2,348 research outputs found
NaNet: a Low-Latency, Real-Time, Multi-Standard Network Interface Card with GPUDirect Features
While the GPGPU paradigm is widely recognized as an effective approach to
high performance computing, its adoption in low-latency, real-time systems is
still in its early stages.
Although GPUs typically show deterministic behaviour in terms of latency in
executing computational kernels as soon as data is available in their internal
memories, assessment of real-time features of a standard GPGPU system needs
careful characterization of all subsystems along data stream path.
The networking subsystem results in being the most critical one in terms of
absolute value and fluctuations of its response latency.
Our envisioned solution to this issue is NaNet, a FPGA-based PCIe Network
Interface Card (NIC) design featuring a configurable and extensible set of
network channels with direct access through GPUDirect to NVIDIA Fermi/Kepler
GPU memories.
NaNet design currently supports both standard - GbE (1000BASE-T) and 10GbE
(10Base-R) - and custom - 34~Gbps APElink and 2.5~Gbps deterministic latency
KM3link - channels, but its modularity allows for a straightforward inclusion
of other link technologies.
To avoid host OS intervention on data stream and remove a possible source of
jitter, the design includes a network/transport layer offload module with
cycle-accurate, upper-bound latency, supporting UDP, KM3link Time Division
Multiplexing and APElink protocols.
After NaNet architecture description and its latency/bandwidth
characterization for all supported links, two real world use cases will be
presented: the GPU-based low level trigger for the RICH detector in the NA62
experiment at CERN and the on-/off-shore data link for KM3 underwater neutrino
telescope
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
Desenvolvimento do coqueiro-anão verde em função de doses de N e K em fertirrigação: avaliação inicial.
bitstream/CNPAT-2010/7109/1/Pa-081.pd
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
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