3,019 research outputs found
Parallel stereo vision algorithm
Integrating a stereo-photogrammetric robot
head into a real-time system requires software
solutions that rapidly resolve the stereo correspondence
problem. The stereo-matcher presented in this
paper uses therefore code parallelisation and was
tested on three different processors with x87 and AVX.
The results show that a 5mega pixels colour image can
be matched in 5,55 seconds or as monochrome in 3,3
seconds
Classification algorithms on the cell processor
The rapid advancement in the capacity and reliability of data storage technology has allowed for the retention of virtually limitless quantity and detail of digital information. Massive information databases are becoming more and more widespread among governmental, educational, scientific, and commercial organizations. By segregating this data into carefully defined input (e.g.: images) and output (e.g.: classification labels) sets, a classification algorithm can be used develop an internal expert model of the data by employing a specialized training algorithm. A properly trained classifier is capable of predicting the output for future input data from the same input domain that it was trained on. Two popular classifiers are Neural Networks and Support Vector Machines. Both, as with most accurate classifiers, require massive computational resources to carry out the training step and can take months to complete when dealing with extremely large data sets. In most cases, utilizing larger training improves the final accuracy of the trained classifier. However, access to the kinds of computational resources required to do so is expensive and out of reach of private or under funded institutions. The Cell Broadband Engine (CBE), introduced by Sony, Toshiba, and IBM has recently been introduced into the market. The current most inexpensive iteration is available in the Sony Playstation 3 ® computer entertainment system. The CBE is a novel multi-core architecture which features many hardware enhancements designed to accelerate the processing of massive amounts of data. These characteristics and the cheap and widespread availability of this technology make the Cell a prime candidate for the task of training classifiers. In this work, the feasibility of the Cell processor in the use of training Neural Networks and Support Vector Machines was explored. In the Neural Network family of classifiers, the fully connected Multilayer Perceptron and Convolution Network were implemented. In the Support Vector Machine family, a Working Set technique known as the Gradient Projection-based Decomposition Technique, as well as the Cascade SVM were implemented
2D operators on topographic and non-topographic architectures-implementation, efficiency analysis, and architecture selection methodology
Topographic and non-topographic image processing architectures and chips, developed within the CNN community recently, are analysed and compared. It is achieved on a way that the 2D operators are collected to classes according to their implementation methods on the different architectures, and the main implementation parameters of the different operator classes are compared. Based on the results, an efficient architecture selection methodology is formalized
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
A Framework for Megascale Agent Based Model Simulations on Graphics Processing Units
Agent-based modeling is a technique for modeling dynamic systems from the bottom up. Individual elements of the system are represented computationally as agents. The system-level behaviors emerge from the micro-level interactions of the agents. Contemporary state-of-the-art agent-based modeling toolkits are essentially discrete-event simulators designed to execute serially on the Central Processing Unit (CPU). They simulate Agent-Based Models (ABMs) by executing agent actions one at a time. In addition to imposing an un-natural execution order, these toolkits have limited scalability. In this article, we investigate data-parallel computer architectures such as Graphics Processing Units (GPUs) to simulate large scale ABMs. We have developed a series of efficient, data parallel algorithms for handling environment updates, various agent interactions, agent death and replication, and gathering statistics. We present three fundamental innovations that provide unprecedented scalability. The first is a novel stochastic memory allocator which enables parallel agent replication in O(1) average time. The second is a technique for resolving precedence constraints for agent actions in parallel. The third is a method that uses specialized graphics hardware, to gather and process statistical measures. These techniques have been implemented on a modern day GPU resulting in a substantial performance increase. We believe that our system is the first ever completely GPU based agent simulation framework. Although GPUs are the focus of our current implementations, our techniques can easily be adapted to other data-parallel architectures. We have benchmarked our framework against contemporary toolkits using two popular ABMs, namely, SugarScape and StupidModel.GPGPU, Agent Based Modeling, Data Parallel Algorithms, Stochastic Simulations
Data Provenance and Management in Radio Astronomy: A Stream Computing Approach
New approaches for data provenance and data management (DPDM) are required
for mega science projects like the Square Kilometer Array, characterized by
extremely large data volume and intense data rates, therefore demanding
innovative and highly efficient computational paradigms. In this context, we
explore a stream-computing approach with the emphasis on the use of
accelerators. In particular, we make use of a new generation of high
performance stream-based parallelization middleware known as InfoSphere
Streams. Its viability for managing and ensuring interoperability and integrity
of signal processing data pipelines is demonstrated in radio astronomy. IBM
InfoSphere Streams embraces the stream-computing paradigm. It is a shift from
conventional data mining techniques (involving analysis of existing data from
databases) towards real-time analytic processing. We discuss using InfoSphere
Streams for effective DPDM in radio astronomy and propose a way in which
InfoSphere Streams can be utilized for large antennae arrays. We present a
case-study: the InfoSphere Streams implementation of an autocorrelating
spectrometer, and using this example we discuss the advantages of the
stream-computing approach and the utilization of hardware accelerators
Indexed dependence metadata and its applications in software performance optimisation
To achieve continued performance improvements, modern microprocessor design is tending to concentrate
an increasing proportion of hardware on computation units with less automatic management
of data movement and extraction of parallelism. As a result, architectures increasingly include multiple
computation cores and complicated, software-managed memory hierarchies. Compilers have
difficulty characterizing the behaviour of a kernel in a general enough manner to enable automatic
generation of efficient code in any but the most straightforward of cases.
We propose the concept of indexed dependence metadata to improve application development and
mapping onto such architectures. The metadata represent both the iteration space of a kernel and the
mapping of that iteration space from a given index to the set of data elements that iteration might
use: thus the dependence metadata is indexed by the kernel’s iteration space. This explicit mapping
allows the compiler or runtime to optimise the program more efficiently, and improves the program
structure for the developer. We argue that this form of explicit interface specification reduces the need
for premature, architecture-specific optimisation. It improves program portability, supports intercomponent
optimisation and enables generation of efficient data movement code.
We offer the following contributions: an introduction to the concept of indexed dependence metadata
as a generalisation of stream programming, a demonstration of its advantages in a component
programming system, the decoupled access/execute model for C++ programs, and how indexed dependence
metadata might be used to improve the programming model for GPU-based designs. Our
experimental results with prototype implementations show that indexed dependence metadata supports
automatic synthesis of double-buffered data movement for the Cell processor and enables aggressive
loop fusion optimisations in image processing, linear algebra and multigrid application case
studies
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