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This project aims to exploit the parallel computing power of a commercial Graphics Processing Unit (GPU) to implement fast pattern matching in the Ring Imaging Cherenkov (RICH) detector for the level 0 (L0) trigger of the NA62 experiment. In this approach, the ring-fitting algorithm is seedless, being fed with raw RICH data, with no previous information on the ring position from other detectors. Moreover, since the L0 trigger is provided with a more elaborated information than a simple multiplicity number, it results in a higher selection power. Two methods have been studied in order to reduce the data transfer latency from the readout boards of the detector to the GPU, i.e., the use of a dedicated NIC device driver with very low latency and a direct data transfer protocol from a custom FPGA-based NIC to the GPU. The performance of the system, developed through the FPGA approach, for multi-ring Cherenkov online reconstruction obtained during the NA62 physics runs is presented.

NaNet is a FPGA-based PCIe Network Interface Card (NIC) design with GPUDirect and Remote Direct Memory Access (RDMA) capabilities featuring a configurable and extensible set of network channels. The 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 straightforward inclusion of other link technologies. The GPUDirect feature combined with a transport layer offload module and a data stream processing stage makes NaNet a low-latency NIC suitable for real-time GPU processing. In this paper we describe the NaNet architecture and its performances, exhibiting two of its use cases: the GPU-based low-level trigger for the RICH detector in the NA62 experiment at CERN and the on-/off-shore data transport system for the KM3NeT-IT underwater neutrino telescope.

APEnet+ is a point-to-point, low-latency, 3D-torus network controller integrated in a PCIe Gen2 board based on the Altera Stratix IV FPGA. We characterize the transmission system (embedded transceivers driving external QSFP+ modules) analyzing signal integrity, throughput, latency, BER and jitter at different data rates up to 34 Gbps. We estimate the efficiency of a custom logic able to sustain 2.6 GB/s per link with an FPGA on-chip memory footprint of 40 KB, providing deadlock-free routing and systemic awareness of faults. Finally, we show the preliminary results obtained with the embedded transceivers of a next-generation FPGA and outline some ideas to increase the performance with the same FPGA memory footprint.

We present test results and characterization of a data compression system for the readout of the NA62 liquid krypton calorimeter trigger processor. The Level-0 electromagnetic calorimeter trigger processor of the NA62 experiment at CERN receives digitized data from the calorimeter main readout board. These data are stored on an on-board DDR2 RAM memory and read out upon reception of a Level-0 accept signal. The maximum raw data throughput from the trigger front-end cards is 2.6 Gbps. To readout these data over two Gbit Ethernet interfaces we investigated different implementations of a data compression system based on the Rice-Golomb coding: one is implemented in the FPGA as a custom block and one is implemented on the FPGA embedded processor running a C code. The two implementations are tested on a set of sample events and compared with respect to achievable readout bandwidth.