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While G6PD deficiency is one of the major causes of acute hemolytic anemia, the membrane changes leading to red cell lysis have not been extensively studied. New findings concerning the mechanisms of G6PD deficient red cell destruction may facilitate our understanding of the large individual variations in susceptibility to pro-oxidant compounds and aid the prediction of the hemolytic activity of new drugs. Our results show that treatment of G6PD deficient red cells with diamide (0.25 mM) or divicine (0.5 mM) causes: (1) an increase in the oxidation and tyrosine phosphorylation of AE1; (2) progressive recruitment of phosphorylated AE1 in large membrane complexes which also contain hemichromes; (3) parallel red cell lysis and a massive release of vesicles containing hemichromes. We have observed that inhibition of AE1 phosphorylation by Syk kinase inhibitors prevented its clustering and the membrane vesiculation while increases in AE1 phosphorylation by tyrosine phosphatase inhibitors increased both red cell lysis and vesiculation rates. In control RBCs we observed only transient AE1 phosphorylation. Collectively, our findings indicate that persistent tyrosine phosphorylation produces extensive membrane destabilization leading to the loss of vesicles which contain hemichromes. The proposed mechanism of hemolysis may be applied to other hemolytic diseases characterized by the accumulation of hemoglobin denaturation products.

The use of GPUs to implement general purpose computational tasks, known as GPGPU since fifteen years ago, has reached maturity. Applications take advantage of the parallel architectures of these devices in many different domains. Over the last few years several works have demonstrated the effectiveness of the integration of GPU-based systems in the high level trigger of various HEP experiments. On the other hand, the use of GPUs in the DAQ and low level trigger systems, characterized by stringent real-time constraints, poses several challenges. In order to achieve such a goal we devised NaNet, a FPGA-based PCI-Express Network Interface Card design capable of direct (zero-copy) data transferring with CPU and GPU (GPUDirect) while online processing incoming and outgoing data streams. The board provides as well support for multiple link technologies (1/10/40GbE and custom ones). The validity of our approach has been tested in the context of the NA62 CERN experiment, harvesting the computing power of last generation NVIDIA Pascal GPUs and of the FPGA hosted by NaNet to build in real-time refined physics-related primitives for the RICH detector (i.e. the Cerenkov rings parameters) that enable the building of more stringent conditions for data selection in the low level trigger.

In the attempt to develop an interconnection architecture optimized for hybrid HPC systems dedicated to scientific computing, we designed APEnet+, a point-to-point, low-latency and high-performance network controller supporting 6 fully bidirectional off-board links over a 3D torus topology. The first release of APEnet+ (named V4) was a board based on a 40 nm Altera FPGA, integrating 6 channels at 34 Gbps of raw bandwidth per direction and a PCIe Gen2 x8 host interface. It has been the first-of-its-kind device to implement an RDMA protocol to directly read write data from to Fermi and Kepler NVIDIA GPUs using NVIDIA peer-to-peer and GPUDirect RDMA protocols, obtaining real zero-copy GPU-to-GPU transfers over the network. The latest generation of APEnet+ systems (now named V5) implements a PCIe Gen3 x8 host interface on a 28 nm Altera Stratix V FPGA, with multi-standard fast transceivers (up to 14.4 Gbps) and an increased amount of configurable internal resources and hardware IP cores to support main interconnection standard protocols. Herein we present the APEnet+ V5 architecture, the status of its hardware and its system software design. Both its Linux Device Driver and the low-level libraries have been redeveloped to support the PCIe Gen3 protocol, introducing optimizations and solutions based on hardware software co-design.

With processor architecture evolution, the HPC market has undergone a paradigm shift. The adoption of low-cost, Linux-based clusters extended the reach of HPC from its roots in modelling and simulation of complex physical systems to a broader range of industries, from biotechnology, cloud computing, computer analytics and big data challenges to manufacturing sectors. In this perspective, the near future HPC systems can be envisioned as composed of millions of low-power computing cores, densely packed - meaning cooling by appropriate technology - with a tightly interconnected, low latency and high performance network and equipped with a distributed storage architecture. Each of these features - dense packing, distributed storage and high performance interconnect - represents a challenge, made all the harder by the need to solve them at the same time. These challenges lie as stumbling blocks along the road towards Exascale-class systems; the ExaNeSt project acknowledges them and tasks itself with investigating ways around them.

In the supercomputing arena, the strong rise of GPU-accelerated clusters is a matter of fact. Within INFN, we proposed an initiative — the QUonG project — whose aim is to deploy a high performance computing system dedicated to scientific computations leveraging on commodity multi-core processors coupled with latest generation GPUs. The inter-node interconnection system is based on a point-to-point, high performance, low latency 3D torus network which is built in the framework of the APEnet+ project. It takes the form of an FPGA-based PCIe network card exposing six full bidirectional links running at 34 Gbps each that implements the RDMA protocol. In order to enable significant access latency reduction for inter-node data transfer, a direct network-to-GPU interface was built. The specialized hardware blocks, integrated in the APEnet+ board, provide support for GPU-initiated communications using the so called PCIe peer-to-peer (P2P) transactions. This development is made in close collaboration with the GPU vendor NVIDIA. The final shape of a complete QUonG deployment is an assembly of standard 42U racks, each one capable of 80 TFLOPS/rack of peak performance, at a cost of 5 k€/T F LOPS and for an estimated power consumption of 25 kW/rack. In this paper we report on the status of final rack deployment and on the R&D activities for 2012 that will focus on performance enhancement of the APEnet+ hardware through the adoption of new generation 28 nm FPGAs allowing the implementation of PCIe Gen3 host interface and the addition of new fault tolerance-oriented capabilities.

A commercial Graphics Processing Unit (GPU) is used to build a fast Level 0 (L0) trigger system tested parasitically with the TDAQ (Trigger and Data Acquisition systems) of the NA62 experiment at CERN. In particular, the parallel computing power of the GPU is exploited to perform real-time fitting in the Ring Imaging CHerenkov (RICH) detector. Direct GPU communication using a FPGA-based board has been used to reduce the data transmission latency. The performance of the system for multi-ring reconstrunction obtained during the NA62 physics run will be presented.

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.

The KM3NeT-Italia underwater neutrino detection unit, the tower, consists of 14 floors. Each floor supports 6 Optical Modules containing front-end electronics needed to digitize the PMT signal, format and transmit the data and 2 hydrophones that reconstruct in real-time the position of Optical Modules, for a maximum tower throughput of more than 600 MB/s. All floor data are collected by the Floor Control Module (FCM) board and transmitted by optical bidirectional virtual point-to-point connections to the on-shore laboratory, each FCM needing an on-shore counterpart as communication endpoint. In this contribution we present NaNet3, an on-shore readout board based on Altera Stratix V GX FPGA able to manage multiple FCM data channels with a capability of 800 Mbps each. The design is a NaNet customization for the KM3NeT-Italia experiment, adding support in its I/O interface for a synchronous link protocol with deterministic latency at physical level and for a Time Division Multiplexing protocol at data level.

We describe a pilot project (GAP – GPU Application Project) for the use of GPUs (Graphics processing units) for online triggering applications in High Energy Physics experiments. Two major trends can be identified in the development of trigger and DAQ systems for particle physics experiments: the massive use of general-purpose commodity systems such as commercial multicore PC farms for data acquisition, and the reduction of trigger levels implemented in hardware, towards a fully software data selection system (\"trigger-less\"). The innovative approach presented here aims at exploiting the parallel computing power of commercial GPUs to perform fast computations in software not only in high level trigger levels but also in early trigger stages. General-purpose computing on GPUs is emerging as a new paradigm in several fields of science, although so far applications have been tailored to the specific strengths of such devices as accelerators in offline computation. With the steady reduction of GPU latencies, and the increase in link and memory throughputs, the use of such devices for real-time applications in high energy physics data acquisition and trigger systems is becoming relevant. We discuss in detail the use of online parallel computing on GPUs for synchronous low-level triggers with fixed latency. In particular we show preliminary results on a first test in the CERN NA62 experiment. The use of GPUs in high level triggers is also considered, the CERN ATLAS experiment being taken as a case study of possible applications.

APEnet+ is an INFN (Italian Institute for Nuclear Physics) project aiming to develop a custom 3-Dimensional torus interconnect network optimized for hybrid clusters CPU-GPU dedicated to High Performance scientific Computing. The APEnet+ interconnect fabric is built on a FPGA-based PCI-express board with 6 bi-directional off-board links showing 34 Gbps of raw bandwidth per direction, and leverages upon peer-to-peer capabilities of Fermi and Kepler-class NVIDIA GPUs to obtain real zero-copy, GPU-to-GPU low latency transfers. The minimization of APEnet+ transfer latency is achieved through the adoption of RDMA protocol implemented in FPGA with specialized hardware blocks tightly coupled with embedded microprocessor. This architecture provides a high performance low latency offload engine for both trasmit and receive side of data transactions: preliminary results are encouraging, showing 50% of bandwidth increase for large packet size transfers. In this paper we describe the APEnet+ architecture, detailing the hardware implementation and discuss the impact of such RDMA specialized hardware on host interface latency and bandwidth.