High-speed data transfer with FPGAs and QSFP+ modules

We present test results and characterization of a data transmission system based on a last generation FPGA and a commercial QSFP+ (Quad Small Form Pluggable +) module. QSFP+ standard defines a hot-pluggable transceiver available in copper or optical cable assemblies for an aggregated bandwidth of up to 40 Gbps. We implemented a complete testbench based on a commercial development card mounting an Altera Stratix IV FPGA with 24 serial transceivers at 8.5 Gbps, together with a custom mezzanine hosting three QSFP+ modules. We present test results and signal integrity measurements up to an aggregated bandwidth of 12 Gbps.Comment: 5 pages, 3 figures, Published on JINST Journal of Instrumentation proceedings of Topical Workshop on Electronics for Particle Physics 2010, 20-24 September 2010, Aachen, Germany(R Ammendola et al 2010 JINST 5 C12019

Auditory stimulation and deep learning predict awakening from coma after cardiac arrest.

Assessing the integrity of neural functions in coma after cardiac arrest remains an open challenge. Prognostication of coma outcome relies mainly on visual expert scoring of physiological signals, which is prone to subjectivity and leaves a considerable number of patients in a 'grey zone', with uncertain prognosis. Quantitative analysis of EEG responses to auditory stimuli can provide a window into neural functions in coma and information about patients' chances of awakening. However, responses to standardized auditory stimulation are far from being used in a clinical routine due to heterogeneous and cumbersome protocols. Here, we hypothesize that convolutional neural networks can assist in extracting interpretable patterns of EEG responses to auditory stimuli during the first day of coma that are predictive of patients' chances of awakening and survival at 3 months. We used convolutional neural networks (CNNs) to model single-trial EEG responses to auditory stimuli in the first day of coma, under standardized sedation and targeted temperature management, in a multicentre and multiprotocol patient cohort and predict outcome at 3 months. The use of CNNs resulted in a positive predictive power for predicting awakening of 0.83 ± 0.04 and 0.81 ± 0.06 and an area under the curve in predicting outcome of 0.69 ± 0.05 and 0.70 ± 0.05, for patients undergoing therapeutic hypothermia and normothermia, respectively. These results also persisted in a subset of patients that were in a clinical 'grey zone'. The network's confidence in predicting outcome was based on interpretable features: it strongly correlated to the neural synchrony and complexity of EEG responses and was modulated by independent clinical evaluations, such as the EEG reactivity, background burst-suppression or motor responses. Our results highlight the strong potential of interpretable deep learning algorithms in combination with auditory stimulation to improve prognostication of coma outcome

GPU-based Real-time Triggering in the NA62 Experiment

Over the last few years the GPGPU (General-Purpose computing on Graphics Processing Units) paradigm represented a remarkable development in the world of computing. Computing for High-Energy Physics is no exception: several works have demonstrated the effectiveness of the integration of GPU-based systems in high level trigger of different experiments. On the other hand the use of GPUs in the low level trigger systems, characterized by stringent real-time constraints, such as tight time budget and high throughput, poses several challenges. In this paper we focus on the low level trigger in the CERN NA62 experiment, investigating the use of real-time computing on GPUs in this synchronous system. Our approach aimed at harvesting the GPU computing power to build in real-time refined physics-related trigger primitives for the RICH detector, as the the knowledge of Cerenkov rings parameters allows to build stringent conditions for data selection at trigger level. Latencies of all components of the trigger chain have been analyzed, pointing out that networking is the most critical one. To keep the latency of data transfer task under control, we devised NaNet, an FPGA-based PCIe Network Interface Card (NIC) with GPUDirect capabilities. For the processing task, we developed specific multiple ring trigger algorithms to leverage the parallel architecture of GPUs and increase the processing throughput to keep up with the high event rate. Results obtained during the first months of 2016 NA62 run are presented and discussed

APEnet+: high bandwidth 3D torus direct network for petaflops scale commodity clusters

We describe herein the APElink+ board, a PCIe interconnect adapter featuring the latest advances in wire speed and interface technology plus hardware support for a RDMA programming model and experimental acceleration of GPU networking; this design allows us to build a low latency, high bandwidth PC cluster, the APEnet+ network, the new generation of our cost-effective, tens-of-thousands-scalable cluster network architecture. Some test results and characterization of data transmission of a complete testbench, based on a commercial development card mounting an Altera FPGA, are provided.Comment: 6 pages, 7 figures, proceeding of CHEP 2010, Taiwan, October 18-2

apeNEXT: A multi-TFlops Computer for Simulations in Lattice Gauge Theory

We present the APE (Array Processor Experiment) project for the development of dedicated parallel computers for numerical simulations in lattice gauge theories. While APEmille is a production machine in today's physics simulations at various sites in Europe, a new machine, apeNEXT, is currently being developed to provide multi-Tflops computing performance. Like previous APE machines, the new supercomputer is largely custom designed and specifically optimized for simulations of Lattice QCD.Comment: Poster at the XXIII Physics in Collisions Conference (PIC03), Zeuthen, Germany, June 2003, 3 pages, Latex. PSN FRAP15. Replaced for adding forgotten autho

Synthesis of CdS and CdSe nanocrystallites using a novel single-molecule precursors approach

The synthesis of CdS and CdSe nanocrystallites using the thermolysis of several dithioor diselenocarbamato complexes of cadmium in trioctylphosphine oxide (TOPO) is reported. The nanodispersed materials obtained show quantum size effects in their optical spectra and exhibit near band-edge luminescence. The influence of experimental parameters on the properties of the nanocrystallites is discussed. HRTEM images of these materials show well-defined, crystalline nanosized particles. Standard size fractionation procedures can be performed in order to narrow the size dispersion of the samples. The TOPO-capped CdS and CdSe nanocrystallites and simple organic bridging ligands, such as 2,2¢-bipyrimidine, are used as the starting materials for the preparation of novel nanocomposites. The optical properties shown by these new nanocomposites are compared with those of the starting nanodispersed materials

Uneven integration for perception and action cues in children’s working memory

We examined the development of visual cue integration in a desktop working-memory task using boxes with different visual action cues (opening actions) and perceptual surface cues (colours, monochromatic textures, or images of faces). Children had to recall which box held a hidden toy, based on (a) the action cue, (b) the surface cue, or (c) a conjunction of the two. Results from three experiments show a set of asymmetries in children's integration of action and surface cues. The 18–24-month-olds disregarded colour in conjunction judgements with action; 30–36-month-olds used colour but disregarded texture. Images of faces were not disregarded at either age. We suggest that 18–24-month-olds' disregard of colour, seen previously in reorientation tasks (Hermer & Spelke, 1994), may represent a general phenomenon, likened to uneven integration between the dorsal and ventral streams in early development