Hardware Acceleration of Background Modeling in the Compressed Domain

In intelligent video surveillance systems, scalability (of the number of simultaneous video streams) is important. Two key factors which hinder scalability are the time spent in decompressing the input video streams, and the limited computational power of the processor. This paper demonstrates how a combination of algorithmic and hardware techniques can overcome these limitations, and significantly increase the number of simultaneous streams. The techniques used are processing in the compressed domain, and exploitation of the multicore and vector processing capability of modern processors. The paper presents a system which performs background modeling, using a Mixture of Gaussians approach. This is an important first step in the segmentation of moving targets. The paper explores the effects of reducing the number of coefficients in the compressed domain, in terms of throughput speed and quality of the background modeling. The speedups achieved by exploiting compressed domain processing, multicore and vector processing are explored individually. Experiments show that a combination of all these techniques can give a speedup of 170 times on a single CPU compared to a purely serial, spatial domain implementation, with a slight gain in quality.

Energy expenditure during activity in the American lobster Homarus americanus:Correlations with body acceleration

How animals manage time and expend energy has implications for survivorship. Being able to measure key metabolic costs of animals under natural conditions is therefore an important tool in behavioral ecology. One method for estimating activity-specific metabolic rate is via derived measures of acceleration, often 'overall dynamic body acceleration' (ODBA), recorded by an instrumented acceleration logger. ODBA has been shown to correlate well with rate of oxygen consumption (V ?o) in a range of species during activity in the laboratory. This study devised a method for attaching acceleration loggers to decapod crustaceans and then correlated ODBA against concurrent respirometry readings to assess accelerometry as a proxy for activity-specific energy expenditure in a model species, the American lobster Homarus americanus. Where the instrumented animals exhibited a sufficient range of activity levels, positive linear relationships were found between V ?o and ODBA over 20min periods at a range of ambient temperatures (6, 13 and 20°C). Mixed effect linear models based on these data and morphometrics provided reasonably strong predictive power for estimating activity-specific V ?o from ODBA. These V ?o-ODBA calibrations demonstrate the potential of accelerometry as an effective predictor of behavior-specific metabolic rate of crustaceans in the wild during periods of activity. © 2013 Elsevier Inc.

Acceleration of ions up to 20MeV/nucleon in the ultrashort, high-intensity regime

The measurements reported here provide scaling laws for the ion acceleration process in the regime of ultrashort (50 fs), ultrahigh contrast (10) and ultrahigh intensity (> 10W/cm ), never investigated previously. The scaling of the accelerated ion energies was studied by varying a number of parameters such as target thickness (down to 10nm), target material (C and Al) and laser light polar- ization (circular and linear) at 35° and normal laser incidence. A twofold increase in proton energy and an order of magnitude enhancement in ion flux have been observed over the investigated thickness range at 35° angle of incidence. Further- more, at normal laser incidence, measured peak proton energies of about 20 MeV are observed almost independently of the target thickness over a wide range (50nm- 10 µm). 1. © 2012 by Società Italiana di Fisica.

Laser-driven acceleration of ions:State of the art, perspectives and applications

The acceleration of ions with high-power lasers has been a very active field of research during the past 10 years. This paper summarizes the main results obtained in the field, detailing the mechanisms of the acceleration process and the main observed beam characteristics. Perspectives for future development of the field and current and future applications are also discussed. © 2012 by Società Italiana di Fisica.

cudaMap: a GPU accelerated program for gene expression connectivity mapping

Background: Modern cancer research often involves large datasets and the use of sophisticated statistical techniques. Together these add a heavy computational load to the analysis, which is often coupled with issues surrounding data accessibility. Connectivity mapping is an advanced bioinformatic and computational technique dedicated to therapeutics discovery and drug re-purposing around differential gene expression analysis. On a normal desktop PC, it is common for the connectivity mapping task with a single gene signature to take >2h to complete using sscMap, a popular Java application that runs on standard CPUs (Central Processing Units). Here, we describe new software, cudaMap, which has been implemented using CUDA C/C++ to harness the computational power of NVIDIA GPUs (Graphics Processing Units) to greatly reduce processing times for connectivity mapping.

Results: cudaMap can identify candidate therapeutics from the same signature in just over thirty seconds when using an NVIDIA Tesla C2050 GPU. Results from the analysis of multiple gene signatures, which would previously have taken several days, can now be obtained in as little as 10 minutes, greatly facilitating candidate therapeutics discovery with high throughput. We are able to demonstrate dramatic speed differentials between GPU assisted performance and CPU executions as the computational load increases for high accuracy evaluation of statistical significance.

Conclusion: Emerging 'omics' technologies are constantly increasing the volume of data and information to be processed in all areas of biomedical research. Embracing the multicore functionality of GPUs represents a major avenue of local accelerated computing. cudaMap will make a strong contribution in the discovery of candidate therapeutics by enabling speedy execution of heavy duty connectivity mapping tasks, which are increasingly required in modern cancer research. cudaMap is open source and can be freely downloaded from http://purl.oclc.org/NET/cudaMap.

Advanced strategies for ion acceleration using high-power lasers

A short overview of laser-plasma acceleration of ions is presented. The focus is on some recent experimental results and the related theoretical work on advanced regimes. These latter include in particular target normal sheath acceleration using ultrashort low-energy pulses and structured targets, radiation pressure acceleration in both thick and ultrathin targets and collisionless shock acceleration in moderate density plasmas. For each approach, open issues and the need and potential for further developments are briefly discussed. © 2013 IOP Publishing Ltd.

Laser-driven ion acceleration: State of the art and emerging mechanisms

Ion acceleration driven by high intensity laser pulses is attracting an impressive and steadily increasing research effort. Experiments over the past 10-15 years have demonstrated, over a wide range of laser and target parameters, the generation of multi-MeV proton and ion beams with unique properties, which have stimulated interest in a number of innovative applications. While most of this work has been based on sheath acceleration processes, where space-charge fields are established by relativistic electrons at surfaces of the irradiated target, a number of novel mechanisms has been the focus of recent theoretical and experimental activities. This paper will provide a brief review of the state of the art in the field of laser-driven ion acceleration, with particular attention to recent developments.

Progress in Laser-Driven Ion Acceleration towards Applications in Radiotherapy

We will outline recent progress, in the UK ASAIL laser-ion acceleration programme, which aims to advance laser-driven ion beams to the point at which they will become a serious alternative to conventional accelerators for radiotherapy.

Shortening design time through multiplatform simulations with a portable OpenCL golden-model: the LDPC decoder case

Hardware designers and engineers typically need to explore a multi-parametric design space in order to find the best configuration for their designs using simulations that can take weeks to months to complete. For example, designers of special purpose chips need to explore parameters such as the optimal bitwidth and data representation. This is the case for the development of complex algorithms such as Low-Density Parity-Check (LDPC) decoders used in modern communication systems. Currently, high-performance computing offers a wide set of acceleration options, that range from multicore CPUs to graphics processing units (GPUs) and FPGAs. Depending on the simulation requirements, the ideal architecture to use can vary. In this paper we propose a new design flow based on OpenCL, a unified multiplatform programming model, which accelerates LDPC decoding simulations, thereby significantly reducing architectural exploration and design time. OpenCL-based parallel kernels are used without modifications or code tuning on multicore CPUs, GPUs and FPGAs. We use SOpenCL (Silicon to OpenCL), a tool that automatically converts OpenCL kernels to RTL for mapping the simulations into FPGAs. To the best of our knowledge, this is the first time that a single, unmodified OpenCL code is used to target those three different platforms. We show that, depending on the design parameters to be explored in the simulation, on the dimension and phase of the design, the GPU or the FPGA may suit different purposes more conveniently, providing different acceleration factors. For example, although simulations can typically execute more than 3x faster on FPGAs than on GPUs, the overhead of circuit synthesis often outweighs the benefits of FPGA-accelerated execution.

Autonomic scheduling of tasks from data parallel patterns to CPU/GPU core mixes

We propose a methodology for optimizing the execution of data parallel (sub-)tasks on CPU and GPU cores of the same heterogeneous architecture. The methodology is based on two main components: i) an analytical performance model for scheduling tasks among CPU and GPU cores, such that the global execution time of the overall data parallel pattern is optimized; and ii) an autonomic module which uses the analytical performance model to implement the data parallel computations in a completely autonomic way, requiring no programmer intervention to optimize the computation across CPU and GPU cores. The analytical performance model uses a small set of simple parameters to devise a partitioning-between CPU and GPU cores-of the tasks derived from structured data parallel patterns/algorithmic skeletons. The model takes into account both hardware related and application dependent parameters. It computes the percentage of tasks to be executed on CPU and GPU cores such that both kinds of cores are exploited and performance figures are optimized. The autonomic module, implemented in FastFlow, executes a generic map (reduce) data parallel pattern scheduling part of the tasks to the GPU and part to CPU cores so as to achieve optimal execution time. Experimental results on state-of-the-art CPU/GPU architectures are shown that assess both performance model properties and autonomic module effectiveness. © 2013 IEEE.

Diagnostics for studies of novel laser ion acceleration mechanisms

Diagnostic for investigating and distinguishing different laser ion acceleration mechanisms has been developed and successfully tested. An ion separation wide angle spectrometer can simultaneously investigate three important aspects of the laser plasma interaction: (1) acquire angularly resolved energy spectra for two ion species, (2) obtain ion energy spectra for multiple species, separated according to their charge to mass ratio, along selected axes, and (3) collect laser radiation reflected from and transmitted through the target and propagating in the same direction as the ion beam. Thus, the presented diagnostic constitutes a highly adaptable tool for accurately studying novel acceleration mechanisms in terms of their angular energy distribution, conversion efficiency, and plasma density evolution.