The Brain on Low Power Architectures - Efficient Simulation of Cortical Slow Waves and Asynchronous States

Efficient brain simulation is a scientific grand challenge, a parallel/distributed coding challenge and a source of requirements and suggestions for future computing architectures. Indeed, the human brain includes about 10^15 synapses and 10^11 neurons activated at a mean rate of several Hz. Full brain simulation poses Exascale challenges even if simulated at the highest abstraction level. The WaveScalES experiment in the Human Brain Project (HBP) has the goal of matching experimental measures and simulations of slow waves during deep-sleep and anesthesia and the transition to other brain states. The focus is the development of dedicated large-scale parallel/distributed simulation technologies. The ExaNeSt project designs an ARM-based, low-power HPC architecture scalable to million of cores, developing a dedicated scalable interconnect system, and SWA/AW simulations are included among the driving benchmarks. At the joint between both projects is the INFN proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation engine. DPSNN can be configured to stress either the networking or the computation features available on the execution platforms. The simulation stresses the networking component when the neural net - composed by a relatively low number of neurons, each one projecting thousands of synapses - is distributed over a large number of hardware cores. When growing the number of neurons per core, the computation starts to be the dominating component for short range connections. This paper reports about preliminary performance results obtained on an ARM-based HPC prototype developed in the framework of the ExaNeSt project. Furthermore, a comparison is given of instantaneous power, total energy consumption, execution time and energetic cost per synaptic event of SWA/AW DPSNN simulations when executed on either ARM- or Intel-based server platforms

NaNet:a low-latency NIC enabling GPU-based, real-time low level trigger systems

We implemented the NaNet FPGA-based PCI2 Gen2 GbE/APElink NIC, featuring GPUDirect RDMA capabilities and UDP protocol management offloading. NaNet is able to receive a UDP input data stream from its GbE interface and redirect it, without any intermediate buffering or CPU intervention, to the memory of a Fermi/Kepler GPU hosted on the same PCIe bus, provided that the two devices share the same upstream root complex. Synthetic benchmarks for latency and bandwidth are presented. We describe how NaNet can be employed in the prototype of the GPU-based RICH low-level trigger processor of the NA62 CERN experiment, to implement the data link between the TEL62 readout boards and the low level trigger processor. Results for the throughput and latency of the integrated system are presented and discussed.Comment: Proceedings for the 20th International Conference on Computing in High Energy and Nuclear Physics (CHEP

Experimental validation of specificity of the squamous cell carcinoma antigen-immunoglobulin M (SCCA-IgM) assay in patients with cirrhosis

Background: Squamous cell carcinoma antigen-immunoglobulin M (SCCA-IgM) is a useful biomarker for the risk of development of hepatocellular carcinoma (HCC) in patients with cirrhosis due to its progressive increase associated to HCC evolution. In patients with cirrhosis, other assays have been affected by interfering reactivities of IgM. In this study, the analytical specificity of the SCCA-IgM assay was assessed by evaluating SCCA-IgM measurement dependence on different capture phases, and by measuring the recovery of SCCA-IgM reactivity following serum fractionation. Methods: Serum samples from 82 patients with cirrhosis were analyzed. SCCA-IgM was measured using the reference test (Hepa-IC, Xeptagen, Italy) that is based on rabbit oligoclonal anti-squamous cell carcinoma antigen (SCCA) and a dedicated ELISA with a mouse monoclonal anti-SCCA as the capture antibody. Results: SCCA-IgM concentrations measured with the reference assay (median value=87 AU/mL) were higher than those measured with the mouse monoclonal test (median value=78 AU/mL). However, the differences in the SCCA-IgM distribution were not statistically significant (p>0.05). When SCCA-IgM concentrations measured with both tests were compared, a linear correlation was found (r=0.77, p<0.05). Fractionation of the most reactive sera by gel-filtration chromatography showed that total recovery of SCCA-IgM reactivity was seen only in the fractions corresponding to components with a molecular weight higher than IgM and SCCA (>2000 kDa) with both tests. Conclusions: The equivalence of both SCCA-IgM assays and the absence of reactivity not related to immune complexes support the analytical specificity of SCCA-IgM measurements. The results validate the assessment of SCCA-IgM for prognostic purposes in patients with cirrhosis. Clin Chem Lab Med 2010;48:217–23.Peer Reviewe

Gaussian and exponential lateral connectivity on distributed spiking neural network simulation

We measured the impact of long-range exponentially decaying intra-areal lateral connectivity on the scaling and memory occupation of a distributed spiking neural network simulator compared to that of short-range Gaussian decays. While previous studies adopted short-range connectivity, recent experimental neurosciences studies are pointing out the role of longer-range intra-areal connectivity with implications on neural simulation platforms. Two-dimensional grids of cortical columns composed by up to 11 M point-like spiking neurons with spike frequency adaption were connected by up to 30 G synapses using short- and long-range connectivity models. The MPI processes composing the distributed simulator were run on up to 1024 hardware cores, hosted on a 64 nodes server platform. The hardware platform was a cluster of IBM NX360 M5 16-core compute nodes, each one containing two Intel Xeon Haswell 8-core E5-2630 v3 processors, with a clock of 2.40 G Hz, interconnected through an InfiniBand network, equipped with 4x QDR switches.Comment: 9 pages, 9 figures, added reference to final peer reviewed version on conference paper and DO

Real-time cortical simulations: energy and interconnect scaling on distributed systems

We profile the impact of computation and inter-processor communication on the energy consumption and on the scaling of cortical simulations approaching the real-time regime on distributed computing platforms. Also, the speed and energy consumption of processor architectures typical of standard HPC and embedded platforms are compared. We demonstrate the importance of the design of low-latency interconnect for speed and energy consumption. The cost of cortical simulations is quantified using the Joule per synaptic event metric on both architectures. Reaching efficient real-time on large scale cortical simulations is of increasing relevance for both future bio-inspired artificial intelligence applications and for understanding the cognitive functions of the brain, a scientific quest that will require to embed large scale simulations into highly complex virtual or real worlds. This work stands at the crossroads between the WaveScalES experiment in the Human Brain Project (HBP), which includes the objective of large scale thalamo-cortical simulations of brain states and their transitions, and the ExaNeSt and EuroExa projects, that investigate the design of an ARM-based, low-power High Performance Computing (HPC) architecture with a dedicated interconnect scalable to million of cores; simulation of deep sleep Slow Wave Activity (SWA) and Asynchronous aWake (AW) regimes expressed by thalamo-cortical models are among their benchmarks.Comment: 8 pages, 8 figures, 4 tables, submitted after final publication on PDP2019 proceedings, corrected final DOI. arXiv admin note: text overlap with arXiv:1812.04974, arXiv:1804.0344

A multi-port 10GbE PCIe NIC featuring UDP offload and GPUDirect capabilities

NaNet-10 is a four-ports 10GbE PCIe Network Interface Card designed for low-latency real-time operations with GPU systems. To this purpose the design includes an UDP offload module, for fast and clock-cycle deterministic handling of the transport layer protocol, plus a GPUDirect P2P/RDMA engine for low-latency communication with NVIDIA Tesla GPU devices. A dedicated module (Multi-Stream) can optionally process input UDP streams before data is delivered through PCIe DMA to their destination devices, re-organizing data from different streams guaranteeing computational optimization. NaNet-10 is going to be integrated in the NA62 CERN experiment in order to assess the suitability of GPGPU systems as real-time triggers; results and lessons learned while performing this activity will be reported herein

Search for heavy neutral lepton production in K+ decays

A search for heavy neutral lepton production in K + decays using a data sample collected with a minimum bias trigger by the NA62 experiment at CERN in 2015 is reported. Upper limits at the 10−7 to 10−6 level are established on the elements of the extended neutrino mixing matrix |Ue4| 2 and |Uμ4| 2 for heavy neutral lepton mass in the ranges 170–448 MeV/c2 and 250–373 MeV/c2, respectively. This improves on the previous limits from HNL production searches over the whole mass range considered for |Ue4|2 and above 300 MeV/c2 for |Uμ4|2

Measurement of the very rare K+→π+ννˉK^+ \to \pi^+ \nu \bar\nu decay

The decay K+→π+νν¯ , with a very precisely predicted branching ratio of less than 10−10 , is among the best processes to reveal indirect effects of new physics. The NA62 experiment at CERN SPS is designed to study the K+→π+νν¯ decay and to measure its branching ratio using a decay-in-flight technique. NA62 took data in 2016, 2017 and 2018, reaching the sensitivity of the Standard Model for the K+→π+νν¯ decay by the analysis of the 2016 and 2017 data, and providing the most precise measurement of the branching ratio to date by the analysis of the 2018 data. This measurement is also used to set limits on BR(K+→π+X ), where X is a scalar or pseudo-scalar particle. The final result of the BR(K+→π+νν¯ ) measurement and its interpretation in terms of the K+→π+X decay from the analysis of the full 2016-2018 data set is presented, and future plans and prospects are reviewed

Serpins, Immunity and Autoimmunity: Old Molecules, New Functions.

Serine protease inhibitors (serpins) are evolutionary old, structurally conserved molecules which encompass nearly all branches of life. More than 1,000 serpins were characterized to date which are subdivided into 16 subgroups (A-P) according to their common ancestry; among them, 37 are found in humans. Serpins were termed after their capability to inhibit serine proteases, but mounting evidence suggests that they may achieve a greater deal of functions, ranging from embryological growth to synaptic plasticity, development of both myeloid and lymphoid immune cells, and modulation of apoptosis. Serpins are mainly extracellular molecules, although some of them (namely, ov-serpins or clade B serpins) mostly act inside the cells, being either ubiquitously or tissue-specifically expressed. Among newly characterized serpin functions, regulation of cellular proliferation through apoptosis modulation and proteasome disturbance seems to play a major role. Accordingly, several serpins were found to be hyperexpressed in tumor cells. Indeed, apoptosis dysregulation is likely to be a cornerstone in both tumorigenesis and autoimmunity, since uncontrolled cellular viability results in tumor proliferation, while inefficient disposal of apoptotic debris may favor the rescue of autoreactive immune cells. Such a process was widely documented in systemic lupus erythematosus (SLE). Interestingly, alterations in the expression of some serpins, e.g., the ov-serpin SERPINB3, are being unraveled in patients affected with SLE and other autoimmune disorders, suggesting that a failure in serpin function might affect immune homeostasis and self-tolerance, thereby contributing to autoimmunity. Here, we provide an overview of serpin origin, function, and dysfunction, focusing on human serpins and ov-serpins, with a hub on SERPINB3

1-Piperidine Propionic Acid as an Allosteric Inhibitor of Protease Activated Receptor-2

In the last decades, studies on the inflammatory signaling pathways in multiple pathological contexts have revealed new targets for novel therapies. Among the family of G-protein-coupled Proteases Activated Receptors, PAR2 was identified as a driver of the inflammatory cascade in many pathologies, ranging from autoimmune disease to cancer metastasis. For this reason, many efforts have been focused on the development of potential antagonists of PAR2 activity. This work focuses on a small molecule, 1-Piperidine Propionic Acid (1-PPA), previously described to be active against inflammatory processes, but whose target is still unknown. Stabilization effects observed by cellular thermal shift assay coupled to in-silico investigations, including molecular docking and molecular dynamics simulations, suggested that 1-PPA binds PAR2 in an allosteric pocket of the receptor inactive conformation. Functional studies revealed the antagonist effects on MAPKs signaling and on platelet aggregation, processes mediated by PAR family members, including PAR2. Since the allosteric pocket binding 1-PPA is highly conserved in all the members of the PAR family, the evidence reported here suggests that 1-PPA could represent a promising new small molecule targeting PARs with antagonistic activity