin systems on IANUS, an FPGA-based computer
We describe the hardwired implementation of algorithms for Monte Carlo
simulations of a large class of spin models. We have implemented these
algorithms as VHDL codes and we have mapped them onto a dedicated processor
based on a large FPGA device. The measured performance on one such processor is
comparable to O(100) carefully programmed high-end PCs: it turns out to be even
better for some selected spin models. We describe here codes that we are
currently executing on the IANUS massively parallel FPGA-based system.Comment: 19 pages, 8 figures; submitted to Computer Physics Communication
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
Simulating spin models on GPU
Over the last couple of years it has been realized that the vast
computational power of graphics processing units (GPUs) could be harvested for
purposes other than the video game industry. This power, which at least
nominally exceeds that of current CPUs by large factors, results from the
relative simplicity of the GPU architectures as compared to CPUs, combined with
a large number of parallel processing units on a single chip. To benefit from
this setup for general computing purposes, the problems at hand need to be
prepared in a way to profit from the inherent parallelism and hierarchical
structure of memory accesses. In this contribution I discuss the performance
potential for simulating spin models, such as the Ising model, on GPU as
compared to conventional simulations on CPU.Comment: 5 pages, 4 figures, elsarticl
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