Constructing cluster of simple FPGA boards for cryptologic computations

In this paper, we propose an FPGA cluster infrastructure, which can be utilized in implementing cryptanalytic attacks and accelerating cryptographic operations. The cluster can be formed using simple and inexpensive, off-the-shelf FPGA boards featuring an FPGA device, local storage, CPLD, and network connection. Forming the cluster is simple and no effort for the hardware development is needed except for the hardware design for the actual computation. Using a softcore processor on FPGA, we are able to configure FPGA devices dynamically and change their configuration on the fly from a remote computer. The softcore on FPGA can execute relatively complicated programs for mundane tasks unworthy of FPGA resources. Finally, we propose and implement a fast and efficient dynamic configuration switch technique that is shown to be useful especially in cryptanalytic applications. Our infrastructure provides a cost-effective alternative for formerly proposed cryptanalytic engines based on FPGA devices

APEnet+: a 3D toroidal network enabling Petaflops scale Lattice QCD simulations on commodity clusters

Many scientific computations need multi-node parallelism for matching up both space (memory) and time (speed) ever-increasing requirements. The use of GPUs as accelerators introduces yet another level of complexity for the programmer and may potentially result in large overheads due to the complex memory hierarchy. Additionally, top-notch problems may easily employ more than a Petaflops of sustained computing power, requiring thousands of GPUs orchestrated with some parallel programming model. Here we describe APEnet+, the new generation of our interconnect, which scales up to tens of thousands of nodes with linear cost, thus improving the price/performance ratio on large clusters. The project target is the development of the Apelink+ host adapter featuring a low latency, high bandwidth direct network, state-of-the-art wire speeds on the links and a PCIe X8 gen2 host interface. It features hardware support for the RDMA programming model and experimental acceleration of GPU networking. A Linux kernel driver, a set of low-level RDMA APIs and an OpenMPI library driver are available, allowing for painless porting of standard applications. Finally, we give an insight of future work and intended developments

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

Ultra-high throughput string matching for deep packet inspection

Deep Packet Inspection (DPI) involves searching a packet's header and payload against thousands of rules to detect possible attacks. The increase in Internet usage and growing number of attacks which must be searched for has meant hardware acceleration has become essential in the prevention of DPI becoming a bottleneck to a network if used on an edge or core router. In this paper we present a new multi-pattern matching algorithm which can search for the fixed strings contained within these rules at a guaranteed rate of one character per cycle independent of the number of strings or their length. Our algorithm is based on the Aho-Corasick string matching algorithm with our modifications resulting in a memory reduction of over 98% on the strings tested from the Snort ruleset. This allows the search structures needed for matching thousands of strings to be small enough to fit in the on-chip memory of an FPGA. Combined with a simple architecture for hardware, this leads to high throughput and low power consumption. Our hardware implementation uses multiple string matching engines working in parallel to search through packets. It can achieve a throughput of over 40 Gbps (OC-768) when implemented on a Stratix 3 FPGA and over 10 Gbps (OC-192) when implemented on the lower power Cyclone 3 FPGA