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The proliferation of high-throughput sequencing machines ensures rapid generation of up to billions of short nucleotide fragments in a short period of time. This massive amount of sequence data can quickly overwhelm today's storage and compute infrastructure. This paper explores the use of hardware acceleration to significantly improve the runtime of short-read alignment, a crucial step in preprocessing sequenced genomes. We focus on the Levenshtein distance (edit-distance) computation kernel and propose the ASAP accelerator, which utilizes the intrinsic delay of circuits for edit-distance computation elements as a proxy for computation. Our design is implemented on an Xilinx Virtex 7 FPGA in an IBM POWER8 system that uses the CAPI interface for cache coherence across the CPU and FPGA. Our design is $200\times$ faster than an equivalent Smith-Waterman-C implementation of the kernel running on the host processor, $40-60\times$ faster than an equivalent Landau-Vishkin-C++ implementation of the kernel running on the IBM Power8 host processor, and $2\times$ faster for an end-to-end alignment tool for 120–150 base-pair short-read sequences. Further the design represents a $3760\times$ improvement over the CPU in performance/Watt terms. [ABSTRACT FROM AUTHOR]

Quality of service (QoS) is becoming an attractive feature for high-performance networks and parallel machines because, in those environments, there are different traffic types, each one having its own requirements. In that sense, deadline-based algorithms can provide powerful QoS provision. However, the cost associated with keeping ordered lists of packets makes these algorithms impractical for high-performance networks. In this paper, we explore how to efficiently adapt the Earliest Deadline First family of algorithms to high-speed network environments. The results show excellent performance using just two virtual channels, FIFO queues, and a cost feasible with today's technology. [ABSTRACT FROM AUTHOR]

In an open environment such as the Internet, the decision to collaborate with a stranger (e.g., by granting access to a resource) is often based on the characteristics (rather than the identity) of the requester, via digital credentials: Access is granted if Alice's credentials satisfy Bob's access policy. The literature contains many scenarios in which it is desirable to carry out such trust negotiations in a privacy-preserving manner, i.e., so as minimize the disclosure of credentials and/or of access policies. Elegant solutions were proposed for achieving various degrees of privacy-preservation through minimal disclosure. In this paper, we present protocols that protect both sensitive credentials and sensitive policies. That is, Alice gets the resource only if she satisfies the policy, Bob does not learn anything about Alice's credentials (not even whether Alice got access), and Alice learns neither Bob's policy structure nor which credentials caused her to gain access. Our protocols are efficient in terms of communication and in rounds of interaction. [ABSTRACT FROM AUTHOR]

In the oblivious path selection problem, each packet in the network independently chooses a path, which is an important property if the routing algorithm is to be independent of the traffic distribution. The quality of the paths is determined by the congestion, C, the maximum number of paths crossing an edge, and the dilation, D, the maximum path length. So far, the oblivious algorithms studied in the literature have focused on minimizing the congestion while ignoring the dilation. An open problem is to give algorithms for networks in which C and D can be controlled simultaneously. Here, we solve this problem for the d-dimensional mesh. We present an oblivious algorithm for which C and D are both within O(d²) of the optimal. The algorithm uses randomization and we show that the number of random bits required per packet is within O(d) of the minimum number of random bits required by any algorithm that obtains the same congestion. For a fixed d, our algorithm is asymptotically optimal. [ABSTRACT FROM AUTHOR]